rebase IPython.parallel after removal of IPython.kernel

This commit removes all '*z' suffixes from scripts and docs,
as there is no longer conflict with IPython.kernel.

IPython/config/default/ipcluster_config.py

@@ -1,184 +1,241 b''
 import os
 
 c = get_config()
 
 #-----------------------------------------------------------------------------
 # Select which launchers to use
 #-----------------------------------------------------------------------------
 
 # This allows you to control what method is used to start the controller
 # and engines.  The following methods are currently supported:
 # - Start as a regular process on localhost.
 # - Start using mpiexec.
 # - Start using the Windows HPC Server 2008 scheduler
-# - Start using PBS
-# - Start using SSH (currently broken)
+# - Start using PBS/SGE
+# - Start using SSH
 
 
 # The selected launchers can be configured below.
 
 # Options are:
 # - LocalControllerLauncher
 # - MPIExecControllerLauncher
 # - PBSControllerLauncher
+# - SGEControllerLauncher
 # - WindowsHPCControllerLauncher
-# c.Global.controller_launcher = 'IPython.kernel.launcher.LocalControllerLauncher'
+# c.Global.controller_launcher = 'IPython.parallel.launcher.LocalControllerLauncher'
+# c.Global.controller_launcher = 'IPython.parallel.launcher.PBSControllerLauncher'
 
 # Options are:
 # - LocalEngineSetLauncher
 # - MPIExecEngineSetLauncher
 # - PBSEngineSetLauncher
+# - SGEEngineSetLauncher
 # - WindowsHPCEngineSetLauncher
-# c.Global.engine_launcher = 'IPython.kernel.launcher.LocalEngineSetLauncher'
+# c.Global.engine_launcher = 'IPython.parallel.launcher.LocalEngineSetLauncher'
 
 #-----------------------------------------------------------------------------
 # Global configuration
 #-----------------------------------------------------------------------------
 
 # The default number of engines that will be started. This is overridden by
 # the -n command line option: "ipcluster start -n 4"
 # c.Global.n = 2
 
 # Log to a file in cluster_dir/log, otherwise just log to sys.stdout.
 # c.Global.log_to_file = False
 
 # Remove old logs from cluster_dir/log before starting.
 # c.Global.clean_logs = True
 
 # The working directory for the process. The application will use os.chdir
 # to change to this directory before starting.
 # c.Global.work_dir = os.getcwd()
 
 
 #-----------------------------------------------------------------------------
 # Local process launchers
 #-----------------------------------------------------------------------------
 
 # The command line arguments to call the controller with.
 # c.LocalControllerLauncher.controller_args = \
 #    ['--log-to-file','--log-level', '40']
 
 # The working directory for the controller
 # c.LocalEngineSetLauncher.work_dir = u''
 
 # Command line argument passed to the engines.
 # c.LocalEngineSetLauncher.engine_args = ['--log-to-file','--log-level', '40']
 
 #-----------------------------------------------------------------------------
 # MPIExec launchers
 #-----------------------------------------------------------------------------
 
-# The mpiexec/mpirun command to use in started the controller.
-# c.MPIExecControllerLauncher.mpi_cmd = ['mpiexec']
+# The mpiexec/mpirun command to use in both the controller and engines.
+# c.MPIExecLauncher.mpi_cmd = ['mpiexec']
 
 # Additional arguments to pass to the actual mpiexec command.
+# c.MPIExecLauncher.mpi_args = []
+
+# The mpiexec/mpirun command and args can be overridden if they should be different
+# for controller and engines.
+# c.MPIExecControllerLauncher.mpi_cmd = ['mpiexec']
 # c.MPIExecControllerLauncher.mpi_args = []
+# c.MPIExecEngineSetLauncher.mpi_cmd = ['mpiexec']
+# c.MPIExecEngineSetLauncher.mpi_args = []
 
 # The command line argument to call the controller with.
 # c.MPIExecControllerLauncher.controller_args = \
 #     ['--log-to-file','--log-level', '40']
 
-
-# The mpiexec/mpirun command to use in started the controller.
-# c.MPIExecEngineSetLauncher.mpi_cmd = ['mpiexec']
-
-# Additional arguments to pass to the actual mpiexec command.
-# c.MPIExecEngineSetLauncher.mpi_args = []
-
 # Command line argument passed to the engines.
 # c.MPIExecEngineSetLauncher.engine_args = ['--log-to-file','--log-level', '40']
 
 # The default number of engines to start if not given elsewhere.
 # c.MPIExecEngineSetLauncher.n = 1
 
 #-----------------------------------------------------------------------------
 # SSH launchers
 #-----------------------------------------------------------------------------
 
-# Todo
+# ipclusterz can be used to launch controller and engines remotely via ssh.
+# Note that currently ipclusterz does not do any file distribution, so if
+# machines are not on a shared filesystem, config and json files must be
+# distributed.  For this reason, the reuse_files defaults to True on an
+# ssh-launched Controller.  This flag can be overridded by the program_args
+# attribute of c.SSHControllerLauncher.
+
+# set the ssh cmd for launching remote commands. The default is ['ssh']
+# c.SSHLauncher.ssh_cmd = ['ssh']
+
+# set the ssh cmd for launching remote commands. The default is ['ssh']
+# c.SSHLauncher.ssh_args = ['tt']
+
+# Set the user and hostname for the controller
+# c.SSHControllerLauncher.hostname = 'controller.example.com'
+# c.SSHControllerLauncher.user = os.environ.get('USER','username')
+
+# Set the arguments to be passed to ipcontrollerz
+# note that remotely launched ipcontrollerz will not get the contents of 
+# the local ipcontrollerz_config.py unless it resides on the *remote host*
+# in the location specified by the --cluster_dir argument.
+# c.SSHControllerLauncher.program_args = ['-r', '-ip', '0.0.0.0', '--cluster_dir', '/path/to/cd']
+
+# Set the default args passed to ipenginez for SSH launched engines
+# c.SSHEngineSetLauncher.engine_args = ['--mpi', 'mpi4py']
 
+# SSH engines are launched as a dict of locations/n-engines.
+# if a value is a tuple instead of an int, it is assumed to be of the form
+# (n, [args]), setting the arguments to passed to ipenginez on `host`.
+# otherwise, c.SSHEngineSetLauncher.engine_args will be used as the default.
+
+# In this case, there will be 3 engines at my.example.com, and
+# 2 at [e-mail] with a special json connector location.
+# c.SSHEngineSetLauncher.engines = {'my.example.com' : 3,
+#                                   '[e-mail]' : (2, ['-f', '/path/to/ipcontroller-engine.json']}
+#                                  }
 
 #-----------------------------------------------------------------------------
 # Unix batch (PBS) schedulers launchers
 #-----------------------------------------------------------------------------
 
+# SGE and PBS are very similar. All configurables in this section called 'PBS*'
+# also exist as 'SGE*'.
+
 # The command line program to use to submit a PBS job.
-# c.PBSControllerLauncher.submit_command = 'qsub'
+# c.PBSLauncher.submit_command = ['qsub']
 
 # The command line program to use to delete a PBS job.
-# c.PBSControllerLauncher.delete_command = 'qdel'
+# c.PBSLauncher.delete_command = ['qdel']
+
+# The PBS queue in which the job should run
+# c.PBSLauncher.queue = 'myqueue'
 
 # A regular expression that takes the output of qsub and find the job id.
-# c.PBSControllerLauncher.job_id_regexp = r'\d+'
+# c.PBSLauncher.job_id_regexp = r'\d+'
+
+# If for some reason the Controller and Engines have different options above, they
+# can be set as c.PBSControllerLauncher.<option> etc.
+
+# PBS and SGE have default templates, but you can specify your own, either as strings
+# or from files, as described here:
 
 # The batch submission script used to start the controller. This is where
-# environment variables would be setup, etc. This string is interpolated using
+# environment variables would be setup, etc. This string is interpreted using
 # the Itpl module in IPython.external. Basically, you can use ${n} for the
 # number of engine and ${cluster_dir} for the cluster_dir.
-# c.PBSControllerLauncher.batch_template = """"""
+# c.PBSControllerLauncher.batch_template = """
+# #PBS -N ipcontroller
+# #PBS -q $queue
+# 
+# ipcontrollerz --cluster-dir $cluster_dir
+# """
+
+# You can also load this template from a file
+# c.PBSControllerLauncher.batch_template_file = u"/path/to/my/template.sh"
 
 # The name of the instantiated batch script that will actually be used to
 # submit the job. This will be written to the cluster directory.
-# c.PBSControllerLauncher.batch_file_name = u'pbs_batch_script_controller'
-
-
-# The command line program to use to submit a PBS job.
-# c.PBSEngineSetLauncher.submit_command = 'qsub'
-
-# The command line program to use to delete a PBS job.
-# c.PBSEngineSetLauncher.delete_command = 'qdel'
-
-# A regular expression that takes the output of qsub and find the job id.
-# c.PBSEngineSetLauncher.job_id_regexp = r'\d+'
+# c.PBSControllerLauncher.batch_file_name = u'pbs_controller'
 
 # The batch submission script used to start the engines. This is where
-# environment variables would be setup, etc. This string is interpolated using
+# environment variables would be setup, etc. This string is interpreted using
 # the Itpl module in IPython.external. Basically, you can use ${n} for the
 # number of engine and ${cluster_dir} for the cluster_dir.
-# c.PBSEngineSetLauncher.batch_template = """"""
+# c.PBSEngineSetLauncher.batch_template = """
+# #PBS -N ipcontroller
+# #PBS -l nprocs=$n
+# 
+# ipenginez --cluster-dir $cluster_dir$s
+# """
+
+# You can also load this template from a file
+# c.PBSControllerLauncher.batch_template_file = u"/path/to/my/template.sh"
 
 # The name of the instantiated batch script that will actually be used to
 # submit the job. This will be written to the cluster directory.
-# c.PBSEngineSetLauncher.batch_file_name = u'pbs_batch_script_engines'
+# c.PBSEngineSetLauncher.batch_file_name = u'pbs_engines'
+
+
 
 #-----------------------------------------------------------------------------
 # Windows HPC Server 2008 launcher configuration
 #-----------------------------------------------------------------------------
 
 # c.IPControllerJob.job_name = 'IPController'
 # c.IPControllerJob.is_exclusive = False
 # c.IPControllerJob.username = r'USERDOMAIN\USERNAME'
 # c.IPControllerJob.priority = 'Highest'
 # c.IPControllerJob.requested_nodes = ''
 # c.IPControllerJob.project = 'MyProject'
 
 # c.IPControllerTask.task_name = 'IPController'
 # c.IPControllerTask.controller_cmd = [u'ipcontroller.exe']
 # c.IPControllerTask.controller_args = ['--log-to-file', '--log-level', '40']
 # c.IPControllerTask.environment_variables = {}
 
 # c.WindowsHPCControllerLauncher.scheduler = 'HEADNODE'
 # c.WindowsHPCControllerLauncher.job_file_name = u'ipcontroller_job.xml'
 
 
 # c.IPEngineSetJob.job_name = 'IPEngineSet'
 # c.IPEngineSetJob.is_exclusive = False
 # c.IPEngineSetJob.username = r'USERDOMAIN\USERNAME'
 # c.IPEngineSetJob.priority = 'Highest'
 # c.IPEngineSetJob.requested_nodes = ''
 # c.IPEngineSetJob.project = 'MyProject'
 
 # c.IPEngineTask.task_name = 'IPEngine'
 # c.IPEngineTask.engine_cmd = [u'ipengine.exe']
 # c.IPEngineTask.engine_args = ['--log-to-file', '--log-level', '40']
 # c.IPEngineTask.environment_variables = {}
 
 # c.WindowsHPCEngineSetLauncher.scheduler = 'HEADNODE'
 # c.WindowsHPCEngineSetLauncher.job_file_name = u'ipengineset_job.xml'
 
 
 
 
 
 
 

IPython/config/default/ipcontroller_config.py

@@ -1,136 +1,180 b''
 from IPython.config.loader import Config
 
 c = get_config()
 
 #-----------------------------------------------------------------------------
 # Global configuration
 #-----------------------------------------------------------------------------
 
 # Basic Global config attributes
 
 # Start up messages are logged to stdout using the logging module.
 # These all happen before the twisted reactor is started and are
 # useful for debugging purposes. Can be (10=DEBUG,20=INFO,30=WARN,40=CRITICAL) 
 # and smaller is more verbose.
 # c.Global.log_level = 20
 
 # Log to a file in cluster_dir/log, otherwise just log to sys.stdout.
 # c.Global.log_to_file = False
 
 # Remove old logs from cluster_dir/log before starting.
 # c.Global.clean_logs = True
 
 # A list of Python statements that will be run before starting the 
 # controller. This is provided because occasionally certain things need to 
 # be imported in the controller for pickling to work.
 # c.Global.import_statements = ['import math']
 
-# Reuse the controller's FURL files. If False, FURL files are regenerated
+# Reuse the controller's JSON files. If False, JSON files are regenerated
 # each time the controller is run. If True, they will be reused, *but*, you
 # also must set the network ports by hand. If set, this will override the
 # values set for the client and engine connections below.
-# c.Global.reuse_furls = True
+# c.Global.reuse_files = True
 
-# Enable SSL encryption on all connections to the controller. If set, this
-# will override the values set for the client and engine connections below.
+# Enable exec_key authentication on all messages. Default is True
 # c.Global.secure = True
 
 # The working directory for the process. The application will use os.chdir
 # to change to this directory before starting.
 # c.Global.work_dir = os.getcwd()
 
+# The log url for logging to an `iploggerz` application.  This will override
+# log-to-file.
+# c.Global.log_url = 'tcp://127.0.0.1:20202'
+
+# The specific external IP that is used to disambiguate multi-interface URLs.
+# The default behavior is to guess from external IPs gleaned from `socket`.
+# c.Global.location = '192.168.1.123'
+
+# The ssh server remote clients should use to connect to this controller.
+# It must be a machine that can see the interface specified in client_ip.
+# The default for client_ip is localhost, in which case the sshserver must
+# be an external IP of the controller machine.
+# c.Global.sshserver = 'controller.example.com'
+
+# the url to use for registration.  If set, this overrides engine-ip,
+# engine-transport client-ip,client-transport, and regport.
+# c.RegistrationFactory.url = 'tcp://*:12345'
+
+# the port to use for registration.  Clients and Engines both use this
+# port for registration.
+# c.RegistrationFactory.regport = 10101
+
 #-----------------------------------------------------------------------------
-# Configure the client services
+# Configure the Task Scheduler
 #-----------------------------------------------------------------------------
 
-# Basic client service config attributes
+# The routing scheme. 'pure' will use the pure-ZMQ scheduler. Any other 
+# value will use a Python scheduler with various routing schemes.
+# python schemes are: lru, weighted, random, twobin. Default is 'weighted'.
+# Note that the pure ZMQ scheduler does not support many features, such as
+# dying engines, dependencies, or engine-subset load-balancing.
+# c.ControllerFactory.scheme = 'pure'
 
-# The network interface the controller will listen on for client connections.
-# This should be an IP address or hostname of the controller's host. The empty
-# string means listen on all interfaces.
-# c.FCClientServiceFactory.ip = ''
+# The pure ZMQ scheduler can limit the number of outstanding tasks per engine
+# by using the ZMQ HWM option.  This allows engines with long-running tasks
+# to not steal too many tasks from other engines. The default is 0, which
+# means agressively distribute messages, never waiting for them to finish.
+# c.ControllerFactory.hwm = 1
 
-# The TCP/IP port the controller will listen on for client connections. If 0
-# a random port will be used. If the controller's host has a firewall running
-# it must allow incoming traffic on this port.
-# c.FCClientServiceFactory.port = 0
+# Whether to use Threads or Processes to start the Schedulers.  Threads will
+# use less resources, but potentially reduce throughput. Default is to 
+# use processes.  Note that the a Python scheduler will always be in a Process.
+# c.ControllerFactory.usethreads
 
-# The client learns how to connect to the controller by looking at the
-# location field embedded in the FURL. If this field is empty, all network
-# interfaces that the controller is listening on will be listed. To have the
-# client connect on a particular interface, list it here.
-# c.FCClientServiceFactory.location = ''
+#-----------------------------------------------------------------------------
+# Configure the Hub
+#-----------------------------------------------------------------------------
+
+# Which class to use for the db backend.  Currently supported are DictDB (the
+# default), and MongoDB. Uncomment this line to enable MongoDB, which will
+# slow-down the Hub's responsiveness, but also reduce its memory footprint.
+# c.HubFactory.db_class = 'IPython.parallel.mongodb.MongoDB'
 
-# Use SSL encryption for the client connection.
-# c.FCClientServiceFactory.secure =  True
+# The heartbeat ping frequency.  This is the frequency (in ms) at which the
+# Hub pings engines for heartbeats.  This determines how quickly the Hub
+# will react to engines coming and going.  A lower number means faster response
+# time, but more network activity.  The default is 100ms
+# c.HubFactory.ping = 100
 
-# Reuse the client FURL each time the controller is started. If set, you must
-# also pick a specific network port above (FCClientServiceFactory.port).
-# c.FCClientServiceFactory.reuse_furls = False
+# HubFactory queue port pairs, to set by name: mux, iopub, control, task.  Set
+# each as a tuple of length 2 of ints.  The default is to find random
+# available ports
+# c.HubFactory.mux = (10102,10112)
 
 #-----------------------------------------------------------------------------
-# Configure the engine services
+# Configure the client connections
 #-----------------------------------------------------------------------------
 
-# Basic config attributes for the engine services.
+# Basic client connection config attributes
 
-# The network interface the controller will listen on for engine connections.
-# This should be an IP address or hostname of the controller's host. The empty
-# string means listen on all interfaces.
-# c.FCEngineServiceFactory.ip = ''
+# The network interface the controller will listen on for client connections.
+# This should be an IP address or interface on the controller. An asterisk
+# means listen on all interfaces. The transport can be any transport
+# supported by zeromq (tcp,epgm,pgm,ib,ipc):
+# c.HubFactory.client_ip = '*'
+# c.HubFactory.client_transport = 'tcp'
 
-# The TCP/IP port the controller will listen on for engine connections. If 0
-# a random port will be used. If the controller's host has a firewall running
-# it must allow incoming traffic on this port.
-# c.FCEngineServiceFactory.port = 0
+# individual client ports to configure by name: query_port, notifier_port
+# c.HubFactory.query_port = 12345
 
-# The engine learns how to connect to the controller by looking at the
-# location field embedded in the FURL. If this field is empty, all network
-# interfaces that the controller is listening on will be listed. To have the
-# client connect on a particular interface, list it here.
-# c.FCEngineServiceFactory.location = ''
+#-----------------------------------------------------------------------------
+# Configure the engine connections
+#-----------------------------------------------------------------------------
 
-# Use SSL encryption for the engine connection.
-# c.FCEngineServiceFactory.secure = True
+# Basic config attributes for the engine connections.
 
-# Reuse the client FURL each time the controller is started. If set, you must
-# also pick a specific network port above (FCClientServiceFactory.port).
-# c.FCEngineServiceFactory.reuse_furls = False
+# The network interface the controller will listen on for engine connections.
+# This should be an IP address or interface on the controller. An asterisk
+# means listen on all interfaces. The transport can be any transport
+# supported by zeromq (tcp,epgm,pgm,ib,ipc):
+# c.HubFactory.engine_ip = '*'
+# c.HubFactory.engine_transport = 'tcp'
+
+# set the engine heartbeat ports to use:
+# c.HubFactory.hb = (10303,10313)
 
 #-----------------------------------------------------------------------------
-# Developer level configuration attributes
+# Configure the TaskRecord database backend
 #-----------------------------------------------------------------------------
 
-# You shouldn't have to modify anything in this section. These attributes
-# are more for developers who want to change the behavior of the controller
-# at a fundamental level.
-
-# c.FCClientServiceFactory.cert_file = u'ipcontroller-client.pem'
-
-# default_client_interfaces = Config()
-# default_client_interfaces.Task.interface_chain = [
-#     'IPython.kernel.task.ITaskController',
-#     'IPython.kernel.taskfc.IFCTaskController'
-# ]
-# 
-# default_client_interfaces.Task.furl_file = u'ipcontroller-tc.furl'
-# 
-# default_client_interfaces.MultiEngine.interface_chain = [
-#     'IPython.kernel.multiengine.IMultiEngine',
-#     'IPython.kernel.multienginefc.IFCSynchronousMultiEngine'
-# ]
-# 
-# default_client_interfaces.MultiEngine.furl_file = u'ipcontroller-mec.furl'
-# 
-# c.FCEngineServiceFactory.interfaces = default_client_interfaces
-
-# c.FCEngineServiceFactory.cert_file = u'ipcontroller-engine.pem'
-
-# default_engine_interfaces = Config()
-# default_engine_interfaces.Default.interface_chain = [
-#     'IPython.kernel.enginefc.IFCControllerBase'
-# ]
-# 
-# default_engine_interfaces.Default.furl_file = u'ipcontroller-engine.furl'
-# 
-# c.FCEngineServiceFactory.interfaces = default_engine_interfaces
+# For memory/persistance reasons, tasks can be stored out-of-memory in a database.
+# Currently, only sqlite and mongodb are supported as backends, but the interface
+# is fairly simple, so advanced developers could write their own backend.
+
+# ----- in-memory configuration --------
+# this line restores the default behavior: in-memory storage of all results.
+# c.HubFactory.db_class = 'IPython.parallel.dictdb.DictDB'
+
+# ----- sqlite configuration --------
+# use this line to activate sqlite:
+# c.HubFactory.db_class = 'IPython.parallel.sqlitedb.SQLiteDB'
+
+# You can specify the name of the db-file.  By default, this will be located
+# in the active cluster_dir, e.g. ~/.ipython/clusterz_default/tasks.db
+# c.SQLiteDB.filename = 'tasks.db'
+
+# You can also specify the location of the db-file, if you want it to be somewhere
+# other than the cluster_dir.
+# c.SQLiteDB.location = '/scratch/'
+
+# This will specify the name of the table for the controller to use.  The default
+# behavior is to use the session ID of the SessionFactory object (a uuid). Overriding
+# this will result in results persisting for multiple sessions.
+# c.SQLiteDB.table = 'results'
+
+# ----- mongodb configuration --------
+# use this line to activate mongodb:
+# c.HubFactory.db_class = 'IPython.parallel.mongodb.MongoDB'
+
+# You can specify the args and kwargs pymongo will use when creating the Connection.
+# For more information on what these options might be, see pymongo documentation.
+# c.MongoDB.connection_kwargs = {}
+# c.MongoDB.connection_args = []
+
+# This will specify the name of the mongo database for the controller to use.  The default
+# behavior is to use the session ID of the SessionFactory object (a uuid). Overriding
+# this will result in task results persisting through multiple sessions.
+# c.MongoDB.database = 'ipythondb'
+
+

IPython/config/default/ipengine_config.py

@@ -1,90 +1,85 b''
 c = get_config()
 
 #-----------------------------------------------------------------------------
 # Global configuration
 #-----------------------------------------------------------------------------
 
 # Start up messages are logged to stdout using the logging module.
 # These all happen before the twisted reactor is started and are
 # useful for debugging purposes. Can be (10=DEBUG,20=INFO,30=WARN,40=CRITICAL) 
 # and smaller is more verbose.
 # c.Global.log_level = 20
 
 # Log to a file in cluster_dir/log, otherwise just log to sys.stdout.
 # c.Global.log_to_file = False
 
 # Remove old logs from cluster_dir/log before starting.
 # c.Global.clean_logs = True
 
 # A list of strings that will be executed in the users namespace on the engine
 # before it connects to the controller.
 # c.Global.exec_lines = ['import numpy']
 
 # The engine will try to connect to the controller multiple times, to allow
 # the controller time to startup and write its FURL file. These parameters 
 # control the number of retries (connect_max_tries) and the initial delay
 # (connect_delay) between attemps. The actual delay between attempts gets
 # longer each time by a factor of 1.5 (delay[i] = 1.5*delay[i-1])
 # those attemps.
 # c.Global.connect_delay = 0.1
 # c.Global.connect_max_tries = 15
 
-# By default, the engine will look for the controller's FURL file in its own
-# cluster directory. Sometimes, the FURL file will be elsewhere and this 
-# attribute can be set to the full path of the FURL file.
-# c.Global.furl_file = u''
+# By default, the engine will look for the controller's JSON file in its own
+# cluster directory. Sometimes, the JSON file will be elsewhere and this 
+# attribute can be set to the full path of the JSON file.
+# c.Global.url_file = u'/path/to/my/ipcontroller-engine.json'
 
 # The working directory for the process. The application will use os.chdir
 # to change to this directory before starting.
 # c.Global.work_dir = os.getcwd()
 
 #-----------------------------------------------------------------------------
 # MPI configuration
 #-----------------------------------------------------------------------------
 
 # Upon starting the engine can be configured to call MPI_Init. This section
 # configures that.
 
 # Select which MPI section to execute to setup MPI. The value of this 
 # attribute must match the name of another attribute in the MPI config 
 # section (mpi4py, pytrilinos, etc.). This can also be set by the --mpi
 # command line option.
 # c.MPI.use = ''
 
 # Initialize MPI using mpi4py. To use this, set c.MPI.use = 'mpi4py' to use
 # --mpi=mpi4py at the command line.
 # c.MPI.mpi4py = """from mpi4py import MPI as mpi
 # mpi.size = mpi.COMM_WORLD.Get_size()
 # mpi.rank = mpi.COMM_WORLD.Get_rank()
 # """
 
 # Initialize MPI using pytrilinos. To use this, set c.MPI.use = 'pytrilinos' 
 # to use --mpi=pytrilinos at the command line.
 # c.MPI.pytrilinos = """from PyTrilinos import Epetra
 # class SimpleStruct:
 # pass
 # mpi = SimpleStruct()
 # mpi.rank = 0
 # mpi.size = 0
 # """
 
 #-----------------------------------------------------------------------------
 # Developer level configuration attributes
 #-----------------------------------------------------------------------------
 
 # You shouldn't have to modify anything in this section. These attributes
 # are more for developers who want to change the behavior of the controller
 # at a fundamental level.
 
 # You should not have to change these attributes.
 
-# c.Global.shell_class = 'IPython.kernel.core.interpreter.Interpreter'
-
-# c.Global.furl_file_name = u'ipcontroller-engine.furl'
-
-
-
+# c.Global.url_file_name = u'ipcontroller-engine.furl'
 
 
 
 

IPython/parallel/clusterdir.py

@@ -1,537 +1,537 b''
 #!/usr/bin/env python
 # encoding: utf-8
 """
 The IPython cluster directory
 """
 
 #-----------------------------------------------------------------------------
 #  Copyright (C) 2008-2009  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-----------------------------------------------------------------------------
 
 #-----------------------------------------------------------------------------
 # Imports
 #-----------------------------------------------------------------------------
 
 from __future__ import with_statement
 
 import os
 import logging
 import re
 import shutil
 import sys
 
 from IPython.config.loader import PyFileConfigLoader
 from IPython.config.configurable import Configurable
 from IPython.core.application import Application, BaseAppConfigLoader
 from IPython.core.crashhandler import CrashHandler
 from IPython.core import release
 from IPython.utils.path import (
     get_ipython_package_dir,
     expand_path
 )
 from IPython.utils.traitlets import Unicode
 
 #-----------------------------------------------------------------------------
 # Module errors
 #-----------------------------------------------------------------------------
 
 class ClusterDirError(Exception):
     pass
 
 
 class PIDFileError(Exception):
     pass
 
 
 #-----------------------------------------------------------------------------
 # Class for managing cluster directories
 #-----------------------------------------------------------------------------
 
 class ClusterDir(Configurable):
     """An object to manage the cluster directory and its resources.
 
     The cluster directory is used by :command:`ipengine`, 
     :command:`ipcontroller` and :command:`ipclsuter` to manage the
     configuration, logging and security of these applications.
 
     This object knows how to find, create and manage these directories. This
     should be used by any code that want's to handle cluster directories.
     """
 
     security_dir_name = Unicode('security')
     log_dir_name = Unicode('log')
     pid_dir_name = Unicode('pid')
     security_dir = Unicode(u'')
     log_dir = Unicode(u'')
     pid_dir = Unicode(u'')
     location = Unicode(u'')
 
     def __init__(self, location=u''):
         super(ClusterDir, self).__init__(location=location)
 
     def _location_changed(self, name, old, new):
         if not os.path.isdir(new):
             os.makedirs(new)
         self.security_dir = os.path.join(new, self.security_dir_name)
         self.log_dir = os.path.join(new, self.log_dir_name)
         self.pid_dir = os.path.join(new, self.pid_dir_name)
         self.check_dirs()
 
     def _log_dir_changed(self, name, old, new):
         self.check_log_dir()
 
     def check_log_dir(self):
         if not os.path.isdir(self.log_dir):
             os.mkdir(self.log_dir)
 
     def _security_dir_changed(self, name, old, new):
         self.check_security_dir()
 
     def check_security_dir(self):
         if not os.path.isdir(self.security_dir):
             os.mkdir(self.security_dir, 0700)
         os.chmod(self.security_dir, 0700)
 
     def _pid_dir_changed(self, name, old, new):
         self.check_pid_dir()
 
     def check_pid_dir(self):
         if not os.path.isdir(self.pid_dir):
             os.mkdir(self.pid_dir, 0700)
         os.chmod(self.pid_dir, 0700)
 
     def check_dirs(self):
         self.check_security_dir()
         self.check_log_dir()
         self.check_pid_dir()
 
     def load_config_file(self, filename):
         """Load a config file from the top level of the cluster dir.
 
         Parameters
         ----------
         filename : unicode or str
             The filename only of the config file that must be located in
             the top-level of the cluster directory.
         """
         loader = PyFileConfigLoader(filename, self.location)
         return loader.load_config()
 
     def copy_config_file(self, config_file, path=None, overwrite=False):
         """Copy a default config file into the active cluster directory.
 
         Default configuration files are kept in :mod:`IPython.config.default`.
         This function moves these from that location to the working cluster
         directory.
         """
         if path is None:
             import IPython.config.default
             path = IPython.config.default.__file__.split(os.path.sep)[:-1]
             path = os.path.sep.join(path)
         src = os.path.join(path, config_file)
         dst = os.path.join(self.location, config_file)
         if not os.path.isfile(dst) or overwrite:
             shutil.copy(src, dst)
 
     def copy_all_config_files(self, path=None, overwrite=False):
         """Copy all config files into the active cluster directory."""
-        for f in [u'ipcontrollerz_config.py', u'ipenginez_config.py',
-                  u'ipclusterz_config.py']:
+        for f in [u'ipcontroller_config.py', u'ipengine_config.py',
+                  u'ipcluster_config.py']:
             self.copy_config_file(f, path=path, overwrite=overwrite)
 
     @classmethod
     def create_cluster_dir(csl, cluster_dir):
         """Create a new cluster directory given a full path.
 
         Parameters
         ----------
         cluster_dir : str
             The full path to the cluster directory.  If it does exist, it will
             be used.  If not, it will be created.
         """
         return ClusterDir(location=cluster_dir)
 
     @classmethod
     def create_cluster_dir_by_profile(cls, path, profile=u'default'):
         """Create a cluster dir by profile name and path.
 
         Parameters
         ----------
         path : str
             The path (directory) to put the cluster directory in.
         profile : str
             The name of the profile.  The name of the cluster directory will
-            be "clusterz_<profile>".
+            be "cluster_<profile>".
         """
         if not os.path.isdir(path):
             raise ClusterDirError('Directory not found: %s' % path)
-        cluster_dir = os.path.join(path, u'clusterz_' + profile)
+        cluster_dir = os.path.join(path, u'cluster_' + profile)
         return ClusterDir(location=cluster_dir)
 
     @classmethod
     def find_cluster_dir_by_profile(cls, ipython_dir, profile=u'default'):
         """Find an existing cluster dir by profile name, return its ClusterDir.
 
         This searches through a sequence of paths for a cluster dir.  If it
         is not found, a :class:`ClusterDirError` exception will be raised.
 
         The search path algorithm is:
         1. ``os.getcwd()``
         2. ``ipython_dir``
         3. The directories found in the ":" separated 
            :env:`IPCLUSTER_DIR_PATH` environment variable.
 
         Parameters
         ----------
         ipython_dir : unicode or str
             The IPython directory to use.
         profile : unicode or str
             The name of the profile.  The name of the cluster directory
-            will be "clusterz_<profile>".
+            will be "cluster_<profile>".
         """
-        dirname = u'clusterz_' + profile
+        dirname = u'cluster_' + profile
         cluster_dir_paths = os.environ.get('IPCLUSTER_DIR_PATH','')
         if cluster_dir_paths:
             cluster_dir_paths = cluster_dir_paths.split(':')
         else:
             cluster_dir_paths = []
         paths = [os.getcwd(), ipython_dir] + cluster_dir_paths
         for p in paths:
             cluster_dir = os.path.join(p, dirname)
             if os.path.isdir(cluster_dir):
                 return ClusterDir(location=cluster_dir)
         else:
             raise ClusterDirError('Cluster directory not found in paths: %s' % dirname)
 
     @classmethod
     def find_cluster_dir(cls, cluster_dir):
         """Find/create a cluster dir and return its ClusterDir.
 
         This will create the cluster directory if it doesn't exist.
 
         Parameters
         ----------
         cluster_dir : unicode or str
             The path of the cluster directory.  This is expanded using
             :func:`IPython.utils.genutils.expand_path`.
         """
         cluster_dir = expand_path(cluster_dir)
         if not os.path.isdir(cluster_dir):
             raise ClusterDirError('Cluster directory not found: %s' % cluster_dir)
         return ClusterDir(location=cluster_dir)
 
 
 #-----------------------------------------------------------------------------
 # Command line options
 #-----------------------------------------------------------------------------
 
 class ClusterDirConfigLoader(BaseAppConfigLoader):
 
     def _add_cluster_profile(self, parser):
         paa = parser.add_argument
         paa('-p', '--profile',
             dest='Global.profile',type=unicode,
             help=
             """The string name of the profile to be used. This determines the name
             of the cluster dir as: cluster_<profile>. The default profile is named
             'default'.  The cluster directory is resolve this way if the
             --cluster-dir option is not used.""",
             metavar='Global.profile')
 
     def _add_cluster_dir(self, parser):
         paa = parser.add_argument
         paa('--cluster-dir',
             dest='Global.cluster_dir',type=unicode,
             help="""Set the cluster dir. This overrides the logic used by the
             --profile option.""",
             metavar='Global.cluster_dir')
 
     def _add_work_dir(self, parser):
         paa = parser.add_argument
         paa('--work-dir',
             dest='Global.work_dir',type=unicode,
             help='Set the working dir for the process.',
             metavar='Global.work_dir')
 
     def _add_clean_logs(self, parser):
         paa = parser.add_argument
         paa('--clean-logs',
             dest='Global.clean_logs', action='store_true',
             help='Delete old log flies before starting.')
 
     def _add_no_clean_logs(self, parser):
         paa = parser.add_argument
         paa('--no-clean-logs',
             dest='Global.clean_logs', action='store_false',
             help="Don't Delete old log flies before starting.")
 
     def _add_arguments(self):
         super(ClusterDirConfigLoader, self)._add_arguments()
         self._add_cluster_profile(self.parser)
         self._add_cluster_dir(self.parser)
         self._add_work_dir(self.parser)
         self._add_clean_logs(self.parser)
         self._add_no_clean_logs(self.parser)
 
 
 #-----------------------------------------------------------------------------
 # Crash handler for this application
 #-----------------------------------------------------------------------------
 
 
 _message_template = """\
 Oops, $self.app_name crashed. We do our best to make it stable, but...
 
 A crash report was automatically generated with the following information:
   - A verbatim copy of the crash traceback.
   - Data on your current $self.app_name configuration.
 
 It was left in the file named:
 \t'$self.crash_report_fname'
 If you can email this file to the developers, the information in it will help
 them in understanding and correcting the problem.
 
 You can mail it to: $self.contact_name at $self.contact_email
 with the subject '$self.app_name Crash Report'.
 
 If you want to do it now, the following command will work (under Unix):
 mail -s '$self.app_name Crash Report' $self.contact_email < $self.crash_report_fname
 
 To ensure accurate tracking of this issue, please file a report about it at:
 $self.bug_tracker
 """
 
 class ClusterDirCrashHandler(CrashHandler):
     """sys.excepthook for IPython itself, leaves a detailed report on disk."""
 
     message_template = _message_template
 
     def __init__(self, app):
         contact_name = release.authors['Brian'][0]
         contact_email = release.authors['Brian'][1]
         bug_tracker = 'http://github.com/ipython/ipython/issues'
         super(ClusterDirCrashHandler,self).__init__(
             app, contact_name, contact_email, bug_tracker
         )
 
 
 #-----------------------------------------------------------------------------
 # Main application
 #-----------------------------------------------------------------------------
 
 class ApplicationWithClusterDir(Application):
     """An application that puts everything into a cluster directory.
 
     Instead of looking for things in the ipython_dir, this type of application
     will use its own private directory called the "cluster directory"
     for things like config files, log files, etc.
 
     The cluster directory is resolved as follows:
 
     * If the ``--cluster-dir`` option is given, it is used.
     * If ``--cluster-dir`` is not given, the application directory is 
       resolve using the profile name as ``cluster_<profile>``. The search 
       path for this directory is then i) cwd if it is found there
       and ii) in ipython_dir otherwise.
 
     The config file for the application is to be put in the cluster
     dir and named the value of the ``config_file_name`` class attribute.
     """
 
     command_line_loader = ClusterDirConfigLoader
     crash_handler_class = ClusterDirCrashHandler
     auto_create_cluster_dir = True
     # temporarily override default_log_level to INFO
     default_log_level = logging.INFO
 
     def create_default_config(self):
         super(ApplicationWithClusterDir, self).create_default_config()
         self.default_config.Global.profile = u'default'
         self.default_config.Global.cluster_dir = u''
         self.default_config.Global.work_dir = os.getcwd()
         self.default_config.Global.log_to_file = False
         self.default_config.Global.log_url = None
         self.default_config.Global.clean_logs = False
 
     def find_resources(self):
         """This resolves the cluster directory.
 
         This tries to find the cluster directory and if successful, it will
         have done:
         * Sets ``self.cluster_dir_obj`` to the :class:`ClusterDir` object for 
           the application.
         * Sets ``self.cluster_dir`` attribute of the application and config
           objects.
 
         The algorithm used for this is as follows:
         1. Try ``Global.cluster_dir``.
         2. Try using ``Global.profile``.
         3. If both of these fail and ``self.auto_create_cluster_dir`` is
            ``True``, then create the new cluster dir in the IPython directory.
         4. If all fails, then raise :class:`ClusterDirError`.
         """
 
         try:
             cluster_dir = self.command_line_config.Global.cluster_dir
         except AttributeError:
             cluster_dir = self.default_config.Global.cluster_dir
         cluster_dir = expand_path(cluster_dir)
         try:
             self.cluster_dir_obj = ClusterDir.find_cluster_dir(cluster_dir)
         except ClusterDirError:
             pass
         else:
             self.log.info('Using existing cluster dir: %s' % \
                 self.cluster_dir_obj.location
             )
             self.finish_cluster_dir()
             return
 
         try:
             self.profile = self.command_line_config.Global.profile
         except AttributeError:
             self.profile = self.default_config.Global.profile
         try:
             self.cluster_dir_obj = ClusterDir.find_cluster_dir_by_profile(
                 self.ipython_dir, self.profile)
         except ClusterDirError:
             pass
         else:
             self.log.info('Using existing cluster dir: %s' % \
                 self.cluster_dir_obj.location
             )
             self.finish_cluster_dir()
             return
 
         if self.auto_create_cluster_dir:
             self.cluster_dir_obj = ClusterDir.create_cluster_dir_by_profile(
                 self.ipython_dir, self.profile
             )
             self.log.info('Creating new cluster dir: %s' % \
                 self.cluster_dir_obj.location
             )
             self.finish_cluster_dir()
         else:
             raise ClusterDirError('Could not find a valid cluster directory.')
 
     def finish_cluster_dir(self):
         # Set the cluster directory
         self.cluster_dir = self.cluster_dir_obj.location
         
         # These have to be set because they could be different from the one
         # that we just computed.  Because command line has the highest
         # priority, this will always end up in the master_config.
         self.default_config.Global.cluster_dir = self.cluster_dir
         self.command_line_config.Global.cluster_dir = self.cluster_dir
 
     def find_config_file_name(self):
         """Find the config file name for this application."""
         # For this type of Application it should be set as a class attribute.
         if not hasattr(self, 'default_config_file_name'):
             self.log.critical("No config filename found")
         else:
             self.config_file_name = self.default_config_file_name
 
     def find_config_file_paths(self):
         # Set the search path to to the cluster directory. We should NOT
         # include IPython.config.default here as the default config files
         # are ALWAYS automatically moved to the cluster directory.
         conf_dir = os.path.join(get_ipython_package_dir(), 'config', 'default')
         self.config_file_paths = (self.cluster_dir,)
 
     def pre_construct(self):
         # The log and security dirs were set earlier, but here we put them
         # into the config and log them.
         config = self.master_config
         sdir = self.cluster_dir_obj.security_dir
         self.security_dir = config.Global.security_dir = sdir
         ldir = self.cluster_dir_obj.log_dir
         self.log_dir = config.Global.log_dir = ldir
         pdir = self.cluster_dir_obj.pid_dir
         self.pid_dir = config.Global.pid_dir = pdir
         self.log.info("Cluster directory set to: %s" % self.cluster_dir)
         config.Global.work_dir = unicode(expand_path(config.Global.work_dir))
         # Change to the working directory. We do this just before construct
         # is called so all the components there have the right working dir.
         self.to_work_dir()
 
     def to_work_dir(self):
         wd = self.master_config.Global.work_dir
         if unicode(wd) != unicode(os.getcwd()):
             os.chdir(wd)
             self.log.info("Changing to working dir: %s" % wd)
 
     def start_logging(self):
         # Remove old log files
         if self.master_config.Global.clean_logs:
             log_dir = self.master_config.Global.log_dir
             for f in os.listdir(log_dir):
                 if re.match(r'%s-\d+\.(log|err|out)'%self.name,f):
                 # if f.startswith(self.name + u'-') and f.endswith('.log'):
                     os.remove(os.path.join(log_dir, f))
         # Start logging to the new log file
         if self.master_config.Global.log_to_file:
             log_filename = self.name + u'-' + str(os.getpid()) + u'.log'
             logfile = os.path.join(self.log_dir, log_filename)
             open_log_file = open(logfile, 'w')
         elif self.master_config.Global.log_url:
             open_log_file = None
         else:
             open_log_file = sys.stdout
         if open_log_file is not None:
             self.log.removeHandler(self._log_handler)
             self._log_handler = logging.StreamHandler(open_log_file)
             self._log_formatter = logging.Formatter("[%(name)s] %(message)s")
             self._log_handler.setFormatter(self._log_formatter)
             self.log.addHandler(self._log_handler)
         # log.startLogging(open_log_file)
 
     def write_pid_file(self, overwrite=False):
         """Create a .pid file in the pid_dir with my pid.
 
         This must be called after pre_construct, which sets `self.pid_dir`.
         This raises :exc:`PIDFileError` if the pid file exists already.
         """
         pid_file = os.path.join(self.pid_dir, self.name + u'.pid')
         if os.path.isfile(pid_file):
             pid = self.get_pid_from_file()
             if not overwrite:
                 raise PIDFileError(
                     'The pid file [%s] already exists. \nThis could mean that this '
                     'server is already running with [pid=%s].' % (pid_file, pid)
                 )
         with open(pid_file, 'w') as f:
             self.log.info("Creating pid file: %s" % pid_file)
             f.write(repr(os.getpid())+'\n')
 
     def remove_pid_file(self):
         """Remove the pid file.
 
         This should be called at shutdown by registering a callback with
         :func:`reactor.addSystemEventTrigger`. This needs to return
         ``None``.
         """
         pid_file = os.path.join(self.pid_dir, self.name + u'.pid')
         if os.path.isfile(pid_file):
             try:
                 self.log.info("Removing pid file: %s" % pid_file)
                 os.remove(pid_file)
             except:
                 self.log.warn("Error removing the pid file: %s" % pid_file)
 
     def get_pid_from_file(self):
         """Get the pid from the pid file.
 
         If the  pid file doesn't exist a :exc:`PIDFileError` is raised.
         """
         pid_file = os.path.join(self.pid_dir, self.name + u'.pid')
         if os.path.isfile(pid_file):
             with open(pid_file, 'r') as f:
                 pid = int(f.read().strip())
                 return pid
         else:
             raise PIDFileError('pid file not found: %s' % pid_file)
 

IPython/parallel/ipclusterapp.py

@@ -1,592 +1,592 b''
 #!/usr/bin/env python
 # encoding: utf-8
 """
 The ipcluster application.
 """
 
 #-----------------------------------------------------------------------------
 #  Copyright (C) 2008-2009  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-----------------------------------------------------------------------------
 
 #-----------------------------------------------------------------------------
 # Imports
 #-----------------------------------------------------------------------------
 
 import errno
 import logging
 import os
 import re
 import signal
 
 import zmq
 from zmq.eventloop import ioloop
 
 from IPython.external.argparse import ArgumentParser, SUPPRESS
 from IPython.utils.importstring import import_item
 from IPython.parallel.clusterdir import (
     ApplicationWithClusterDir, ClusterDirConfigLoader,
     ClusterDirError, PIDFileError
 )
 
 
 #-----------------------------------------------------------------------------
 # Module level variables
 #-----------------------------------------------------------------------------
 
 
-default_config_file_name = u'ipclusterz_config.py'
+default_config_file_name = u'ipcluster_config.py'
 
 
 _description = """\
 Start an IPython cluster for parallel computing.\n\n
 
 An IPython cluster consists of 1 controller and 1 or more engines.
 This command automates the startup of these processes using a wide
 range of startup methods (SSH, local processes, PBS, mpiexec,
 Windows HPC Server 2008). To start a cluster with 4 engines on your
-local host simply do 'ipclusterz start -n 4'. For more complex usage 
-you will typically do 'ipclusterz create -p mycluster', then edit
-configuration files, followed by 'ipclusterz start -p mycluster -n 4'.
+local host simply do 'ipcluster start -n 4'. For more complex usage 
+you will typically do 'ipcluster create -p mycluster', then edit
+configuration files, followed by 'ipcluster start -p mycluster -n 4'.
 """
 
 
 # Exit codes for ipcluster
 
 # This will be the exit code if the ipcluster appears to be running because
 # a .pid file exists
 ALREADY_STARTED = 10
 
 
 # This will be the exit code if ipcluster stop is run, but there is not .pid
 # file to be found.
 ALREADY_STOPPED = 11
 
 # This will be the exit code if ipcluster engines is run, but there is not .pid
 # file to be found.
 NO_CLUSTER = 12
 
 
 #-----------------------------------------------------------------------------
 # Command line options
 #-----------------------------------------------------------------------------
 
 
 class IPClusterAppConfigLoader(ClusterDirConfigLoader):
 
     def _add_arguments(self):
         # Don't call ClusterDirConfigLoader._add_arguments as we don't want
         # its defaults on self.parser. Instead, we will put those on
         # default options on our subparsers.
         
         # This has all the common options that all subcommands use
         parent_parser1 = ArgumentParser(
             add_help=False,
             argument_default=SUPPRESS
         )
         self._add_ipython_dir(parent_parser1)
         self._add_log_level(parent_parser1)
 
         # This has all the common options that other subcommands use
         parent_parser2 = ArgumentParser(
             add_help=False,
             argument_default=SUPPRESS
         )
         self._add_cluster_profile(parent_parser2)
         self._add_cluster_dir(parent_parser2)
         self._add_work_dir(parent_parser2)
         paa = parent_parser2.add_argument
         paa('--log-to-file',
             action='store_true', dest='Global.log_to_file', 
             help='Log to a file in the log directory (default is stdout)')
 
         # Create the object used to create the subparsers.
         subparsers = self.parser.add_subparsers(
             dest='Global.subcommand',
             title='ipcluster subcommands',
             description=
             """ipcluster has a variety of subcommands. The general way of 
-            running ipcluster is 'ipclusterz <cmd> [options]'. To get help
-            on a particular subcommand do 'ipclusterz <cmd> -h'."""
-            # help="For more help, type 'ipclusterz <cmd> -h'",
+            running ipcluster is 'ipcluster <cmd> [options]'. To get help
+            on a particular subcommand do 'ipcluster <cmd> -h'."""
+            # help="For more help, type 'ipcluster <cmd> -h'",
         )
 
         # The "list" subcommand parser
         parser_list = subparsers.add_parser(
             'list',
             parents=[parent_parser1],
             argument_default=SUPPRESS,
             help="List all clusters in cwd and ipython_dir.",
             description=
             """List all available clusters, by cluster directory, that can
             be found in the current working directly or in the ipython
             directory. Cluster directories are named using the convention
-            'clusterz_<profile>'."""
+            'cluster_<profile>'."""
         )
 
         # The "create" subcommand parser
         parser_create = subparsers.add_parser(
             'create',
             parents=[parent_parser1, parent_parser2],
             argument_default=SUPPRESS,
             help="Create a new cluster directory.",
             description=
             """Create an ipython cluster directory by its profile name or 
             cluster directory path. Cluster directories contain 
             configuration, log and security related files and are named 
-            using the convention 'clusterz_<profile>'. By default they are
+            using the convention 'cluster_<profile>'. By default they are
             located in your ipython directory. Once created, you will
             probably need to edit the configuration files in the cluster
             directory to configure your cluster. Most users will create a
             cluster directory by profile name, 
-            'ipclusterz create -p mycluster', which will put the directory
-            in '<ipython_dir>/clusterz_mycluster'. 
+            'ipcluster create -p mycluster', which will put the directory
+            in '<ipython_dir>/cluster_mycluster'. 
             """
         )
         paa = parser_create.add_argument
         paa('--reset-config',
             dest='Global.reset_config', action='store_true',
             help=
             """Recopy the default config files to the cluster directory.
             You will loose any modifications you have made to these files.""")
 
         # The "start" subcommand parser
         parser_start = subparsers.add_parser(
             'start',
             parents=[parent_parser1, parent_parser2],
             argument_default=SUPPRESS,
             help="Start a cluster.",
             description=
             """Start an ipython cluster by its profile name or cluster 
             directory. Cluster directories contain configuration, log and
             security related files and are named using the convention
-            'clusterz_<profile>' and should be creating using the 'start'
+            'cluster_<profile>' and should be creating using the 'start'
             subcommand of 'ipcluster'. If your cluster directory is in 
             the cwd or the ipython directory, you can simply refer to it
-            using its profile name, 'ipclusterz start -n 4 -p <profile>`,
+            using its profile name, 'ipcluster start -n 4 -p <profile>`,
             otherwise use the '--cluster-dir' option.
             """
         )
         
         paa = parser_start.add_argument
         paa('-n', '--number',
             type=int, dest='Global.n',
             help='The number of engines to start.',
             metavar='Global.n')
         paa('--clean-logs',
             dest='Global.clean_logs', action='store_true',
             help='Delete old log flies before starting.')
         paa('--no-clean-logs',
             dest='Global.clean_logs', action='store_false',
             help="Don't delete old log flies before starting.")
         paa('--daemon',
             dest='Global.daemonize', action='store_true',
             help='Daemonize the ipcluster program. This implies --log-to-file')
         paa('--no-daemon',
             dest='Global.daemonize', action='store_false',
             help="Dont't daemonize the ipcluster program.")
         paa('--delay',
             type=float, dest='Global.delay',
             help="Specify the delay (in seconds) between starting the controller and starting the engine(s).")
 
         # The "stop" subcommand parser
         parser_stop = subparsers.add_parser(
             'stop',
             parents=[parent_parser1, parent_parser2],
             argument_default=SUPPRESS,
             help="Stop a running cluster.",
             description=
             """Stop a running ipython cluster by its profile name or cluster 
             directory. Cluster directories are named using the convention
-            'clusterz_<profile>'. If your cluster directory is in 
+            'cluster_<profile>'. If your cluster directory is in 
             the cwd or the ipython directory, you can simply refer to it
-            using its profile name, 'ipclusterz stop -p <profile>`, otherwise
+            using its profile name, 'ipcluster stop -p <profile>`, otherwise
             use the '--cluster-dir' option.
             """
         )
         paa = parser_stop.add_argument
         paa('--signal',
             dest='Global.signal', type=int,
             help="The signal number to use in stopping the cluster (default=2).",
             metavar="Global.signal")
         
         # the "engines" subcommand parser
         parser_engines = subparsers.add_parser(
             'engines',
             parents=[parent_parser1, parent_parser2],
             argument_default=SUPPRESS,
             help="Attach some engines to an existing controller or cluster.",
             description=
             """Start one or more engines to connect to an existing Cluster
             by profile name or cluster directory.
             Cluster directories contain configuration, log and
             security related files and are named using the convention
-            'clusterz_<profile>' and should be creating using the 'start'
+            'cluster_<profile>' and should be creating using the 'start'
             subcommand of 'ipcluster'. If your cluster directory is in 
             the cwd or the ipython directory, you can simply refer to it
-            using its profile name, 'ipclusterz engines -n 4 -p <profile>`,
+            using its profile name, 'ipcluster engines -n 4 -p <profile>`,
             otherwise use the '--cluster-dir' option.
             """
         )
         paa = parser_engines.add_argument
         paa('-n', '--number',
             type=int, dest='Global.n',
             help='The number of engines to start.',
             metavar='Global.n')
         paa('--daemon',
             dest='Global.daemonize', action='store_true',
             help='Daemonize the ipcluster program. This implies --log-to-file')
         paa('--no-daemon',
             dest='Global.daemonize', action='store_false',
             help="Dont't daemonize the ipcluster program.")
 
 #-----------------------------------------------------------------------------
 # Main application
 #-----------------------------------------------------------------------------
 
 
 class IPClusterApp(ApplicationWithClusterDir):
 
-    name = u'ipclusterz'
+    name = u'ipcluster'
     description = _description
     usage = None
     command_line_loader = IPClusterAppConfigLoader
     default_config_file_name = default_config_file_name
     default_log_level = logging.INFO
     auto_create_cluster_dir = False
 
     def create_default_config(self):
         super(IPClusterApp, self).create_default_config()
         self.default_config.Global.controller_launcher = \
             'IPython.parallel.launcher.LocalControllerLauncher'
         self.default_config.Global.engine_launcher = \
             'IPython.parallel.launcher.LocalEngineSetLauncher'
         self.default_config.Global.n = 2
         self.default_config.Global.delay = 2
         self.default_config.Global.reset_config = False
         self.default_config.Global.clean_logs = True
         self.default_config.Global.signal = signal.SIGINT
         self.default_config.Global.daemonize = False
 
     def find_resources(self):
         subcommand = self.command_line_config.Global.subcommand
         if subcommand=='list':
             self.list_cluster_dirs()
             # Exit immediately because there is nothing left to do.
             self.exit()
         elif subcommand=='create':
             self.auto_create_cluster_dir = True
             super(IPClusterApp, self).find_resources()
         elif subcommand=='start' or subcommand=='stop':
             self.auto_create_cluster_dir = True
             try:
                 super(IPClusterApp, self).find_resources()
             except ClusterDirError:
                 raise ClusterDirError(
                     "Could not find a cluster directory. A cluster dir must "
-                    "be created before running 'ipclusterz start'.  Do "
-                    "'ipclusterz create -h' or 'ipclusterz list -h' for more "
+                    "be created before running 'ipcluster start'.  Do "
+                    "'ipcluster create -h' or 'ipcluster list -h' for more "
                     "information about creating and listing cluster dirs."
                 )
         elif subcommand=='engines':
             self.auto_create_cluster_dir = False
             try:
                 super(IPClusterApp, self).find_resources()
             except ClusterDirError:
                 raise ClusterDirError(
                     "Could not find a cluster directory. A cluster dir must "
-                    "be created before running 'ipclusterz start'.  Do "
-                    "'ipclusterz create -h' or 'ipclusterz list -h' for more "
+                    "be created before running 'ipcluster start'.  Do "
+                    "'ipcluster create -h' or 'ipcluster list -h' for more "
                     "information about creating and listing cluster dirs."
                 )
 
     def list_cluster_dirs(self):
         # Find the search paths
         cluster_dir_paths = os.environ.get('IPCLUSTER_DIR_PATH','')
         if cluster_dir_paths:
             cluster_dir_paths = cluster_dir_paths.split(':')
         else:
             cluster_dir_paths = []
         try:
             ipython_dir = self.command_line_config.Global.ipython_dir
         except AttributeError:
             ipython_dir = self.default_config.Global.ipython_dir
         paths = [os.getcwd(), ipython_dir] + \
             cluster_dir_paths
         paths = list(set(paths))
 
         self.log.info('Searching for cluster dirs in paths: %r' % paths)
         for path in paths:
             files = os.listdir(path)
             for f in files:
                 full_path = os.path.join(path, f)
-                if os.path.isdir(full_path) and f.startswith('clusterz_'):
+                if os.path.isdir(full_path) and f.startswith('cluster_'):
                     profile = full_path.split('_')[-1]
-                    start_cmd = 'ipclusterz start -p %s -n 4' % profile
+                    start_cmd = 'ipcluster start -p %s -n 4' % profile
                     print start_cmd + " ==> " + full_path
 
     def pre_construct(self):
         # IPClusterApp.pre_construct() is where we cd to the working directory.
         super(IPClusterApp, self).pre_construct()
         config = self.master_config
         try:
             daemon = config.Global.daemonize
             if daemon:
                 config.Global.log_to_file = True
         except AttributeError:
             pass
 
     def construct(self):
         config = self.master_config
         subcmd = config.Global.subcommand
         reset = config.Global.reset_config
         if subcmd == 'list':
             return
         if subcmd == 'create':
             self.log.info('Copying default config files to cluster directory '
             '[overwrite=%r]' % (reset,))
             self.cluster_dir_obj.copy_all_config_files(overwrite=reset)
         if subcmd =='start':
             self.cluster_dir_obj.copy_all_config_files(overwrite=False)
             self.start_logging()
             self.loop = ioloop.IOLoop.instance()
             # reactor.callWhenRunning(self.start_launchers)
             dc = ioloop.DelayedCallback(self.start_launchers, 0, self.loop)
             dc.start()
         if subcmd == 'engines':
             self.start_logging()
             self.loop = ioloop.IOLoop.instance()
             # reactor.callWhenRunning(self.start_launchers)
             engine_only = lambda : self.start_launchers(controller=False)
             dc = ioloop.DelayedCallback(engine_only, 0, self.loop)
             dc.start()
 
     def start_launchers(self, controller=True):
         config = self.master_config
 
         # Create the launchers. In both bases, we set the work_dir of
         # the launcher to the cluster_dir. This is where the launcher's
         # subprocesses will be launched. It is not where the controller
         # and engine will be launched.
         if controller:
             cl_class = import_item(config.Global.controller_launcher)
             self.controller_launcher = cl_class(
                 work_dir=self.cluster_dir, config=config,
                 logname=self.log.name
             )
             # Setup the observing of stopping. If the controller dies, shut
             # everything down as that will be completely fatal for the engines.
             self.controller_launcher.on_stop(self.stop_launchers)
             # But, we don't monitor the stopping of engines. An engine dying
             # is just fine and in principle a user could start a new engine.
             # Also, if we did monitor engine stopping, it is difficult to
             # know what to do when only some engines die. Currently, the
             # observing of engine stopping is inconsistent. Some launchers
             # might trigger on a single engine stopping, other wait until
             # all stop.  TODO: think more about how to handle this.
         else:
             self.controller_launcher = None
         
         el_class = import_item(config.Global.engine_launcher)
         self.engine_launcher = el_class(
             work_dir=self.cluster_dir, config=config, logname=self.log.name
         )
 
         # Setup signals
         signal.signal(signal.SIGINT, self.sigint_handler)
 
         # Start the controller and engines
         self._stopping = False  # Make sure stop_launchers is not called 2x.
         if controller:
             self.start_controller()
         dc = ioloop.DelayedCallback(self.start_engines, 1000*config.Global.delay*controller, self.loop)
         dc.start()
         self.startup_message()
 
     def startup_message(self, r=None):
         self.log.info("IPython cluster: started")
         return r
         
     def start_controller(self, r=None):
         # self.log.info("In start_controller")
         config = self.master_config
         d = self.controller_launcher.start(
             cluster_dir=config.Global.cluster_dir
         )
         return d
             
     def start_engines(self, r=None):
         # self.log.info("In start_engines")
         config = self.master_config
         
         d = self.engine_launcher.start(
             config.Global.n,
             cluster_dir=config.Global.cluster_dir
         )
         return d
 
     def stop_controller(self, r=None):
         # self.log.info("In stop_controller")
         if self.controller_launcher and self.controller_launcher.running:
             return self.controller_launcher.stop()
 
     def stop_engines(self, r=None):
         # self.log.info("In stop_engines")
         if self.engine_launcher.running:
             d = self.engine_launcher.stop()
             # d.addErrback(self.log_err)
             return d
         else:
             return None
 
     def log_err(self, f):
         self.log.error(f.getTraceback())
         return None
         
     def stop_launchers(self, r=None):
         if not self._stopping:
             self._stopping = True
             # if isinstance(r, failure.Failure):
             #     self.log.error('Unexpected error in ipcluster:')
             #     self.log.info(r.getTraceback())
             self.log.error("IPython cluster: stopping")
             # These return deferreds. We are not doing anything with them
             # but we are holding refs to them as a reminder that they 
             # do return deferreds.
             d1 = self.stop_engines()
             d2 = self.stop_controller()
             # Wait a few seconds to let things shut down.
             dc = ioloop.DelayedCallback(self.loop.stop, 4000, self.loop)
             dc.start()
             # reactor.callLater(4.0, reactor.stop)
 
     def sigint_handler(self, signum, frame):
         self.stop_launchers()
         
     def start_logging(self):
         # Remove old log files of the controller and engine
         if self.master_config.Global.clean_logs:
             log_dir = self.master_config.Global.log_dir
             for f in os.listdir(log_dir):
                 if re.match(r'ip(engine|controller)z-\d+\.(log|err|out)',f):
                     os.remove(os.path.join(log_dir, f))
         # This will remove old log files for ipcluster itself
         super(IPClusterApp, self).start_logging()
 
     def start_app(self):
         """Start the application, depending on what subcommand is used."""
         subcmd = self.master_config.Global.subcommand
         if subcmd=='create' or subcmd=='list':
             return
         elif subcmd=='start':
             self.start_app_start()
         elif subcmd=='stop':
             self.start_app_stop()
         elif subcmd=='engines':
             self.start_app_engines()
 
     def start_app_start(self):
         """Start the app for the start subcommand."""
         config = self.master_config
         # First see if the cluster is already running
         try:
             pid = self.get_pid_from_file()
         except PIDFileError:
             pass
         else:
             self.log.critical(
                 'Cluster is already running with [pid=%s]. '
-                'use "ipclusterz stop" to stop the cluster.' % pid
+                'use "ipcluster stop" to stop the cluster.' % pid
             )
             # Here I exit with a unusual exit status that other processes
             # can watch for to learn how I existed.
             self.exit(ALREADY_STARTED)
 
         # Now log and daemonize
         self.log.info(
-            'Starting ipclusterz with [daemon=%r]' % config.Global.daemonize
+            'Starting ipcluster with [daemon=%r]' % config.Global.daemonize
         )
         # TODO: Get daemonize working on Windows or as a Windows Server.
         if config.Global.daemonize:
             if os.name=='posix':
                 from twisted.scripts._twistd_unix import daemonize
                 daemonize()
 
         # Now write the new pid file AFTER our new forked pid is active.
         self.write_pid_file()
         try:
             self.loop.start()
         except KeyboardInterrupt:
             pass
         except zmq.ZMQError as e:
             if e.errno == errno.EINTR:
                 pass
             else:
                 raise
         self.remove_pid_file()
 
     def start_app_engines(self):
         """Start the app for the start subcommand."""
         config = self.master_config
         # First see if the cluster is already running
         
         # Now log and daemonize
         self.log.info(
             'Starting engines with [daemon=%r]' % config.Global.daemonize
         )
         # TODO: Get daemonize working on Windows or as a Windows Server.
         if config.Global.daemonize:
             if os.name=='posix':
                 from twisted.scripts._twistd_unix import daemonize
                 daemonize()
 
         # Now write the new pid file AFTER our new forked pid is active.
         # self.write_pid_file()
         try:
             self.loop.start()
         except KeyboardInterrupt:
             pass
         except zmq.ZMQError as e:
             if e.errno == errno.EINTR:
                 pass
             else:
                 raise
         # self.remove_pid_file()
     
     def start_app_stop(self):
         """Start the app for the stop subcommand."""
         config = self.master_config
         try:
             pid = self.get_pid_from_file()
         except PIDFileError:
             self.log.critical(
                 'Problem reading pid file, cluster is probably not running.'
             )
             # Here I exit with a unusual exit status that other processes
             # can watch for to learn how I existed.
             self.exit(ALREADY_STOPPED)
         else:
             if os.name=='posix':
                 sig = config.Global.signal
                 self.log.info(
                     "Stopping cluster [pid=%r] with [signal=%r]" % (pid, sig)
                 )
                 os.kill(pid, sig)
             elif os.name=='nt':
                 # As of right now, we don't support daemonize on Windows, so
                 # stop will not do anything. Minimally, it should clean up the
                 # old .pid files.
                 self.remove_pid_file()
 
 
 def launch_new_instance():
     """Create and run the IPython cluster."""
     app = IPClusterApp()
     app.start()
 
 
 if __name__ == '__main__':
     launch_new_instance()
 

IPython/parallel/ipcontrollerapp.py

@@ -1,431 +1,431 b''
 #!/usr/bin/env python
 # encoding: utf-8
 """
 The IPython controller application.
 """
 
 #-----------------------------------------------------------------------------
 #  Copyright (C) 2008-2009  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-----------------------------------------------------------------------------
 
 #-----------------------------------------------------------------------------
 # Imports
 #-----------------------------------------------------------------------------
 
 from __future__ import with_statement
 
 import copy
 import os
 import logging
 import socket
 import stat
 import sys
 import uuid
 
 import zmq
 from zmq.log.handlers import PUBHandler
 from zmq.utils import jsonapi as json
 
 from IPython.config.loader import Config
 from IPython.parallel import factory
 from IPython.parallel.controller import ControllerFactory
 from IPython.parallel.clusterdir import (
     ApplicationWithClusterDir,
     ClusterDirConfigLoader
 )
 from IPython.parallel.util import disambiguate_ip_address, split_url
 # from IPython.kernel.fcutil import FCServiceFactory, FURLError
 from IPython.utils.traitlets import Instance, Unicode
 
 
 
 #-----------------------------------------------------------------------------
 # Module level variables
 #-----------------------------------------------------------------------------
 
 
 #: The default config file name for this application
-default_config_file_name = u'ipcontrollerz_config.py'
+default_config_file_name = u'ipcontroller_config.py'
 
 
 _description = """Start the IPython controller for parallel computing.
 
 The IPython controller provides a gateway between the IPython engines and
 clients. The controller needs to be started before the engines and can be
 configured using command line options or using a cluster directory. Cluster
 directories contain config, log and security files and are usually located in
-your ipython directory and named as "clusterz_<profile>". See the --profile
+your ipython directory and named as "cluster_<profile>". See the --profile
 and --cluster-dir options for details.
 """
 
 #-----------------------------------------------------------------------------
 # Default interfaces
 #-----------------------------------------------------------------------------
 
 # The default client interfaces for FCClientServiceFactory.interfaces
 default_client_interfaces = Config()
 default_client_interfaces.Default.url_file = 'ipcontroller-client.url'
 
 # Make this a dict we can pass to Config.__init__ for the default
 default_client_interfaces = dict(copy.deepcopy(default_client_interfaces.items()))
 
 
 
 # The default engine interfaces for FCEngineServiceFactory.interfaces
 default_engine_interfaces = Config()
 default_engine_interfaces.Default.url_file = u'ipcontroller-engine.url'
 
 # Make this a dict we can pass to Config.__init__ for the default
 default_engine_interfaces = dict(copy.deepcopy(default_engine_interfaces.items()))
 
 
 #-----------------------------------------------------------------------------
 # Service factories
 #-----------------------------------------------------------------------------
 
 # 
 # class FCClientServiceFactory(FCServiceFactory):
 #     """A Foolscap implementation of the client services."""
 # 
 #     cert_file = Unicode(u'ipcontroller-client.pem', config=True)
 #     interfaces = Instance(klass=Config, kw=default_client_interfaces,
 #                           allow_none=False, config=True)
 # 
 # 
 # class FCEngineServiceFactory(FCServiceFactory):
 #     """A Foolscap implementation of the engine services."""
 # 
 #     cert_file = Unicode(u'ipcontroller-engine.pem', config=True)
 #     interfaces = Instance(klass=dict, kw=default_engine_interfaces,
 #                           allow_none=False, config=True)
 # 
 
 #-----------------------------------------------------------------------------
 # Command line options
 #-----------------------------------------------------------------------------
 
 
 class IPControllerAppConfigLoader(ClusterDirConfigLoader):
 
     def _add_arguments(self):
         super(IPControllerAppConfigLoader, self)._add_arguments()
         paa = self.parser.add_argument
         
         ## Hub Config:
         paa('--mongodb', 
             dest='HubFactory.db_class', action='store_const',
             const='IPython.parallel.mongodb.MongoDB', 
             help='Use MongoDB for task storage [default: in-memory]')
         paa('--sqlite', 
             dest='HubFactory.db_class', action='store_const',
             const='IPython.parallel.sqlitedb.SQLiteDB', 
             help='Use SQLite3 for DB task storage [default: in-memory]')
         paa('--hb',
             type=int, dest='HubFactory.hb', nargs=2,
             help='The (2) ports the Hub\'s Heartmonitor will use for the heartbeat '
             'connections [default: random]',
             metavar='Hub.hb_ports')
         paa('--ping',
             type=int, dest='HubFactory.ping',
             help='The frequency at which the Hub pings the engines for heartbeats '
             ' (in ms) [default: 100]',
             metavar='Hub.ping')
         
         # Client config
         paa('--client-ip',
             type=str, dest='HubFactory.client_ip', 
             help='The IP address or hostname the Hub will listen on for '
             'client connections. Both engine-ip and client-ip can be set simultaneously '
             'via --ip [default: loopback]',
             metavar='Hub.client_ip')
         paa('--client-transport',
             type=str, dest='HubFactory.client_transport', 
             help='The ZeroMQ transport the Hub will use for '
             'client connections. Both engine-transport and client-transport can be set simultaneously '
             'via --transport [default: tcp]',
             metavar='Hub.client_transport')
         paa('--query',
             type=int, dest='HubFactory.query_port', 
             help='The port on which the Hub XREP socket will listen for result queries from clients [default: random]',
             metavar='Hub.query_port')
         paa('--notifier',
             type=int, dest='HubFactory.notifier_port', 
             help='The port on which the Hub PUB socket will listen for notification connections [default: random]',
             metavar='Hub.notifier_port')
         
         # Engine config
         paa('--engine-ip',
             type=str, dest='HubFactory.engine_ip', 
             help='The IP address or hostname the Hub will listen on for '
             'engine connections. This applies to the Hub and its schedulers'
             'engine-ip and client-ip can be set simultaneously '
             'via --ip [default: loopback]',
             metavar='Hub.engine_ip')
         paa('--engine-transport',
             type=str, dest='HubFactory.engine_transport', 
             help='The ZeroMQ transport the Hub will use for '
             'client connections. Both engine-transport and client-transport can be set simultaneously '
             'via --transport [default: tcp]',
             metavar='Hub.engine_transport')
         
         # Scheduler config
         paa('--mux',
             type=int, dest='ControllerFactory.mux', nargs=2,
             help='The (2) ports the MUX scheduler will listen on for client,engine '
             'connections, respectively [default: random]',
             metavar='Scheduler.mux_ports')
         paa('--task',
             type=int, dest='ControllerFactory.task', nargs=2,
             help='The (2) ports the Task scheduler will listen on for client,engine '
             'connections, respectively [default: random]',
             metavar='Scheduler.task_ports')
         paa('--control',
             type=int, dest='ControllerFactory.control', nargs=2,
             help='The (2) ports the Control scheduler will listen on for client,engine '
             'connections, respectively [default: random]',
             metavar='Scheduler.control_ports')
         paa('--iopub',
             type=int, dest='ControllerFactory.iopub', nargs=2,
             help='The (2) ports the IOPub scheduler will listen on for client,engine '
             'connections, respectively [default: random]',
             metavar='Scheduler.iopub_ports')
             
         paa('--scheme',
             type=str, dest='HubFactory.scheme',
             choices = ['pure', 'lru', 'plainrandom', 'weighted', 'twobin','leastload'],
             help='select the task scheduler scheme  [default: Python LRU]',
             metavar='Scheduler.scheme')
         paa('--usethreads',
             dest='ControllerFactory.usethreads', action="store_true",
             help='Use threads instead of processes for the schedulers',
             )
         paa('--hwm',
             dest='ControllerFactory.hwm', type=int,
             help='specify the High Water Mark (HWM) for the downstream '
             'socket in the pure ZMQ scheduler. This is the maximum number '
             'of allowed outstanding tasks on each engine.',
             )
         
         ## Global config
         paa('--log-to-file',
             action='store_true', dest='Global.log_to_file',
             help='Log to a file in the log directory (default is stdout)')
         paa('--log-url',
             type=str, dest='Global.log_url',
             help='Broadcast logs to an iploggerz process [default: disabled]')
         paa('-r','--reuse-files', 
             action='store_true', dest='Global.reuse_files',
             help='Try to reuse existing json connection files.')
         paa('--no-secure',
             action='store_false', dest='Global.secure',
             help='Turn off execution keys (default).')
         paa('--secure',
             action='store_true', dest='Global.secure',
             help='Turn on execution keys.')
         paa('--execkey',
             type=str, dest='Global.exec_key',
             help='path to a file containing an execution key.',
             metavar='keyfile')
         paa('--ssh',
             type=str, dest='Global.sshserver',
             help='ssh url for clients to use when connecting to the Controller '
             'processes. It should be of the form: [user@]server[:port]. The '
             'Controller\'s listening addresses must be accessible from the ssh server',
             metavar='Global.sshserver')
         paa('--location',
             type=str, dest='Global.location',
             help="The external IP or domain name of this machine, used for disambiguating "
             "engine and client connections.",
             metavar='Global.location')
         factory.add_session_arguments(self.parser)
         factory.add_registration_arguments(self.parser)
 
 
 #-----------------------------------------------------------------------------
 # The main application
 #-----------------------------------------------------------------------------
 
 
 class IPControllerApp(ApplicationWithClusterDir):
 
-    name = u'ipcontrollerz'
+    name = u'ipcontroller'
     description = _description
     command_line_loader = IPControllerAppConfigLoader
     default_config_file_name = default_config_file_name
     auto_create_cluster_dir = True
     
 
     def create_default_config(self):
         super(IPControllerApp, self).create_default_config()
         # Don't set defaults for Global.secure or Global.reuse_furls
         # as those are set in a component.
         self.default_config.Global.import_statements = []
         self.default_config.Global.clean_logs = True
         self.default_config.Global.secure = True
         self.default_config.Global.reuse_files = False
         self.default_config.Global.exec_key = "exec_key.key"
         self.default_config.Global.sshserver = None
         self.default_config.Global.location = None
 
     def pre_construct(self):
         super(IPControllerApp, self).pre_construct()
         c = self.master_config
         # The defaults for these are set in FCClientServiceFactory and
         # FCEngineServiceFactory, so we only set them here if the global
         # options have be set to override the class level defaults.
         
         # if hasattr(c.Global, 'reuse_furls'):
         #     c.FCClientServiceFactory.reuse_furls = c.Global.reuse_furls
         #     c.FCEngineServiceFactory.reuse_furls = c.Global.reuse_furls
         #     del c.Global.reuse_furls
         # if hasattr(c.Global, 'secure'):
         #     c.FCClientServiceFactory.secure = c.Global.secure
         #     c.FCEngineServiceFactory.secure = c.Global.secure
         #     del c.Global.secure
     
     def save_connection_dict(self, fname, cdict):
         """save a connection dict to json file."""
         c = self.master_config
         url = cdict['url']
         location = cdict['location']
         if not location:
             try:
                 proto,ip,port = split_url(url)
             except AssertionError:
                 pass
             else:
                 location = socket.gethostbyname_ex(socket.gethostname())[2][-1]
             cdict['location'] = location
         fname = os.path.join(c.Global.security_dir, fname)
         with open(fname, 'w') as f:
             f.write(json.dumps(cdict, indent=2))
         os.chmod(fname, stat.S_IRUSR|stat.S_IWUSR)
     
     def load_config_from_json(self):
         """load config from existing json connector files."""
         c = self.master_config
         # load from engine config
         with open(os.path.join(c.Global.security_dir, 'ipcontroller-engine.json')) as f:
             cfg = json.loads(f.read())
         key = c.SessionFactory.exec_key = cfg['exec_key']
         xport,addr = cfg['url'].split('://')
         c.HubFactory.engine_transport = xport
         ip,ports = addr.split(':')
         c.HubFactory.engine_ip = ip
         c.HubFactory.regport = int(ports)
         c.Global.location = cfg['location']
         
         # load client config
         with open(os.path.join(c.Global.security_dir, 'ipcontroller-client.json')) as f:
             cfg = json.loads(f.read())
         assert key == cfg['exec_key'], "exec_key mismatch between engine and client keys"
         xport,addr = cfg['url'].split('://')
         c.HubFactory.client_transport = xport
         ip,ports = addr.split(':')
         c.HubFactory.client_ip = ip
         c.Global.sshserver = cfg['ssh']
         assert int(ports) == c.HubFactory.regport, "regport mismatch"
     
     def construct(self):
         # This is the working dir by now.
         sys.path.insert(0, '')
         c = self.master_config
         
         self.import_statements()
         reusing = c.Global.reuse_files
         if reusing:
             try:
                 self.load_config_from_json()
             except (AssertionError,IOError):
                 reusing=False
         # check again, because reusing may have failed:
         if reusing:
             pass
         elif c.Global.secure:
             keyfile = os.path.join(c.Global.security_dir, c.Global.exec_key)
             key = str(uuid.uuid4())
             with open(keyfile, 'w') as f:
                 f.write(key)
             os.chmod(keyfile, stat.S_IRUSR|stat.S_IWUSR)
             c.SessionFactory.exec_key = key
         else:
             c.SessionFactory.exec_key = ''
             key = None
         
         try:
             self.factory = ControllerFactory(config=c, logname=self.log.name)
             self.start_logging()
             self.factory.construct()
         except:
             self.log.error("Couldn't construct the Controller", exc_info=True)
             self.exit(1)
         
         if not reusing:
             # save to new json config files
             f = self.factory
             cdict = {'exec_key' : key,
                     'ssh' : c.Global.sshserver,
                     'url' : "%s://%s:%s"%(f.client_transport, f.client_ip, f.regport),
                     'location' : c.Global.location
                     }
             self.save_connection_dict('ipcontroller-client.json', cdict)
             edict = cdict
             edict['url']="%s://%s:%s"%((f.client_transport, f.client_ip, f.regport))
             self.save_connection_dict('ipcontroller-engine.json', edict)
         
     
     def save_urls(self):
         """save the registration urls to files."""
         c = self.master_config
         
         sec_dir = c.Global.security_dir
         cf = self.factory
         
         with open(os.path.join(sec_dir, 'ipcontroller-engine.url'), 'w') as f:
             f.write("%s://%s:%s"%(cf.engine_transport, cf.engine_ip, cf.regport))
         
         with open(os.path.join(sec_dir, 'ipcontroller-client.url'), 'w') as f:
             f.write("%s://%s:%s"%(cf.client_transport, cf.client_ip, cf.regport))
         
     
     def import_statements(self):
         statements = self.master_config.Global.import_statements
         for s in statements:
             try:
                 self.log.msg("Executing statement: '%s'" % s)
                 exec s in globals(), locals()
             except:
                 self.log.msg("Error running statement: %s" % s)
 
     def start_logging(self):
         super(IPControllerApp, self).start_logging()
         if self.master_config.Global.log_url:
             context = self.factory.context
             lsock = context.socket(zmq.PUB)
             lsock.connect(self.master_config.Global.log_url)
             handler = PUBHandler(lsock)
             handler.root_topic = 'controller'
             handler.setLevel(self.log_level)
             self.log.addHandler(handler)
     # 
     def start_app(self):
         # Start the subprocesses:
         self.factory.start()
         self.write_pid_file(overwrite=True)
         try:
             self.factory.loop.start()
         except KeyboardInterrupt:
             self.log.critical("Interrupted, Exiting...\n")
 
 
 def launch_new_instance():
     """Create and run the IPython controller"""
     app = IPControllerApp()
     app.start()
 
 
 if __name__ == '__main__':
     launch_new_instance()

IPython/parallel/ipengineapp.py

@@ -1,294 +1,294 b''
 #!/usr/bin/env python
 # encoding: utf-8
 """
 The IPython engine application
 """
 
 #-----------------------------------------------------------------------------
 #  Copyright (C) 2008-2009  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-----------------------------------------------------------------------------
 
 #-----------------------------------------------------------------------------
 # Imports
 #-----------------------------------------------------------------------------
 
 import json
 import os
 import sys
 
 import zmq
 from zmq.eventloop import ioloop
 
 from IPython.parallel.clusterdir import (
     ApplicationWithClusterDir,
     ClusterDirConfigLoader
 )
 from IPython.zmq.log import EnginePUBHandler
 
 from IPython.parallel import factory
 from IPython.parallel.engine import EngineFactory
 from IPython.parallel.streamkernel import Kernel
 from IPython.parallel.util import disambiguate_url
 from IPython.utils.importstring import import_item
 
 
 #-----------------------------------------------------------------------------
 # Module level variables
 #-----------------------------------------------------------------------------
 
 #: The default config file name for this application
-default_config_file_name = u'ipenginez_config.py'
+default_config_file_name = u'ipengine_config.py'
 
 
 mpi4py_init = """from mpi4py import MPI as mpi
 mpi.size = mpi.COMM_WORLD.Get_size()
 mpi.rank = mpi.COMM_WORLD.Get_rank()
 """
 
 
 pytrilinos_init = """from PyTrilinos import Epetra
 class SimpleStruct:
 pass
 mpi = SimpleStruct()
 mpi.rank = 0
 mpi.size = 0
 """
 
 
 _description = """Start an IPython engine for parallel computing.\n\n
 
 IPython engines run in parallel and perform computations on behalf of a client
 and controller. A controller needs to be started before the engines. The
 engine can be configured using command line options or using a cluster
 directory. Cluster directories contain config, log and security files and are
-usually located in your ipython directory and named as "clusterz_<profile>".
+usually located in your ipython directory and named as "cluster_<profile>".
 See the --profile and --cluster-dir options for details.
 """
 
 #-----------------------------------------------------------------------------
 # Command line options
 #-----------------------------------------------------------------------------
 
 
 class IPEngineAppConfigLoader(ClusterDirConfigLoader):
 
     def _add_arguments(self):
         super(IPEngineAppConfigLoader, self)._add_arguments()
         paa = self.parser.add_argument
         # Controller config
         paa('--file', '-f',
             type=unicode, dest='Global.url_file',
             help='The full location of the file containing the connection information fo '
             'controller. If this is not given, the file must be in the '
             'security directory of the cluster directory.  This location is '
             'resolved using the --profile and --app-dir options.',
             metavar='Global.url_file')
         # MPI
         paa('--mpi',
             type=str, dest='MPI.use',
             help='How to enable MPI (mpi4py, pytrilinos, or empty string to disable).',
             metavar='MPI.use')
         # Global config
         paa('--log-to-file',
             action='store_true', dest='Global.log_to_file',
             help='Log to a file in the log directory (default is stdout)')
         paa('--log-url',
             dest='Global.log_url',
             help="url of ZMQ logger, as started with iploggerz")
         # paa('--execkey',
         #     type=str, dest='Global.exec_key',
         #     help='path to a file containing an execution key.',
         #     metavar='keyfile')
         # paa('--no-secure',
         #     action='store_false', dest='Global.secure',
         #     help='Turn off execution keys.')
         # paa('--secure',
         #     action='store_true', dest='Global.secure',
         #     help='Turn on execution keys (default).')
         # init command
         paa('-c',
             type=str, dest='Global.extra_exec_lines',
             help='specify a command to be run at startup')
         
         factory.add_session_arguments(self.parser)
         factory.add_registration_arguments(self.parser)
 
 
 #-----------------------------------------------------------------------------
 # Main application
 #-----------------------------------------------------------------------------
 
 
 class IPEngineApp(ApplicationWithClusterDir):
 
-    name = u'ipenginez'
+    name = u'ipengine'
     description = _description
     command_line_loader = IPEngineAppConfigLoader
     default_config_file_name = default_config_file_name
     auto_create_cluster_dir = True
 
     def create_default_config(self):
         super(IPEngineApp, self).create_default_config()
 
         # The engine should not clean logs as we don't want to remove the
         # active log files of other running engines.
         self.default_config.Global.clean_logs = False
         self.default_config.Global.secure = True
 
         # Global config attributes
         self.default_config.Global.exec_lines = []
         self.default_config.Global.extra_exec_lines = ''
 
         # Configuration related to the controller
         # This must match the filename (path not included) that the controller
         # used for the FURL file.
         self.default_config.Global.url_file = u''
         self.default_config.Global.url_file_name = u'ipcontroller-engine.json'
         # If given, this is the actual location of the controller's FURL file.
         # If not, this is computed using the profile, app_dir and furl_file_name
         # self.default_config.Global.key_file_name = u'exec_key.key'
         # self.default_config.Global.key_file = u''
 
         # MPI related config attributes
         self.default_config.MPI.use = ''
         self.default_config.MPI.mpi4py = mpi4py_init
         self.default_config.MPI.pytrilinos = pytrilinos_init
 
     def post_load_command_line_config(self):
         pass
 
     def pre_construct(self):
         super(IPEngineApp, self).pre_construct()
         # self.find_cont_url_file()
         self.find_url_file()
         if self.master_config.Global.extra_exec_lines:
             self.master_config.Global.exec_lines.append(self.master_config.Global.extra_exec_lines)
 
     # def find_key_file(self):
     #     """Set the key file.
     # 
     #     Here we don't try to actually see if it exists for is valid as that
     #     is hadled by the connection logic.
     #     """
     #     config = self.master_config
     #     # Find the actual controller key file
     #     if not config.Global.key_file:
     #         try_this = os.path.join(
     #             config.Global.cluster_dir, 
     #             config.Global.security_dir,
     #             config.Global.key_file_name
     #         )
     #         config.Global.key_file = try_this
         
     def find_url_file(self):
         """Set the key file.
 
         Here we don't try to actually see if it exists for is valid as that
         is hadled by the connection logic.
         """
         config = self.master_config
         # Find the actual controller key file
         if not config.Global.url_file:
             try_this = os.path.join(
                 config.Global.cluster_dir, 
                 config.Global.security_dir,
                 config.Global.url_file_name
             )
             config.Global.url_file = try_this
         
     def construct(self):
         # This is the working dir by now.
         sys.path.insert(0, '')
         config = self.master_config
         # if os.path.exists(config.Global.key_file) and config.Global.secure:
         #     config.SessionFactory.exec_key = config.Global.key_file
         if os.path.exists(config.Global.url_file):
             with open(config.Global.url_file) as f:
                 d = json.loads(f.read())
                 for k,v in d.iteritems():
                     if isinstance(v, unicode):
                         d[k] = v.encode()
             if d['exec_key']:
                 config.SessionFactory.exec_key = d['exec_key']
             d['url'] = disambiguate_url(d['url'], d['location'])
             config.RegistrationFactory.url=d['url']
             config.EngineFactory.location = d['location']
         
         
         
         config.Kernel.exec_lines = config.Global.exec_lines
 
         self.start_mpi()
 
         # Create the underlying shell class and EngineService
         # shell_class = import_item(self.master_config.Global.shell_class)
         try:
             self.engine = EngineFactory(config=config, logname=self.log.name)
         except:
             self.log.error("Couldn't start the Engine", exc_info=True)
             self.exit(1)
         
         self.start_logging()
 
         # Create the service hierarchy
         # self.main_service = service.MultiService()
         # self.engine_service.setServiceParent(self.main_service)
         # self.tub_service = Tub()
         # self.tub_service.setServiceParent(self.main_service)
         # # This needs to be called before the connection is initiated
         # self.main_service.startService()
 
         # This initiates the connection to the controller and calls
         # register_engine to tell the controller we are ready to do work
         # self.engine_connector = EngineConnector(self.tub_service)
 
         # self.log.info("Using furl file: %s" % self.master_config.Global.furl_file)
 
         # reactor.callWhenRunning(self.call_connect)
 
 
     def start_logging(self):
         super(IPEngineApp, self).start_logging()
         if self.master_config.Global.log_url:
             context = self.engine.context
             lsock = context.socket(zmq.PUB)
             lsock.connect(self.master_config.Global.log_url)
             handler = EnginePUBHandler(self.engine, lsock)
             handler.setLevel(self.log_level)
             self.log.addHandler(handler)
     
     def start_mpi(self):
         global mpi
         mpikey = self.master_config.MPI.use
         mpi_import_statement = self.master_config.MPI.get(mpikey, None)
         if mpi_import_statement is not None:
             try:
                 self.log.info("Initializing MPI:")
                 self.log.info(mpi_import_statement)
                 exec mpi_import_statement in globals()
             except:
                 mpi = None
         else:
             mpi = None
 
 
     def start_app(self):
         self.engine.start()
         try:
             self.engine.loop.start()
         except KeyboardInterrupt:
             self.log.critical("Engine Interrupted, shutting down...\n")
 
 
 def launch_new_instance():
     """Create and run the IPython controller"""
     app = IPEngineApp()
     app.start()
 
 
 if __name__ == '__main__':
     launch_new_instance()
 

IPython/parallel/iploggerapp.py

@@ -1,132 +1,132 b''
 #!/usr/bin/env python
 # encoding: utf-8
 """
 A simple IPython logger application
 """
 
 #-----------------------------------------------------------------------------
 #  Copyright (C) 2011  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-----------------------------------------------------------------------------
 
 #-----------------------------------------------------------------------------
 # Imports
 #-----------------------------------------------------------------------------
 
 import os
 import sys
 
 import zmq
 
 from IPython.parallel.clusterdir import (
     ApplicationWithClusterDir,
     ClusterDirConfigLoader
 )
 from .logwatcher import LogWatcher
 
 #-----------------------------------------------------------------------------
 # Module level variables
 #-----------------------------------------------------------------------------
 
 #: The default config file name for this application
 default_config_file_name = u'iplogger_config.py'
 
 _description = """Start an IPython logger for parallel computing.\n\n
 
 IPython controllers and engines (and your own processes) can broadcast log messages
 by registering a `zmq.log.handlers.PUBHandler` with the `logging` module. The
 logger can be configured using command line options or using a cluster
 directory. Cluster directories contain config, log and security files and are
-usually located in your ipython directory and named as "clusterz_<profile>".
+usually located in your ipython directory and named as "cluster_<profile>".
 See the --profile and --cluster-dir options for details.
 """
 
 #-----------------------------------------------------------------------------
 # Command line options
 #-----------------------------------------------------------------------------
 
 
 class IPLoggerAppConfigLoader(ClusterDirConfigLoader):
 
     def _add_arguments(self):
         super(IPLoggerAppConfigLoader, self)._add_arguments()
         paa = self.parser.add_argument
         # Controller config
         paa('--url',
             type=str, dest='LogWatcher.url',
             help='The url the LogWatcher will listen on',
             )
         # MPI
         paa('--topics',
             type=str, dest='LogWatcher.topics', nargs='+',
             help='What topics to subscribe to',
             metavar='topics')
         # Global config
         paa('--log-to-file',
             action='store_true', dest='Global.log_to_file',
             help='Log to a file in the log directory (default is stdout)')
         
 
 #-----------------------------------------------------------------------------
 # Main application
 #-----------------------------------------------------------------------------
 
 
 class IPLoggerApp(ApplicationWithClusterDir):
 
     name = u'iploggerz'
     description = _description
     command_line_loader = IPLoggerAppConfigLoader
     default_config_file_name = default_config_file_name
     auto_create_cluster_dir = True
 
     def create_default_config(self):
         super(IPLoggerApp, self).create_default_config()
 
         # The engine should not clean logs as we don't want to remove the
         # active log files of other running engines.
         self.default_config.Global.clean_logs = False
 
         # If given, this is the actual location of the logger's URL file.
         # If not, this is computed using the profile, app_dir and furl_file_name
         self.default_config.Global.url_file_name = u'iplogger.url'
         self.default_config.Global.url_file = u''
 
     def post_load_command_line_config(self):
         pass
 
     def pre_construct(self):
         super(IPLoggerApp, self).pre_construct()
 
     def construct(self):
         # This is the working dir by now.
         sys.path.insert(0, '')
 
         self.start_logging()
 
         try:
             self.watcher = LogWatcher(config=self.master_config, logname=self.log.name)
         except:
             self.log.error("Couldn't start the LogWatcher", exc_info=True)
             self.exit(1)
         
 
     def start_app(self):
         try:
             self.watcher.start()
             self.watcher.loop.start()
         except KeyboardInterrupt:
             self.log.critical("Logging Interrupted, shutting down...\n")
 
 
 def launch_new_instance():
     """Create and run the IPython LogWatcher"""
     app = IPLoggerApp()
     app.start()
 
 
 if __name__ == '__main__':
     launch_new_instance()
 

IPython/parallel/launcher.py

@@ -1,971 +1,971 b''
 #!/usr/bin/env python
 # encoding: utf-8
 """
 Facilities for launching IPython processes asynchronously.
 """
 
 #-----------------------------------------------------------------------------
 #  Copyright (C) 2008-2009  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-----------------------------------------------------------------------------
 
 #-----------------------------------------------------------------------------
 # Imports
 #-----------------------------------------------------------------------------
 
 import copy
 import logging
 import os
 import re
 import stat
 
 from signal import SIGINT, SIGTERM
 try:
     from signal import SIGKILL
 except ImportError:
     SIGKILL=SIGTERM
 
 from subprocess import Popen, PIPE, STDOUT
 try:
     from subprocess import check_output
 except ImportError:
     # pre-2.7, define check_output with Popen
     def check_output(*args, **kwargs):
         kwargs.update(dict(stdout=PIPE))
         p = Popen(*args, **kwargs)
         out,err = p.communicate()
         return out
 
 from zmq.eventloop import ioloop
 
 from IPython.external import Itpl
 # from IPython.config.configurable import Configurable
 from IPython.utils.traitlets import Any, Str, Int, List, Unicode, Dict, Instance, CUnicode
 from IPython.utils.path import get_ipython_module_path
 from IPython.utils.process import find_cmd, pycmd2argv, FindCmdError
 
 from .factory import LoggingFactory
 
 # load winhpcjob only on Windows
 try:
     from .winhpcjob import (
         IPControllerTask, IPEngineTask,
         IPControllerJob, IPEngineSetJob
     )
 except ImportError:
     pass
 
 
 #-----------------------------------------------------------------------------
 # Paths to the kernel apps
 #-----------------------------------------------------------------------------
 
 
-ipclusterz_cmd_argv = pycmd2argv(get_ipython_module_path(
+ipcluster_cmd_argv = pycmd2argv(get_ipython_module_path(
     'IPython.parallel.ipclusterapp'
 ))
 
-ipenginez_cmd_argv = pycmd2argv(get_ipython_module_path(
+ipengine_cmd_argv = pycmd2argv(get_ipython_module_path(
     'IPython.parallel.ipengineapp'
 ))
 
-ipcontrollerz_cmd_argv = pycmd2argv(get_ipython_module_path(
+ipcontroller_cmd_argv = pycmd2argv(get_ipython_module_path(
     'IPython.parallel.ipcontrollerapp'
 ))
 
 #-----------------------------------------------------------------------------
 # Base launchers and errors
 #-----------------------------------------------------------------------------
 
 
 class LauncherError(Exception):
     pass
 
 
 class ProcessStateError(LauncherError):
     pass
 
 
 class UnknownStatus(LauncherError):
     pass
 
 
 class BaseLauncher(LoggingFactory):
     """An asbtraction for starting, stopping and signaling a process."""
 
     # In all of the launchers, the work_dir is where child processes will be
     # run. This will usually be the cluster_dir, but may not be. any work_dir
     # passed into the __init__ method will override the config value.
     # This should not be used to set the work_dir for the actual engine
     # and controller. Instead, use their own config files or the
     # controller_args, engine_args attributes of the launchers to add
     # the --work-dir option.
     work_dir = Unicode(u'.')
     loop = Instance('zmq.eventloop.ioloop.IOLoop')
     
     start_data = Any()
     stop_data = Any()
     
     def _loop_default(self):
         return ioloop.IOLoop.instance()
 
     def __init__(self, work_dir=u'.', config=None, **kwargs):
         super(BaseLauncher, self).__init__(work_dir=work_dir, config=config, **kwargs)
         self.state = 'before' # can be before, running, after
         self.stop_callbacks = []
         self.start_data = None
         self.stop_data = None
 
     @property
     def args(self):
         """A list of cmd and args that will be used to start the process.
 
         This is what is passed to :func:`spawnProcess` and the first element
         will be the process name.
         """
         return self.find_args()
 
     def find_args(self):
         """The ``.args`` property calls this to find the args list.
 
         Subcommand should implement this to construct the cmd and args.
         """
         raise NotImplementedError('find_args must be implemented in a subclass')
 
     @property
     def arg_str(self):
         """The string form of the program arguments."""
         return ' '.join(self.args)
 
     @property
     def running(self):
         """Am I running."""
         if self.state == 'running':
             return True
         else:
             return False
 
     def start(self):
         """Start the process.
 
         This must return a deferred that fires with information about the
         process starting (like a pid, job id, etc.).
         """
         raise NotImplementedError('start must be implemented in a subclass')
 
     def stop(self):
         """Stop the process and notify observers of stopping.
 
         This must return a deferred that fires with information about the
         processing stopping, like errors that occur while the process is
         attempting to be shut down. This deferred won't fire when the process
         actually stops. To observe the actual process stopping, see
         :func:`observe_stop`.
         """
         raise NotImplementedError('stop must be implemented in a subclass')
 
     def on_stop(self, f):
         """Get a deferred that will fire when the process stops.
 
         The deferred will fire with data that contains information about
         the exit status of the process.
         """
         if self.state=='after':
             return f(self.stop_data)
         else:
             self.stop_callbacks.append(f)
 
     def notify_start(self, data):
         """Call this to trigger startup actions.
 
         This logs the process startup and sets the state to 'running'.  It is
         a pass-through so it can be used as a callback.
         """
 
         self.log.info('Process %r started: %r' % (self.args[0], data))
         self.start_data = data
         self.state = 'running'
         return data
 
     def notify_stop(self, data):
         """Call this to trigger process stop actions.
 
         This logs the process stopping and sets the state to 'after'. Call
         this to trigger all the deferreds from :func:`observe_stop`."""
 
         self.log.info('Process %r stopped: %r' % (self.args[0], data))
         self.stop_data = data
         self.state = 'after'
         for i in range(len(self.stop_callbacks)):
             d = self.stop_callbacks.pop()
             d(data)
         return data
 
     def signal(self, sig):
         """Signal the process.
 
         Return a semi-meaningless deferred after signaling the process.
 
         Parameters
         ----------
         sig : str or int
             'KILL', 'INT', etc., or any signal number
         """
         raise NotImplementedError('signal must be implemented in a subclass')
 
 
 #-----------------------------------------------------------------------------
 # Local process launchers
 #-----------------------------------------------------------------------------
 
 
 class LocalProcessLauncher(BaseLauncher):
     """Start and stop an external process in an asynchronous manner.
 
     This will launch the external process with a working directory of
     ``self.work_dir``.
     """
 
     # This is used to to construct self.args, which is passed to 
     # spawnProcess.
     cmd_and_args = List([])
     poll_frequency = Int(100) # in ms
 
     def __init__(self, work_dir=u'.', config=None, **kwargs):
         super(LocalProcessLauncher, self).__init__(
             work_dir=work_dir, config=config, **kwargs
         )
         self.process = None
         self.start_deferred = None
         self.poller = None
 
     def find_args(self):
         return self.cmd_and_args
 
     def start(self):
         if self.state == 'before':
             self.process = Popen(self.args,
                 stdout=PIPE,stderr=PIPE,stdin=PIPE,
                 env=os.environ,
                 cwd=self.work_dir
             )
             
             self.loop.add_handler(self.process.stdout.fileno(), self.handle_stdout, self.loop.READ)
             self.loop.add_handler(self.process.stderr.fileno(), self.handle_stderr, self.loop.READ)
             self.poller = ioloop.PeriodicCallback(self.poll, self.poll_frequency, self.loop)
             self.poller.start()
             self.notify_start(self.process.pid)
         else:
             s = 'The process was already started and has state: %r' % self.state
             raise ProcessStateError(s)
 
     def stop(self):
         return self.interrupt_then_kill()
 
     def signal(self, sig):
         if self.state == 'running':
             self.process.send_signal(sig)
 
     def interrupt_then_kill(self, delay=2.0):
         """Send INT, wait a delay and then send KILL."""
         self.signal(SIGINT)
         self.killer  = ioloop.DelayedCallback(lambda : self.signal(SIGKILL), delay*1000, self.loop)
         self.killer.start()
 
     # callbacks, etc:
     
     def handle_stdout(self, fd, events):
         line = self.process.stdout.readline()
         # a stopped process will be readable but return empty strings
         if line:
             self.log.info(line[:-1])
         else:
             self.poll()
     
     def handle_stderr(self, fd, events):
         line = self.process.stderr.readline()
         # a stopped process will be readable but return empty strings
         if line:
             self.log.error(line[:-1])
         else:
             self.poll()
     
     def poll(self):
         status = self.process.poll()
         if status is not None:
             self.poller.stop()
             self.loop.remove_handler(self.process.stdout.fileno())
             self.loop.remove_handler(self.process.stderr.fileno())
             self.notify_stop(dict(exit_code=status, pid=self.process.pid))
         return status
 
 class LocalControllerLauncher(LocalProcessLauncher):
     """Launch a controller as a regular external process."""
 
-    controller_cmd = List(ipcontrollerz_cmd_argv, config=True)
+    controller_cmd = List(ipcontroller_cmd_argv, config=True)
     # Command line arguments to ipcontroller.
     controller_args = List(['--log-to-file','--log-level', str(logging.INFO)], config=True)
 
     def find_args(self):
         return self.controller_cmd + self.controller_args
 
     def start(self, cluster_dir):
         """Start the controller by cluster_dir."""
         self.controller_args.extend(['--cluster-dir', cluster_dir])
         self.cluster_dir = unicode(cluster_dir)
         self.log.info("Starting LocalControllerLauncher: %r" % self.args)
         return super(LocalControllerLauncher, self).start()
 
 
 class LocalEngineLauncher(LocalProcessLauncher):
     """Launch a single engine as a regular externall process."""
 
-    engine_cmd = List(ipenginez_cmd_argv, config=True)
+    engine_cmd = List(ipengine_cmd_argv, config=True)
     # Command line arguments for ipengine.
     engine_args = List(
         ['--log-to-file','--log-level', str(logging.INFO)], config=True
     )
 
     def find_args(self):
         return self.engine_cmd + self.engine_args
 
     def start(self, cluster_dir):
         """Start the engine by cluster_dir."""
         self.engine_args.extend(['--cluster-dir', cluster_dir])
         self.cluster_dir = unicode(cluster_dir)
         return super(LocalEngineLauncher, self).start()
 
 
 class LocalEngineSetLauncher(BaseLauncher):
     """Launch a set of engines as regular external processes."""
 
     # Command line arguments for ipengine.
     engine_args = List(
         ['--log-to-file','--log-level', str(logging.INFO)], config=True
     )
     # launcher class
     launcher_class = LocalEngineLauncher
     
     launchers = Dict()
     stop_data = Dict()
     
     def __init__(self, work_dir=u'.', config=None, **kwargs):
         super(LocalEngineSetLauncher, self).__init__(
             work_dir=work_dir, config=config, **kwargs
         )
         self.stop_data = {}
 
     def start(self, n, cluster_dir):
         """Start n engines by profile or cluster_dir."""
         self.cluster_dir = unicode(cluster_dir)
         dlist = []
         for i in range(n):
             el = self.launcher_class(work_dir=self.work_dir, config=self.config, logname=self.log.name)
             # Copy the engine args over to each engine launcher.
             el.engine_args = copy.deepcopy(self.engine_args)
             el.on_stop(self._notice_engine_stopped)
             d = el.start(cluster_dir)
             if i==0:
                 self.log.info("Starting LocalEngineSetLauncher: %r" % el.args)
             self.launchers[i] = el
             dlist.append(d)
         self.notify_start(dlist)
         # The consumeErrors here could be dangerous
         # dfinal = gatherBoth(dlist, consumeErrors=True)
         # dfinal.addCallback(self.notify_start)
         return dlist
 
     def find_args(self):
         return ['engine set']
 
     def signal(self, sig):
         dlist = []
         for el in self.launchers.itervalues():
             d = el.signal(sig)
             dlist.append(d)
         # dfinal = gatherBoth(dlist, consumeErrors=True)
         return dlist
 
     def interrupt_then_kill(self, delay=1.0):
         dlist = []
         for el in self.launchers.itervalues():
             d = el.interrupt_then_kill(delay)
             dlist.append(d)
         # dfinal = gatherBoth(dlist, consumeErrors=True)
         return dlist
 
     def stop(self):
         return self.interrupt_then_kill()
     
     def _notice_engine_stopped(self, data):
         pid = data['pid']
         for idx,el in self.launchers.iteritems():
             if el.process.pid == pid:
                 break
         self.launchers.pop(idx)
         self.stop_data[idx] = data
         if not self.launchers:
             self.notify_stop(self.stop_data)
 
 
 #-----------------------------------------------------------------------------
 # MPIExec launchers
 #-----------------------------------------------------------------------------
 
 
 class MPIExecLauncher(LocalProcessLauncher):
     """Launch an external process using mpiexec."""
 
     # The mpiexec command to use in starting the process.
     mpi_cmd = List(['mpiexec'], config=True)
     # The command line arguments to pass to mpiexec.
     mpi_args = List([], config=True)
     # The program to start using mpiexec.
     program = List(['date'], config=True)
     # The command line argument to the program.
     program_args = List([], config=True)
     # The number of instances of the program to start.
     n = Int(1, config=True)
 
     def find_args(self):
         """Build self.args using all the fields."""
         return self.mpi_cmd + ['-n', str(self.n)] + self.mpi_args + \
                self.program + self.program_args
 
     def start(self, n):
         """Start n instances of the program using mpiexec."""
         self.n = n
         return super(MPIExecLauncher, self).start()
 
 
 class MPIExecControllerLauncher(MPIExecLauncher):
     """Launch a controller using mpiexec."""
 
-    controller_cmd = List(ipcontrollerz_cmd_argv, config=True)
+    controller_cmd = List(ipcontroller_cmd_argv, config=True)
     # Command line arguments to ipcontroller.
     controller_args = List(['--log-to-file','--log-level', str(logging.INFO)], config=True)
     n = Int(1, config=False)
 
     def start(self, cluster_dir):
         """Start the controller by cluster_dir."""
         self.controller_args.extend(['--cluster-dir', cluster_dir])
         self.cluster_dir = unicode(cluster_dir)
         self.log.info("Starting MPIExecControllerLauncher: %r" % self.args)
         return super(MPIExecControllerLauncher, self).start(1)
 
     def find_args(self):
         return self.mpi_cmd + ['-n', self.n] + self.mpi_args + \
                self.controller_cmd + self.controller_args
 
 
 class MPIExecEngineSetLauncher(MPIExecLauncher):
 
-    program = List(ipenginez_cmd_argv, config=True)
+    program = List(ipengine_cmd_argv, config=True)
     # Command line arguments for ipengine.
     program_args = List(
         ['--log-to-file','--log-level', str(logging.INFO)], config=True
     )
     n = Int(1, config=True)
 
     def start(self, n, cluster_dir):
         """Start n engines by profile or cluster_dir."""
         self.program_args.extend(['--cluster-dir', cluster_dir])
         self.cluster_dir = unicode(cluster_dir)
         self.n = n
         self.log.info('Starting MPIExecEngineSetLauncher: %r' % self.args)
         return super(MPIExecEngineSetLauncher, self).start(n)
 
 #-----------------------------------------------------------------------------
 # SSH launchers
 #-----------------------------------------------------------------------------
 
 # TODO: Get SSH Launcher working again.
 
 class SSHLauncher(LocalProcessLauncher):
     """A minimal launcher for ssh.
 
     To be useful this will probably have to be extended to use the ``sshx``
     idea for environment variables.  There could be other things this needs
     as well.
     """
 
     ssh_cmd = List(['ssh'], config=True)
     ssh_args = List(['-tt'], config=True)
     program = List(['date'], config=True)
     program_args = List([], config=True)
     hostname = CUnicode('', config=True)
     user = CUnicode('', config=True)
     location = CUnicode('')
 
     def _hostname_changed(self, name, old, new):
         if self.user:
             self.location = u'%s@%s' % (self.user, new)
         else:
             self.location = new
 
     def _user_changed(self, name, old, new):
         self.location = u'%s@%s' % (new, self.hostname)
 
     def find_args(self):
         return self.ssh_cmd + self.ssh_args + [self.location] + \
                self.program + self.program_args
 
     def start(self, cluster_dir, hostname=None, user=None):
         self.cluster_dir = unicode(cluster_dir)
         if hostname is not None:
             self.hostname = hostname
         if user is not None:
             self.user = user
         
         return super(SSHLauncher, self).start()
     
     def signal(self, sig):
         if self.state == 'running':
             # send escaped ssh connection-closer
             self.process.stdin.write('~.')
             self.process.stdin.flush()
         
 
 
 class SSHControllerLauncher(SSHLauncher):
 
-    program = List(ipcontrollerz_cmd_argv, config=True)
+    program = List(ipcontroller_cmd_argv, config=True)
     # Command line arguments to ipcontroller.
     program_args = List(['-r', '--log-to-file','--log-level', str(logging.INFO)], config=True)
 
 
 class SSHEngineLauncher(SSHLauncher):
-    program = List(ipenginez_cmd_argv, config=True)
+    program = List(ipengine_cmd_argv, config=True)
     # Command line arguments for ipengine.
     program_args = List(
         ['--log-to-file','--log-level', str(logging.INFO)], config=True
     )
     
 class SSHEngineSetLauncher(LocalEngineSetLauncher):
     launcher_class = SSHEngineLauncher
     engines = Dict(config=True)
     
     def start(self, n, cluster_dir):
         """Start engines by profile or cluster_dir.
         `n` is ignored, and the `engines` config property is used instead.
         """
         
         self.cluster_dir = unicode(cluster_dir)
         dlist = []
         for host, n in self.engines.iteritems():
             if isinstance(n, (tuple, list)):
                 n, args = n
             else:
                 args = copy.deepcopy(self.engine_args)
             
             if '@' in host:
                 user,host = host.split('@',1)
             else:
                 user=None
             for i in range(n):
                 el = self.launcher_class(work_dir=self.work_dir, config=self.config, logname=self.log.name)
                 
                 # Copy the engine args over to each engine launcher.
                 i
                 el.program_args = args
                 el.on_stop(self._notice_engine_stopped)
                 d = el.start(cluster_dir, user=user, hostname=host)
                 if i==0:
                     self.log.info("Starting SSHEngineSetLauncher: %r" % el.args)
                 self.launchers[host+str(i)] = el
                 dlist.append(d)
         self.notify_start(dlist)
         return dlist
     
 
 
 #-----------------------------------------------------------------------------
 # Windows HPC Server 2008 scheduler launchers
 #-----------------------------------------------------------------------------
 
 
 # This is only used on Windows.
 def find_job_cmd():
     if os.name=='nt':
         try:
             return find_cmd('job')
         except FindCmdError:
             return 'job'
     else:
         return 'job'
 
 
 class WindowsHPCLauncher(BaseLauncher):
 
     # A regular expression used to get the job id from the output of the 
     # submit_command.
     job_id_regexp = Str(r'\d+', config=True)
     # The filename of the instantiated job script.
     job_file_name = CUnicode(u'ipython_job.xml', config=True)
     # The full path to the instantiated job script. This gets made dynamically
     # by combining the work_dir with the job_file_name.
     job_file = CUnicode(u'')
     # The hostname of the scheduler to submit the job to
     scheduler = CUnicode('', config=True)
     job_cmd = CUnicode(find_job_cmd(), config=True)
 
     def __init__(self, work_dir=u'.', config=None, **kwargs):
         super(WindowsHPCLauncher, self).__init__(
             work_dir=work_dir, config=config, **kwargs
         )
 
     @property
     def job_file(self):
         return os.path.join(self.work_dir, self.job_file_name)
 
     def write_job_file(self, n):
         raise NotImplementedError("Implement write_job_file in a subclass.")
 
     def find_args(self):
         return [u'job.exe']
         
     def parse_job_id(self, output):
         """Take the output of the submit command and return the job id."""
         m = re.search(self.job_id_regexp, output)
         if m is not None:
             job_id = m.group()
         else:
             raise LauncherError("Job id couldn't be determined: %s" % output)
         self.job_id = job_id
         self.log.info('Job started with job id: %r' % job_id)
         return job_id
 
     def start(self, n):
         """Start n copies of the process using the Win HPC job scheduler."""
         self.write_job_file(n)
         args = [
             'submit',
             '/jobfile:%s' % self.job_file,
             '/scheduler:%s' % self.scheduler
         ]
         self.log.info("Starting Win HPC Job: %s" % (self.job_cmd + ' ' + ' '.join(args),))
         # Twisted will raise DeprecationWarnings if we try to pass unicode to this
         output = check_output([self.job_cmd]+args,
             env=os.environ,
             cwd=self.work_dir,
             stderr=STDOUT
         )
         job_id = self.parse_job_id(output)
         self.notify_start(job_id)
         return job_id
 
     def stop(self):
         args = [
             'cancel',
             self.job_id,
             '/scheduler:%s' % self.scheduler
         ]
         self.log.info("Stopping Win HPC Job: %s" % (self.job_cmd + ' ' + ' '.join(args),))
         try:
             output = check_output([self.job_cmd]+args,
                 env=os.environ,
                 cwd=self.work_dir,
                 stderr=STDOUT
             )
         except:
             output = 'The job already appears to be stoppped: %r' % self.job_id
         self.notify_stop(dict(job_id=self.job_id, output=output))  # Pass the output of the kill cmd
         return output
 
 
 class WindowsHPCControllerLauncher(WindowsHPCLauncher):
 
     job_file_name = CUnicode(u'ipcontroller_job.xml', config=True)
     extra_args = List([], config=False)
 
     def write_job_file(self, n):
         job = IPControllerJob(config=self.config)
 
         t = IPControllerTask(config=self.config)
         # The tasks work directory is *not* the actual work directory of 
         # the controller. It is used as the base path for the stdout/stderr
         # files that the scheduler redirects to.
         t.work_directory = self.cluster_dir
         # Add the --cluster-dir and from self.start().
         t.controller_args.extend(self.extra_args)
         job.add_task(t)
 
         self.log.info("Writing job description file: %s" % self.job_file)
         job.write(self.job_file)
 
     @property
     def job_file(self):
         return os.path.join(self.cluster_dir, self.job_file_name)
 
     def start(self, cluster_dir):
         """Start the controller by cluster_dir."""
         self.extra_args = ['--cluster-dir', cluster_dir]
         self.cluster_dir = unicode(cluster_dir)
         return super(WindowsHPCControllerLauncher, self).start(1)
 
 
 class WindowsHPCEngineSetLauncher(WindowsHPCLauncher):
 
     job_file_name = CUnicode(u'ipengineset_job.xml', config=True)
     extra_args = List([], config=False)
 
     def write_job_file(self, n):
         job = IPEngineSetJob(config=self.config)
 
         for i in range(n):
             t = IPEngineTask(config=self.config)
             # The tasks work directory is *not* the actual work directory of 
             # the engine. It is used as the base path for the stdout/stderr
             # files that the scheduler redirects to.
             t.work_directory = self.cluster_dir
             # Add the --cluster-dir and from self.start().
             t.engine_args.extend(self.extra_args)
             job.add_task(t)
 
         self.log.info("Writing job description file: %s" % self.job_file)
         job.write(self.job_file)
 
     @property
     def job_file(self):
         return os.path.join(self.cluster_dir, self.job_file_name)
 
     def start(self, n, cluster_dir):
         """Start the controller by cluster_dir."""
         self.extra_args = ['--cluster-dir', cluster_dir]
         self.cluster_dir = unicode(cluster_dir)
         return super(WindowsHPCEngineSetLauncher, self).start(n)
 
 
 #-----------------------------------------------------------------------------
 # Batch (PBS) system launchers
 #-----------------------------------------------------------------------------
 
 class BatchSystemLauncher(BaseLauncher):
     """Launch an external process using a batch system.
 
     This class is designed to work with UNIX batch systems like PBS, LSF,
     GridEngine, etc.  The overall model is that there are different commands
     like qsub, qdel, etc. that handle the starting and stopping of the process.
 
     This class also has the notion of a batch script. The ``batch_template``
     attribute can be set to a string that is a template for the batch script.
     This template is instantiated using Itpl. Thus the template can use
     ${n} fot the number of instances. Subclasses can add additional variables
     to the template dict.
     """
 
     # Subclasses must fill these in.  See PBSEngineSet
     # The name of the command line program used to submit jobs.
     submit_command = List([''], config=True)
     # The name of the command line program used to delete jobs.
     delete_command = List([''], config=True)
     # A regular expression used to get the job id from the output of the 
     # submit_command.
     job_id_regexp = CUnicode('', config=True)
     # The string that is the batch script template itself.
     batch_template = CUnicode('', config=True)
     # The file that contains the batch template
     batch_template_file = CUnicode(u'', config=True)
     # The filename of the instantiated batch script.
     batch_file_name = CUnicode(u'batch_script', config=True)
     # The PBS Queue
     queue = CUnicode(u'', config=True)
     
     # not configurable, override in subclasses
     # PBS Job Array regex
     job_array_regexp = CUnicode('')
     job_array_template = CUnicode('')
     # PBS Queue regex
     queue_regexp = CUnicode('')
     queue_template = CUnicode('')
     # The default batch template, override in subclasses
     default_template = CUnicode('')
     # The full path to the instantiated batch script.
     batch_file = CUnicode(u'')
     # the format dict used with batch_template:
     context = Dict()
 
     
     def find_args(self):
         return self.submit_command + [self.batch_file]
     
     def __init__(self, work_dir=u'.', config=None, **kwargs):
         super(BatchSystemLauncher, self).__init__(
             work_dir=work_dir, config=config, **kwargs
         )
         self.batch_file = os.path.join(self.work_dir, self.batch_file_name)
 
     def parse_job_id(self, output):
         """Take the output of the submit command and return the job id."""
         m = re.search(self.job_id_regexp, output)
         if m is not None:
             job_id = m.group()
         else:
             raise LauncherError("Job id couldn't be determined: %s" % output)
         self.job_id = job_id
         self.log.info('Job submitted with job id: %r' % job_id)
         return job_id
 
     def write_batch_script(self, n):
         """Instantiate and write the batch script to the work_dir."""
         self.context['n'] = n
         self.context['queue'] = self.queue
         print self.context
         # first priority is batch_template if set
         if self.batch_template_file and not self.batch_template:
             # second priority is batch_template_file
             with open(self.batch_template_file) as f:
                 self.batch_template = f.read()
         if not self.batch_template:
             # third (last) priority is default_template
             self.batch_template = self.default_template
         
         regex = re.compile(self.job_array_regexp)
         # print regex.search(self.batch_template)
         if not regex.search(self.batch_template):
             self.log.info("adding job array settings to batch script")
             firstline, rest = self.batch_template.split('\n',1)
             self.batch_template = u'\n'.join([firstline, self.job_array_template, rest])
         
         regex = re.compile(self.queue_regexp)
         # print regex.search(self.batch_template)
         if self.queue and not regex.search(self.batch_template):
             self.log.info("adding PBS queue settings to batch script")
             firstline, rest = self.batch_template.split('\n',1)
             self.batch_template = u'\n'.join([firstline, self.queue_template, rest])
             
         script_as_string = Itpl.itplns(self.batch_template, self.context)
         self.log.info('Writing instantiated batch script: %s' % self.batch_file)
     
         with open(self.batch_file, 'w') as f:
             f.write(script_as_string)
         os.chmod(self.batch_file, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR)
 
     def start(self, n, cluster_dir):
         """Start n copies of the process using a batch system."""
         # Here we save profile and cluster_dir in the context so they
         # can be used in the batch script template as ${profile} and
         # ${cluster_dir}
         self.context['cluster_dir'] = cluster_dir
         self.cluster_dir = unicode(cluster_dir)
         self.write_batch_script(n)
         output = check_output(self.args, env=os.environ)
         
         job_id = self.parse_job_id(output)
         self.notify_start(job_id)
         return job_id
 
     def stop(self):
         output = check_output(self.delete_command+[self.job_id], env=os.environ)
         self.notify_stop(dict(job_id=self.job_id, output=output)) # Pass the output of the kill cmd
         return output
 
 
 class PBSLauncher(BatchSystemLauncher):
     """A BatchSystemLauncher subclass for PBS."""
 
     submit_command = List(['qsub'], config=True)
     delete_command = List(['qdel'], config=True)
     job_id_regexp = CUnicode(r'\d+', config=True)
     
     batch_file = CUnicode(u'')
     job_array_regexp = CUnicode('#PBS\W+-t\W+[\w\d\-\$]+')
     job_array_template = CUnicode('#PBS -t 1-$n')
     queue_regexp = CUnicode('#PBS\W+-q\W+\$?\w+')
     queue_template = CUnicode('#PBS -q $queue')
 
 
 class PBSControllerLauncher(PBSLauncher):
     """Launch a controller using PBS."""
 
     batch_file_name = CUnicode(u'pbs_controller', config=True)
     default_template= CUnicode("""#!/bin/sh
 #PBS -V
-#PBS -N ipcontrollerz
+#PBS -N ipcontroller
 %s --log-to-file --cluster-dir $cluster_dir
-"""%(' '.join(ipcontrollerz_cmd_argv)))
+"""%(' '.join(ipcontroller_cmd_argv)))
 
     def start(self, cluster_dir):
         """Start the controller by profile or cluster_dir."""
         self.log.info("Starting PBSControllerLauncher: %r" % self.args)
         return super(PBSControllerLauncher, self).start(1, cluster_dir)
 
 
 class PBSEngineSetLauncher(PBSLauncher):
     """Launch Engines using PBS"""
     batch_file_name = CUnicode(u'pbs_engines', config=True)
     default_template= CUnicode(u"""#!/bin/sh
 #PBS -V
-#PBS -N ipenginez
+#PBS -N ipengine
 %s --cluster-dir $cluster_dir
-"""%(' '.join(ipenginez_cmd_argv)))
+"""%(' '.join(ipengine_cmd_argv)))
 
     def start(self, n, cluster_dir):
         """Start n engines by profile or cluster_dir."""
         self.log.info('Starting %i engines with PBSEngineSetLauncher: %r' % (n, self.args))
         return super(PBSEngineSetLauncher, self).start(n, cluster_dir)
 
 #SGE is very similar to PBS
 
 class SGELauncher(PBSLauncher):
     """Sun GridEngine is a PBS clone with slightly different syntax"""
     job_array_regexp = CUnicode('#$$\W+-t\W+[\w\d\-\$]+')
     job_array_template = CUnicode('#$$ -t 1-$n')
     queue_regexp = CUnicode('#$$\W+-q\W+\$?\w+')
     queue_template = CUnicode('#$$ -q $queue')
 
 class SGEControllerLauncher(SGELauncher):
     """Launch a controller using SGE."""
 
     batch_file_name = CUnicode(u'sge_controller', config=True)
     default_template= CUnicode(u"""#$$ -V
 #$$ -S /bin/sh
-#$$ -N ipcontrollerz
+#$$ -N ipcontroller
 %s --log-to-file --cluster-dir $cluster_dir
-"""%(' '.join(ipcontrollerz_cmd_argv)))
+"""%(' '.join(ipcontroller_cmd_argv)))
 
     def start(self, cluster_dir):
         """Start the controller by profile or cluster_dir."""
         self.log.info("Starting PBSControllerLauncher: %r" % self.args)
         return super(PBSControllerLauncher, self).start(1, cluster_dir)
 
 class SGEEngineSetLauncher(SGELauncher):
     """Launch Engines with SGE"""
     batch_file_name = CUnicode(u'sge_engines', config=True)
     default_template = CUnicode("""#$$ -V
 #$$ -S /bin/sh
-#$$ -N ipenginez
+#$$ -N ipengine
 %s --cluster-dir $cluster_dir
-"""%(' '.join(ipenginez_cmd_argv)))
+"""%(' '.join(ipengine_cmd_argv)))
 
     def start(self, n, cluster_dir):
         """Start n engines by profile or cluster_dir."""
         self.log.info('Starting %i engines with SGEEngineSetLauncher: %r' % (n, self.args))
         return super(SGEEngineSetLauncher, self).start(n, cluster_dir)
 
 
 #-----------------------------------------------------------------------------
 # A launcher for ipcluster itself!
 #-----------------------------------------------------------------------------
 
 
 class IPClusterLauncher(LocalProcessLauncher):
     """Launch the ipcluster program in an external process."""
 
-    ipcluster_cmd = List(ipclusterz_cmd_argv, config=True)
+    ipcluster_cmd = List(ipcluster_cmd_argv, config=True)
     # Command line arguments to pass to ipcluster.
     ipcluster_args = List(
         ['--clean-logs', '--log-to-file', '--log-level', str(logging.INFO)], config=True)
     ipcluster_subcommand = Str('start')
     ipcluster_n = Int(2)
 
     def find_args(self):
         return self.ipcluster_cmd + [self.ipcluster_subcommand] + \
             ['-n', repr(self.ipcluster_n)] + self.ipcluster_args
 
     def start(self):
         self.log.info("Starting ipcluster: %r" % self.args)
         return super(IPClusterLauncher, self).start()
 

IPython/parallel/logwatcher.py

@@ -1,98 +1,98 b''
 #!/usr/bin/env python
-"""A simple logger object that consolidates messages incoming from ipclusterz processes."""
+"""A simple logger object that consolidates messages incoming from ipcluster processes."""
 
 #-----------------------------------------------------------------------------
 #  Copyright (C) 2011  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-----------------------------------------------------------------------------
 
 #-----------------------------------------------------------------------------
 # Imports
 #-----------------------------------------------------------------------------
 
 
 import logging
 import sys
 
 import zmq
 from zmq.eventloop import ioloop, zmqstream
 
 from IPython.utils.traitlets import Int, Str, Instance, List
 
 from .factory import LoggingFactory
 
 #-----------------------------------------------------------------------------
 # Classes
 #-----------------------------------------------------------------------------
 
 
 class LogWatcher(LoggingFactory):
     """A simple class that receives messages on a SUB socket, as published
     by subclasses of `zmq.log.handlers.PUBHandler`, and logs them itself.
     
     This can subscribe to multiple topics, but defaults to all topics.
     """
     # configurables
     topics = List([''], config=True)
     url = Str('tcp://127.0.0.1:20202', config=True)
     
     # internals
     context = Instance(zmq.Context, (), {})
     stream = Instance('zmq.eventloop.zmqstream.ZMQStream')
     loop = Instance('zmq.eventloop.ioloop.IOLoop')
     def _loop_default(self):
         return ioloop.IOLoop.instance()
     
     def __init__(self, **kwargs):
         super(LogWatcher, self).__init__(**kwargs)
         s = self.context.socket(zmq.SUB)
         s.bind(self.url)
         self.stream = zmqstream.ZMQStream(s, self.loop)
         self.subscribe()
         self.on_trait_change(self.subscribe, 'topics')
     
     def start(self):
         self.stream.on_recv(self.log_message)
     
     def stop(self):
         self.stream.stop_on_recv()
     
     def subscribe(self):
         """Update our SUB socket's subscriptions."""
         self.stream.setsockopt(zmq.UNSUBSCRIBE, '')
         for topic in self.topics:
             self.log.debug("Subscribing to: %r"%topic)
             self.stream.setsockopt(zmq.SUBSCRIBE, topic)
     
     def _extract_level(self, topic_str):
         """Turn 'engine.0.INFO.extra' into (logging.INFO, 'engine.0.extra')"""
         topics = topic_str.split('.')
         for idx,t in enumerate(topics):
             level = getattr(logging, t, None)
             if level is not None:
                 break
         
         if level is None:
             level = logging.INFO
         else:
             topics.pop(idx)
         
         return level, '.'.join(topics)
             
             
     def log_message(self, raw):
         """receive and parse a message, then log it."""
         if len(raw) != 2 or '.' not in raw[0]:
             self.log.error("Invalid log message: %s"%raw)
             return
         else:
             topic, msg = raw
             # don't newline, since log messages always newline:
             topic,level_name = topic.rsplit('.',1)
             level,topic = self._extract_level(topic)
             if msg[-1] == '\n':
                 msg = msg[:-1]
             logging.log(level, "[%s] %s" % (topic, msg))
 

IPython/parallel/tests/__init__.py

@@ -1,69 +1,69 b''
 """toplevel setup/teardown for parallel tests."""
 
 #-------------------------------------------------------------------------------
 #  Copyright (C) 2011  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-------------------------------------------------------------------------------
 
 #-------------------------------------------------------------------------------
 # Imports
 #-------------------------------------------------------------------------------
 
 import tempfile
 import time
 from subprocess import Popen, PIPE, STDOUT
 
 from IPython.parallel import client
 
 processes = []
 blackhole = tempfile.TemporaryFile()
 
 # nose setup/teardown
 
 def setup():
-    cp = Popen('ipcontrollerz --profile iptest -r --log-level 10 --log-to-file'.split(), stdout=blackhole, stderr=STDOUT)
+    cp = Popen('ipcontroller --profile iptest -r --log-level 10 --log-to-file'.split(), stdout=blackhole, stderr=STDOUT)
     processes.append(cp)
     time.sleep(.5)
     add_engines(1)
     c = client.Client(profile='iptest')
     while not c.ids:
         time.sleep(.1)
         c.spin()
     c.close()
 
 def add_engines(n=1, profile='iptest'):
     rc = client.Client(profile=profile)
     base = len(rc)
     eps = []
     for i in range(n):
-        ep = Popen(['ipenginez']+ ['--profile', profile, '--log-level', '10', '--log-to-file'], stdout=blackhole, stderr=STDOUT)
+        ep = Popen(['ipengine']+ ['--profile', profile, '--log-level', '10', '--log-to-file'], stdout=blackhole, stderr=STDOUT)
         # ep.start()
         processes.append(ep)
         eps.append(ep)
     while len(rc) < base+n:
         time.sleep(.1)
         rc.spin()
     rc.close()
     return eps
 
 def teardown():
     time.sleep(1)
     while processes:
         p = processes.pop()
         if p.poll() is None:
             try:
                 p.terminate()
             except Exception, e:
                 print e
                 pass
         if p.poll() is None:
             time.sleep(.25)
         if p.poll() is None:
             try:
                 print 'killing'
                 p.kill()
             except:
                 print "couldn't shutdown process: ", p
     

IPython/parallel/tests/test_newserialized.py

@@ -1,108 +1,108 b''
 """test serialization with newserialized"""
 
 #-------------------------------------------------------------------------------
 #  Copyright (C) 2011  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-------------------------------------------------------------------------------
 
 #-------------------------------------------------------------------------------
 # Imports
 #-------------------------------------------------------------------------------
 
 from unittest import TestCase
 
-from IPython.testing.parametric import parametric
+from IPython.testing.decorators import parametric
 from IPython.utils import newserialized as ns
 from IPython.utils.pickleutil import can, uncan, CannedObject, CannedFunction
 from IPython.parallel.tests.clienttest import skip_without
 
 
 class CanningTestCase(TestCase):
     def test_canning(self):
         d = dict(a=5,b=6)
         cd = can(d)
         self.assertTrue(isinstance(cd, dict))
 
     def test_canned_function(self):
         f = lambda : 7
         cf = can(f)
         self.assertTrue(isinstance(cf, CannedFunction))
     
     @parametric
     def test_can_roundtrip(cls):
         objs = [
             dict(),
             set(),
             list(),
             ['a',1,['a',1],u'e'],
         ]
         return map(cls.run_roundtrip, objs)
     
     @classmethod
     def run_roundtrip(self, obj):
         o = uncan(can(obj))
         assert o == obj, "failed assertion: %r == %r"%(o,obj)
     
     def test_serialized_interfaces(self):
 
         us = {'a':10, 'b':range(10)}
         s = ns.serialize(us)
         uus = ns.unserialize(s)
         self.assertTrue(isinstance(s, ns.SerializeIt))
         self.assertEquals(uus, us)
 
     def test_pickle_serialized(self):
         obj = {'a':1.45345, 'b':'asdfsdf', 'c':10000L}
         original = ns.UnSerialized(obj)
         originalSer = ns.SerializeIt(original)
         firstData = originalSer.getData()
         firstTD = originalSer.getTypeDescriptor()
         firstMD = originalSer.getMetadata()
         self.assertEquals(firstTD, 'pickle')
         self.assertEquals(firstMD, {})
         unSerialized = ns.UnSerializeIt(originalSer)
         secondObj = unSerialized.getObject()
         for k, v in secondObj.iteritems():
             self.assertEquals(obj[k], v)
         secondSer = ns.SerializeIt(ns.UnSerialized(secondObj))
         self.assertEquals(firstData, secondSer.getData())
         self.assertEquals(firstTD, secondSer.getTypeDescriptor() )
         self.assertEquals(firstMD, secondSer.getMetadata())
     
     @skip_without('numpy')
     def test_ndarray_serialized(self):
         import numpy
         a = numpy.linspace(0.0, 1.0, 1000)
         unSer1 = ns.UnSerialized(a)
         ser1 = ns.SerializeIt(unSer1)
         td = ser1.getTypeDescriptor()
         self.assertEquals(td, 'ndarray')
         md = ser1.getMetadata()
         self.assertEquals(md['shape'], a.shape)
         self.assertEquals(md['dtype'], a.dtype.str)
         buff = ser1.getData()
         self.assertEquals(buff, numpy.getbuffer(a))
         s = ns.Serialized(buff, td, md)
         final = ns.unserialize(s)
         self.assertEquals(numpy.getbuffer(a), numpy.getbuffer(final))
         self.assertTrue((a==final).all())
         self.assertEquals(a.dtype.str, final.dtype.str)
         self.assertEquals(a.shape, final.shape)
         # test non-copying:
         a[2] = 1e9
         self.assertTrue((a==final).all())
     
     def test_uncan_function_globals(self):
         """test that uncanning a module function restores it into its module"""
         from re import search
         cf = can(search)
         csearch = uncan(cf)
         self.assertEqual(csearch.__module__, search.__module__)
         self.assertNotEqual(csearch('asd', 'asdf'), None)
         csearch = uncan(cf, dict(a=5))
         self.assertEqual(csearch.__module__, search.__module__)
         self.assertNotEqual(csearch('asd', 'asdf'), None)
         
         
  No newline at end of file

IPython/testing/iptest.py

@@ -1,419 +1,420 b''
 # -*- coding: utf-8 -*-
 """IPython Test Suite Runner.
 
 This module provides a main entry point to a user script to test IPython
 itself from the command line. There are two ways of running this script:
 
 1. With the syntax `iptest all`.  This runs our entire test suite by
    calling this script (with different arguments) recursively.  This
    causes modules and package to be tested in different processes, using nose
    or trial where appropriate.
 2. With the regular nose syntax, like `iptest -vvs IPython`.  In this form
    the script simply calls nose, but with special command line flags and
    plugins loaded.
 
 """
 
 #-----------------------------------------------------------------------------
 #  Copyright (C) 2009  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-----------------------------------------------------------------------------
 
 #-----------------------------------------------------------------------------
 # Imports
 #-----------------------------------------------------------------------------
 
 # Stdlib
 import os
 import os.path as path
 import signal
 import sys
 import subprocess
 import tempfile
 import time
 import warnings
 
 # Note: monkeypatch!
 # We need to monkeypatch a small problem in nose itself first, before importing
 # it for actual use.  This should get into nose upstream, but its release cycle
 # is slow and we need it for our parametric tests to work correctly.
 from IPython.testing import nosepatch
 # Now, proceed to import nose itself
 import nose.plugins.builtin
 from nose.core import TestProgram
 
 # Our own imports
 from IPython.utils.path import get_ipython_module_path
 from IPython.utils.process import find_cmd, pycmd2argv
 from IPython.utils.sysinfo import sys_info
 
 from IPython.testing import globalipapp
 from IPython.testing.plugin.ipdoctest import IPythonDoctest
 from IPython.external.decorators import KnownFailure
 
 pjoin = path.join
 
 
 #-----------------------------------------------------------------------------
 # Globals
 #-----------------------------------------------------------------------------
 
 
 #-----------------------------------------------------------------------------
 # Warnings control
 #-----------------------------------------------------------------------------
 
 # Twisted generates annoying warnings with Python 2.6, as will do other code
 # that imports 'sets' as of today
 warnings.filterwarnings('ignore', 'the sets module is deprecated',
                         DeprecationWarning )
 
 # This one also comes from Twisted
 warnings.filterwarnings('ignore', 'the sha module is deprecated',
                         DeprecationWarning)
 
 # Wx on Fedora11 spits these out
 warnings.filterwarnings('ignore', 'wxPython/wxWidgets release number mismatch',
                         UserWarning)
 
 #-----------------------------------------------------------------------------
 # Logic for skipping doctests
 #-----------------------------------------------------------------------------
 
 def test_for(mod, min_version=None):
     """Test to see if mod is importable."""
     try:
         __import__(mod)
     except (ImportError, RuntimeError):
         # GTK reports Runtime error if it can't be initialized even if  it's
         # importable.
         return False
     else:
         if min_version:
             return sys.modules[mod].__version__ >= min_version
         else:
             return True
 
 # Global dict where we can store information on what we have and what we don't
 # have available at test run time
 have = {}
 
 have['curses'] = test_for('_curses')
 have['wx'] = test_for('wx')
 have['wx.aui'] = test_for('wx.aui')
 have['pexpect'] = test_for('pexpect')
 have['zmq'] = test_for('zmq', '2.0.10')
 
 #-----------------------------------------------------------------------------
 # Functions and classes
 #-----------------------------------------------------------------------------
 
 def report():
     """Return a string with a summary report of test-related variables."""
 
     out = [ sys_info(), '\n']
 
     avail = []
     not_avail = []
     
     for k, is_avail in have.items():
         if is_avail:
             avail.append(k)
         else:
             not_avail.append(k)
 
     if avail:
         out.append('\nTools and libraries available at test time:\n')
         avail.sort()
         out.append('   ' + ' '.join(avail)+'\n')
 
     if not_avail:
         out.append('\nTools and libraries NOT available at test time:\n')
         not_avail.sort()
         out.append('   ' + ' '.join(not_avail)+'\n')
         
     return ''.join(out)
 
 
 def make_exclude():
     """Make patterns of modules and packages to exclude from testing.
 
     For the IPythonDoctest plugin, we need to exclude certain patterns that
     cause testing problems.  We should strive to minimize the number of
     skipped modules, since this means untested code.
 
     These modules and packages will NOT get scanned by nose at all for tests.
     """
     # Simple utility to make IPython paths more readably, we need a lot of
     # these below
     ipjoin = lambda *paths: pjoin('IPython', *paths)
     
     exclusions = [ipjoin('external'),
                   pjoin('IPython_doctest_plugin'),
                   ipjoin('quarantine'),
                   ipjoin('deathrow'),
                   ipjoin('testing', 'attic'),
                   # This guy is probably attic material
                   ipjoin('testing', 'mkdoctests'),
                   # Testing inputhook will need a lot of thought, to figure out
                   # how to have tests that don't lock up with the gui event
                   # loops in the picture
                   ipjoin('lib', 'inputhook'),
                   # Config files aren't really importable stand-alone
                   ipjoin('config', 'default'),
                   ipjoin('config', 'profile'),
                   ]
 
     if not have['wx']:
         exclusions.append(ipjoin('lib', 'inputhookwx'))
         
     # We do this unconditionally, so that the test suite doesn't import
     # gtk, changing the default encoding and masking some unicode bugs.
     exclusions.append(ipjoin('lib', 'inputhookgtk'))
 
     # These have to be skipped on win32 because the use echo, rm, cd, etc.
     # See ticket https://bugs.launchpad.net/bugs/366982
     if sys.platform == 'win32':
         exclusions.append(ipjoin('testing', 'plugin', 'test_exampleip'))
         exclusions.append(ipjoin('testing', 'plugin', 'dtexample'))
 
     if not have['pexpect']:
         exclusions.extend([ipjoin('scripts', 'irunner'),
                            ipjoin('lib', 'irunner')])
 
     if not have['zmq']:
         exclusions.append(ipjoin('zmq'))
+        exclusions.append(ipjoin('parallel'))
 
     # This is needed for the reg-exp to match on win32 in the ipdoctest plugin.
     if sys.platform == 'win32':
         exclusions = [s.replace('\\','\\\\') for s in exclusions]
 
     return exclusions
 
 
 class IPTester(object):
     """Call that calls iptest or trial in a subprocess.
     """
     #: string, name of test runner that will be called
     runner = None
     #: list, parameters for test runner
     params = None
     #: list, arguments of system call to be made to call test runner
     call_args = None
     #: list, process ids of subprocesses we start (for cleanup)
     pids = None
     
     def __init__(self, runner='iptest', params=None):
         """Create new test runner."""
         p = os.path
         if runner == 'iptest':
             iptest_app = get_ipython_module_path('IPython.testing.iptest')
             self.runner = pycmd2argv(iptest_app) + sys.argv[1:]
         else:
             raise Exception('Not a valid test runner: %s' % repr(runner))
         if params is None:
             params = []
         if isinstance(params, str):
             params = [params]
         self.params = params
 
         # Assemble call
         self.call_args = self.runner+self.params
 
         # Store pids of anything we start to clean up on deletion, if possible
         # (on posix only, since win32 has no os.kill)
         self.pids = []
 
     if sys.platform == 'win32':
         def _run_cmd(self):
             # On Windows, use os.system instead of subprocess.call, because I
             # was having problems with subprocess and I just don't know enough
             # about win32 to debug this reliably.  Os.system may be the 'old
             # fashioned' way to do it, but it works just fine.  If someone
             # later can clean this up that's fine, as long as the tests run
             # reliably in win32.
             # What types of problems are you having. They may be related to
             # running Python in unboffered mode. BG.
             return os.system(' '.join(self.call_args))
     else:
         def _run_cmd(self):
             # print >> sys.stderr, '*** CMD:', ' '.join(self.call_args) # dbg
             subp = subprocess.Popen(self.call_args)
             self.pids.append(subp.pid)
             # If this fails, the pid will be left in self.pids and cleaned up
             # later, but if the wait call succeeds, then we can clear the
             # stored pid.
             retcode = subp.wait()
             self.pids.pop()
             return retcode
         
     def run(self):
         """Run the stored commands"""
         try:
             return self._run_cmd()
         except:
             import traceback
             traceback.print_exc()
             return 1  # signal failure
 
     def __del__(self):
         """Cleanup on exit by killing any leftover processes."""
 
         if not hasattr(os, 'kill'):
             return
         
         for pid in self.pids:
             try:
                 print 'Cleaning stale PID:', pid
                 os.kill(pid, signal.SIGKILL)
             except OSError:
                 # This is just a best effort, if we fail or the process was
                 # really gone, ignore it.
                 pass     
 
 
 def make_runners():
     """Define the top-level packages that need to be tested.
     """
 
     # Packages to be tested via nose, that only depend on the stdlib
     nose_pkg_names = ['config', 'core', 'extensions', 'frontend', 'lib',
                      'scripts', 'testing', 'utils' ]
 
     # For debugging this code, only load quick stuff
     #nose_pkg_names = ['core', 'extensions']  # dbg
 
     # Make fully qualified package names prepending 'IPython.' to our name lists
     nose_packages = ['IPython.%s' % m for m in nose_pkg_names ]
 
     # Make runners
     runners = [ (v, IPTester('iptest', params=v)) for v in nose_packages ]
     
     return runners
 
 
 def run_iptest():
     """Run the IPython test suite using nose.
     
     This function is called when this script is **not** called with the form
     `iptest all`.  It simply calls nose with appropriate command line flags
     and accepts all of the standard nose arguments.
     """
 
     warnings.filterwarnings('ignore', 
         'This will be removed soon.  Use IPython.testing.util instead')
 
     argv = sys.argv + [ '--detailed-errors',  # extra info in tracebacks
                                                 
                         # Loading ipdoctest causes problems with Twisted, but
                         # our test suite runner now separates things and runs
                         # all Twisted tests with trial.
                         '--with-ipdoctest',
                         '--ipdoctest-tests','--ipdoctest-extension=txt',
                         
                         # We add --exe because of setuptools' imbecility (it
                         # blindly does chmod +x on ALL files).  Nose does the
                         # right thing and it tries to avoid executables,
                         # setuptools unfortunately forces our hand here.  This
                         # has been discussed on the distutils list and the
                         # setuptools devs refuse to fix this problem!
                         '--exe',
                         ]
 
     if nose.__version__ >= '0.11':
         # I don't fully understand why we need this one, but depending on what
         # directory the test suite is run from, if we don't give it, 0 tests
         # get run.  Specifically, if the test suite is run from the source dir
         # with an argument (like 'iptest.py IPython.core', 0 tests are run,
         # even if the same call done in this directory works fine).  It appears
         # that if the requested package is in the current dir, nose bails early
         # by default.  Since it's otherwise harmless, leave it in by default
         # for nose >= 0.11, though unfortunately nose 0.10 doesn't support it.
         argv.append('--traverse-namespace')
 
     # Construct list of plugins, omitting the existing doctest plugin, which
     # ours replaces (and extends).
     plugins = [IPythonDoctest(make_exclude()), KnownFailure()]
     for p in nose.plugins.builtin.plugins:
         plug = p()
         if plug.name == 'doctest':
             continue
         plugins.append(plug)
 
     # We need a global ipython running in this process
     globalipapp.start_ipython()
     # Now nose can run
     TestProgram(argv=argv, plugins=plugins)
 
 
 def run_iptestall():
     """Run the entire IPython test suite by calling nose and trial.
     
     This function constructs :class:`IPTester` instances for all IPython
     modules and package and then runs each of them.  This causes the modules
     and packages of IPython to be tested each in their own subprocess using
     nose or twisted.trial appropriately.
     """
 
     runners = make_runners()
 
     # Run the test runners in a temporary dir so we can nuke it when finished
     # to clean up any junk files left over by accident.  This also makes it
     # robust against being run in non-writeable directories by mistake, as the
     # temp dir will always be user-writeable.
     curdir = os.getcwd()
     testdir = tempfile.gettempdir()
     os.chdir(testdir)
 
     # Run all test runners, tracking execution time
     failed = []
     t_start = time.time()
     try:
         for (name, runner) in runners:
             print '*'*70
             print 'IPython test group:',name
             res = runner.run()
             if res:
                 failed.append( (name, runner) )
     finally:
         os.chdir(curdir)
     t_end = time.time()
     t_tests = t_end - t_start
     nrunners = len(runners)
     nfail = len(failed)
     # summarize results
     print
     print '*'*70
     print 'Test suite completed for system with the following information:'
     print report()
     print 'Ran %s test groups in %.3fs' % (nrunners, t_tests)
     print
     print 'Status:'
     if not failed:
         print 'OK'
     else:
         # If anything went wrong, point out what command to rerun manually to
         # see the actual errors and individual summary
         print 'ERROR - %s out of %s test groups failed.' % (nfail, nrunners)
         for name, failed_runner in failed:
             print '-'*40
             print 'Runner failed:',name
             print 'You may wish to rerun this one individually, with:'
             print ' '.join(failed_runner.call_args)
             print
 
 
 def main():
     for arg in sys.argv[1:]:
         if arg.startswith('IPython'):
             # This is in-process
             run_iptest()
     else:
         # This starts subprocesses
         run_iptestall()
 
 
 if __name__ == '__main__':
     main()

docs/source/index.txt

@@ -1,34 +1,33 b''
 =====================
 IPython Documentation
 =====================
 
 .. htmlonly::
 
    :Release: |release|
    :Date: |today|
 
 Welcome to the official IPython documentation.
 
 Contents
 ========
 
 .. toctree::
    :maxdepth: 1
 
    overview.txt
    whatsnew/index.txt
    install/index.txt
    interactive/index.txt
-   .. parallel/index.txt
-   parallelz/index.txt
+   parallel/index.txt
    config/index.txt
    development/index.txt
    api/index.txt
    faq.txt
    about/index.txt
 
 .. htmlonly::
    * :ref:`genindex`
    * :ref:`modindex`
    * :ref:`search`
 

docs/source/install/install.txt

@@ -1,387 +1,328 b''
 Overview
 ========
 
 This document describes the steps required to install IPython.  IPython is
 organized into a number of subpackages, each of which has its own dependencies.
 All of the subpackages come with IPython, so you don't need to download and
 install them separately.  However, to use a given subpackage, you will need to
 install all of its dependencies.
 
 
 Please let us know if you have problems installing IPython or any of its
-dependencies. Officially, IPython requires Python version 2.5 or 2.6.  We
-have *not* yet started to port IPython to Python 3.0.
+dependencies. Officially, IPython requires Python version 2.6 or 2.7.  There
+is an experimental port of IPython for Python3 `on GitHub 
+<https://github.com/ipython/ipython-py3k>`_
 
 .. warning::
 
-    Officially, IPython supports Python versions 2.5 and 2.6.
+    Officially, IPython supports Python versions 2.6 and 2.7.
 
-    IPython 0.10 has only been well tested with Python 2.5 and 2.6.  Parts of
-    it may work with Python 2.4, but we do not officially support Python 2.4
-    anymore.  If you need to use 2.4, you can still run IPython 0.9.
+    IPython 0.11 has a hard syntax dependency on 2.6, and will no longer work
+    on Python <= 2.5.
 
 Some of the installation approaches use the :mod:`setuptools` package and its
 :command:`easy_install` command line program.  In many scenarios, this provides
 the most simple method of installing IPython and its dependencies.  It is not
 required though.  More information about :mod:`setuptools` can be found on its
 website.
 
 More general information about installing Python packages can be found in
 Python's documentation at http://www.python.org/doc/.
 
 Quickstart
 ==========
 
 If you have :mod:`setuptools` installed and you are on OS X or Linux (not
 Windows), the following will download and install IPython *and* the main
 optional dependencies:
 
 .. code-block:: bash
 
-    $ easy_install ipython[kernel,security,test]
+    $ easy_install ipython[zmq,test]
 
-This will get Twisted, zope.interface and Foolscap, which are needed for
+This will get pyzmq, which is needed for
 IPython's parallel computing features as well as the nose package, which will
 enable you to run IPython's test suite.  
 
 .. warning::
 
     IPython's test system is being refactored and currently the
     :command:`iptest` shown below does not work. More details about the
     testing situation can be found :ref:`here <testing>`
 
 To run IPython's test suite, use the :command:`iptest` command:
 
 .. code-block:: bash
 
     $ iptest
 
 Read on for more specific details and instructions for Windows.
 
 Installing IPython itself
 =========================
 
 Given a properly built Python, the basic interactive IPython shell will work
 with no external dependencies.  However, some Python distributions
 (particularly on Windows and OS X), don't come with a working :mod:`readline`
 module.  The IPython shell will work without :mod:`readline`, but will lack
 many features that users depend on, such as tab completion and command line
 editing.  See below for details of how to make sure you have a working
 :mod:`readline`.
 
 Installation using easy_install
 -------------------------------
 
 If you have :mod:`setuptools` installed, the easiest way of getting IPython is
 to simple use :command:`easy_install`:
 
 .. code-block:: bash
 
     $ easy_install ipython
 
 That's it.
 
 Installation from source
 ------------------------
 
 If you don't want to use :command:`easy_install`, or don't have it installed,
 just grab the latest stable build of IPython from `here
 <http://ipython.scipy.org/dist/>`_.  Then do the following:
 
 .. code-block:: bash
 
     $ tar -xzf ipython.tar.gz
     $ cd ipython
     $ python setup.py install
 
 If you are installing to a location (like ``/usr/local``) that requires higher
 permissions, you may need to run the last command with :command:`sudo`.
 
 Windows
 -------
 
 There are a few caveats for Windows users.  The main issue is that a basic
 ``python setup.py install`` approach won't create ``.bat`` file or Start Menu
 shortcuts, which most users want.  To get an installation with these, you can
 use any of the following alternatives:
 
 1. Install using :command:`easy_install`.
 
 2. Install using our binary ``.exe`` Windows installer, which can be found at
    `here <http://ipython.scipy.org/dist/>`_
 
 3. Install from source, but using :mod:`setuptools` (``python setupegg.py
    install``).
 
 IPython by default runs in a terminal window, but the normal terminal
 application supplied by Microsoft Windows is very primitive.  You may want to
 download the excellent and free Console_ application instead, which is a far
 superior tool.  You can even configure Console to give you by default an
 IPython tab, which is very convenient to create new IPython sessions directly
 from the working terminal.
 
 .. _Console:  http://sourceforge.net/projects/console
 
 Note for Windows 64 bit users: you may have difficulties with the stock
 installer on 64 bit systems; in this case (since we currently do not have 64
 bit builds of the Windows installer) your best bet is to install from source
 with the setuptools method indicated in #3 above.  See `this bug report`_ for
 further details.
 
 .. _this bug report: https://bugs.launchpad.net/ipython/+bug/382214
 
    
 Installing the development version
 ----------------------------------
 
 It is also possible to install the development version of IPython from our
 `Bazaar <http://bazaar-vcs.org/>`_ source code repository.  To do this you will
 need to have Bazaar installed on your system.  Then just do:
 
 .. code-block:: bash
 
     $ bzr branch lp:ipython
     $ cd ipython
     $ python setup.py install
 
 Again, this last step on Windows won't create ``.bat`` files or Start Menu
 shortcuts, so you will have to use one of the other approaches listed above.
 
 Some users want to be able to follow the development branch as it changes.  If
 you have :mod:`setuptools` installed, this is easy. Simply replace the last
 step by:
 
 .. code-block:: bash
 
     $ python setupegg.py develop
 
 This creates links in the right places and installs the command line script to
 the appropriate places.  Then, if you want to update your IPython at any time,
 just do:
 
 .. code-block:: bash
 
     $ bzr pull
 
 Basic optional dependencies
 ===========================
 
 There are a number of basic optional dependencies that most users will want to
 get.  These are:
 
 * readline (for command line editing, tab completion, etc.)
 * nose (to run the IPython test suite)
 * pexpect (to use things like irunner)
 
 If you are comfortable installing these things yourself, have at it, otherwise
 read on for more details.
 
 readline
 --------
 
 In principle, all Python distributions should come with a working
 :mod:`readline` module.  But, reality is not quite that simple.  There are two
 common situations where you won't have a working :mod:`readline` module:
 
 * If you are using the built-in Python on Mac OS X.
 
 * If you are running Windows, which doesn't have a :mod:`readline` module.
 
 
 On OS X, the built-in Python doesn't not have :mod:`readline` because of
 license issues.  Starting with OS X 10.5 (Leopard), Apple's built-in Python has
 a BSD-licensed not-quite-compatible readline replacement. As of IPython 0.9,
 many of the issues related to the differences between readline and libedit seem
 to have been resolved.  While you may find libedit sufficient, we have
 occasional reports of bugs with it and several developers who use OS X as their
 main environment consider libedit unacceptable for productive, regular use with
 IPython.
 
 Therefore, we *strongly* recommend that on OS X you get the full
 :mod:`readline` module.  We will *not* consider completion/history problems to
 be bugs for IPython if you are using libedit.
 
 To get a working :mod:`readline` module, just do (with :mod:`setuptools`
 installed):
 
 .. code-block:: bash
 
     $ easy_install readline
 
 .. note::
 
     Other Python distributions on OS X (such as fink, MacPorts and the official
     python.org binaries) already have readline installed so you likely don't
     have to do this step.
 
 If needed, the readline egg can be build and installed from source (see the
 wiki page at http://ipython.scipy.org/moin/InstallationOSXLeopard).
 
 On Windows, you will need the PyReadline module. PyReadline is a separate,
 Windows only implementation of readline that uses native Windows calls through
 :mod:`ctypes`. The easiest way of installing PyReadline is you use the binary
 installer available `here <http://ipython.scipy.org/dist/>`_. The :mod:`ctypes`
-module, which comes with Python 2.5 and greater, is required by PyReadline. It
-is available for Python 2.4 at http://python.net/crew/theller/ctypes.
+module, which comes with Python 2.5 and greater, is required by PyReadline.
 
 nose
 ----
 
 To run the IPython test suite you will need the :mod:`nose` package.  Nose
 provides a great way of sniffing out and running all of the IPython tests.  The
 simplest way of getting nose, is to use :command:`easy_install`:
 
 .. code-block:: bash
 
     $ easy_install nose
 
 Another way of getting this is to do:
 
 .. code-block:: bash
 
     $ easy_install ipython[test]
 
 For more installation options, see the `nose website
 <http://somethingaboutorange.com/mrl/projects/nose/>`_.  
 
 .. warning::
 
     As described above, the :command:`iptest` command currently doesn't work.
 
 Once you have nose installed, you can run IPython's test suite using the
 iptest command:
 
 .. code-block:: bash
 
     $ iptest
 
 pexpect
 -------
 
 The `pexpect <http://www.noah.org/wiki/Pexpect>`_ package is used in IPython's
 :command:`irunner` script.  On Unix platforms (including OS X), just do:
 
 .. code-block:: bash
 
     $ easy_install pexpect
 
 Windows users are out of luck as pexpect does not run there.
 
-Dependencies for IPython.kernel (parallel computing)
-====================================================
+Dependencies for IPython.parallel (parallel computing)
+======================================================
 
-The IPython kernel provides a nice architecture for parallel computing.  The
-main focus of this architecture is on interactive parallel computing.  These
-features require a number of additional packages:
+:mod:`IPython.kernel` has been replaced by :mod:`IPython.parallel`, 
+which uses ZeroMQ for all communication.
 
-* zope.interface (yep, we use interfaces)
-* Twisted (asynchronous networking framework)
-* Foolscap (a nice, secure network protocol)
-* pyOpenSSL (security for network connections)
+IPython.parallel provides a nice architecture for parallel computing.  The
+main focus of this architecture is on interactive parallel computing.  These
+features require just one package: pyzmq.  See the next section for pyzmq
+details.
 
 On a Unix style platform (including OS X), if you want to use
 :mod:`setuptools`, you can just do:
 
 .. code-block:: bash
 
-    $ easy_install ipython[kernel]    # the first three
-    $ easy_install ipython[security]  # pyOpenSSL
-
-zope.interface and Twisted
---------------------------
-
-Twisted [Twisted]_ and zope.interface [ZopeInterface]_ are used for networking
-related things. On Unix style platforms (including OS X), the simplest way of
-getting the these is to use :command:`easy_install`:
-
-.. code-block:: bash
-
-    $ easy_install zope.interface
-    $ easy_install Twisted
+    $ easy_install ipython[zmq]    # will include pyzmq
 
-Of course, you can also download the source tarballs from the Twisted website
-[Twisted]_ and the 
-`zope.interface page at PyPI <http://pypi.python.org/pypi/zope.interface>`_ 
-and do the usual ``python setup.py install`` if you prefer.
+Security in IPython.parallel is provided by SSH tunnels.  By default, Linux
+and OSX clients will use the shell ssh command, but on Windows, we also
+support tunneling with paramiko [paramiko]_.
 
-Windows is a bit different. For zope.interface and Twisted, simply get the
-latest binary ``.exe`` installer from the Twisted website. This installer
-includes both zope.interface and Twisted and should just work.
-
-Foolscap
---------
-
-Foolscap [Foolscap]_ uses Twisted to provide a very nice secure RPC protocol that we use to implement our parallel computing features.
-
-On all platforms a simple:
-
-.. code-block:: bash
-
-    $ easy_install foolscap
-
-should work.  You can also download the source tarballs from the `Foolscap
-website <http://foolscap.lothar.com/trac>`_ and do ``python setup.py install``
-if you prefer.
-
-pyOpenSSL
----------
-
-IPython does not work with version 0.7 of pyOpenSSL [pyOpenSSL]_. It is known
-to work with version 0.6 and will likely work with the more recent 0.8 and 0.9
-versions. There are a couple of options for getting this:
-
-1. Most Linux distributions have packages for pyOpenSSL.
-2. The built-in Python 2.5 on OS X 10.5 already has it installed.
-3. There are source tarballs on the pyOpenSSL website.  On Unix-like
-   platforms, these can be built using ``python seutp.py install``.
-4. There is also a binary ``.exe`` Windows installer on the 
-   `pyOpenSSL website <http://pyopenssl.sourceforge.net/>`_.
-
-Dependencies for IPython.frontend (the IPython GUI)
-===================================================
-
-wxPython
---------
-
-Starting with IPython 0.9, IPython has a new :mod:`IPython.frontend` package
-that has a nice wxPython based IPython GUI. As you would expect, this GUI
-requires wxPython. Most Linux distributions have wxPython packages available
-and the built-in Python on OS X comes with wxPython preinstalled. For Windows,
-a binary installer is available on the `wxPython website
-<http://www.wxpython.org/>`_.
-
-Dependencies for IPython.zmq (new parallel)
-===========================================
+Dependencies for IPython.zmq
+============================
 
 pyzmq
 -----
 
 IPython 0.11 introduced some new functionality, including a two-process
 execution model using ZeroMQ for communication [ZeroMQ]_. The Python bindings
 to ZeroMQ are found in the pyzmq project, which is easy_install-able once you
-have ZeroMQ installed. :mod:`IPython.kernel` is also in the process of being
-replaced by :mod:`IPython.zmq.parallel`, which uses ZeroMQ for all
-communication.
+have ZeroMQ installed (or even if you don't).
+
+IPython.zmq depends on pyzmq >= 2.0.10.1, but IPython.parallel requires the more
+recent 2.1.4.  2.1.4 also has binary releases for OSX and Windows, that do not
+require prior installation of libzmq.
 
 Dependencies for ipython-qtconsole (new GUI)
 ============================================
 
 PyQt
 ----
 
 Also with 0.11, a new GUI was added using the work in :mod:`IPython.zmq`,
 which can be launched with ``ipython-qtconsole``. The GUI is built on PyQt ,
 which can be installed from the 
 `PyQt website <http://www.riverbankcomputing.co.uk/>`_.
 
 pygments
 --------
 
-The syntax-highlighting in ``ipython-qtconsole`` is done with the pygments project, which is easy_install-able.
+The syntax-highlighting in ``ipython-qtconsole`` is done with the pygments project, 
+which is easy_install-able.
 
 .. [Twisted] Twisted matrix.  http://twistedmatrix.org
 .. [ZopeInterface] http://pypi.python.org/pypi/zope.interface
 .. [Foolscap] Foolscap network protocol.  http://foolscap.lothar.com/trac
 .. [pyOpenSSL] pyOpenSSL.  http://pyopenssl.sourceforge.net
 .. [ZeroMQ] ZeroMQ.  http://www.zeromq.org
-
+.. [paramiko] paramiko. https://github.com/robey/paramiko

docs/source/parallel/index.txt

@@ -1,12 +1,22 b''
 .. _parallel_index:
 
 ====================================
 Using IPython for parallel computing
 ====================================
 
-The twisted-based :mod:`IPython.kernel` has been removed, in favor of
-the new 0MQ-based :mod:`IPython.parallel`, whose merge into master is imminent.
+.. toctree::
+   :maxdepth: 2
+
+   parallel_intro.txt
+   parallel_process.txt
+   parallel_multiengine.txt
+   parallel_task.txt
+   parallel_mpi.txt
+   parallel_security.txt
+   parallel_winhpc.txt
+   parallel_demos.txt
+   dag_dependencies.txt
+   parallel_details.txt
+   parallel_transition.txt
+
 
-Until that code is merged, it can be found in the `newparallel branch 
-<https://github.com/ipython/ipython/tree/newparallel>`_, and its draft documentation can be
-found `here <http://minrk.github.com/ipython-doc/newparallel>`_.
  No newline at end of file

docs/source/parallel/parallel_demos.txt

@@ -1,284 +1,284 b''
 =================
 Parallel examples
 =================
 
 .. note::
 
     Performance numbers from ``IPython.kernel``, not newparallel.
 
 In this section we describe two more involved examples of using an IPython
 cluster to perform a parallel computation. In these examples, we will be using
 IPython's "pylab" mode, which enables interactive plotting using the
 Matplotlib package. IPython can be started in this mode by typing::
 
     ipython --pylab
 
 at the system command line.
 
 150 million digits of pi
 ========================
 
 In this example we would like to study the distribution of digits in the
 number pi (in base 10). While it is not known if pi is a normal number (a
 number is normal in base 10 if 0-9 occur with equal likelihood) numerical
 investigations suggest that it is. We will begin with a serial calculation on
 10,000 digits of pi and then perform a parallel calculation involving 150
 million digits.
 
 In both the serial and parallel calculation we will be using functions defined
 in the :file:`pidigits.py` file, which is available in the
 :file:`docs/examples/newparallel` directory of the IPython source distribution.
 These functions provide basic facilities for working with the digits of pi and
 can be loaded into IPython by putting :file:`pidigits.py` in your current
 working directory and then doing:
 
 .. sourcecode:: ipython
 
     In [1]: run pidigits.py
 
 Serial calculation
 ------------------
 
 For the serial calculation, we will use `SymPy <http://www.sympy.org>`_ to
 calculate 10,000 digits of pi and then look at the frequencies of the digits
 0-9. Out of 10,000 digits, we expect each digit to occur 1,000 times. While
 SymPy is capable of calculating many more digits of pi, our purpose here is to
 set the stage for the much larger parallel calculation.
 
 In this example, we use two functions from :file:`pidigits.py`: 
 :func:`one_digit_freqs` (which calculates how many times each digit occurs)
 and :func:`plot_one_digit_freqs` (which uses Matplotlib to plot the result).
 Here is an interactive IPython session that uses these functions with
 SymPy:
 
 .. sourcecode:: ipython
 
     In [7]: import sympy
 
     In [8]: pi = sympy.pi.evalf(40)
 
     In [9]: pi
     Out[9]: 3.141592653589793238462643383279502884197
 
     In [10]: pi = sympy.pi.evalf(10000)
 
     In [11]: digits = (d for d in str(pi)[2:])  # create a sequence of digits
 
     In [12]: run pidigits.py  # load one_digit_freqs/plot_one_digit_freqs
 
     In [13]: freqs = one_digit_freqs(digits)
 
     In [14]: plot_one_digit_freqs(freqs)
     Out[14]: [<matplotlib.lines.Line2D object at 0x18a55290>]
 
 The resulting plot of the single digit counts shows that each digit occurs
 approximately 1,000 times, but that with only 10,000 digits the
 statistical fluctuations are still rather large:
 
 .. image:: single_digits.*
 
 It is clear that to reduce the relative fluctuations in the counts, we need
 to look at many more digits of pi. That brings us to the parallel calculation.
 
 Parallel calculation
 --------------------
 
 Calculating many digits of pi is a challenging computational problem in itself. 
 Because we want to focus on the distribution of digits in this example, we
 will use pre-computed digit of pi from the website of Professor Yasumasa
 Kanada at the University of Tokyo (http://www.super-computing.org). These
 digits come in a set of text files (ftp://pi.super-computing.org/.2/pi200m/)
 that each have 10 million digits of pi.
 
 For the parallel calculation, we have copied these files to the local hard
 drives of the compute nodes. A total of 15 of these files will be used, for a
 total of 150 million digits of pi. To make things a little more interesting we
 will calculate the frequencies of all 2 digits sequences (00-99) and then plot
 the result using a 2D matrix in Matplotlib.
 
 The overall idea of the calculation is simple: each IPython engine will
 compute the two digit counts for the digits in a single file. Then in a final
 step the counts from each engine will be added up. To perform this
 calculation, we will need two top-level functions from :file:`pidigits.py`:
 
 .. literalinclude:: ../../examples/newparallel/pidigits.py
    :language: python
    :lines: 41-56
 
 We will also use the :func:`plot_two_digit_freqs` function to plot the
 results. The code to run this calculation in parallel is contained in
 :file:`docs/examples/newparallel/parallelpi.py`. This code can be run in parallel
 using IPython by following these steps:
 
-1. Use :command:`ipclusterz` to start 15 engines. We used an 8 core (2 quad 
+1. Use :command:`ipcluster` to start 15 engines. We used an 8 core (2 quad 
    core CPUs) cluster with hyperthreading enabled which makes the 8 cores 
    looks like 16 (1 controller + 15 engines) in the OS. However, the maximum
    speedup we can observe is still only 8x.
 2. With the file :file:`parallelpi.py` in your current working directory, open
    up IPython in pylab mode and type ``run parallelpi.py``.  This will download
    the pi files via ftp the first time you run it, if they are not
    present in the Engines' working directory.
 
 When run on our 8 core cluster, we observe a speedup of 7.7x. This is slightly
 less than linear scaling (8x) because the controller is also running on one of
 the cores.
 
 To emphasize the interactive nature of IPython, we now show how the
 calculation can also be run by simply typing the commands from
 :file:`parallelpi.py` interactively into IPython:
 
 .. sourcecode:: ipython
 
     In [1]: from IPython.parallel import Client
 
     # The Client allows us to use the engines interactively.
     # We simply pass Client the name of the cluster profile we
     # are using.
     In [2]: c = Client(profile='mycluster')
     In [3]: view = c.load_balanced_view()
 
     In [3]: c.ids
     Out[3]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
 
     In [4]: run pidigits.py
 
     In [5]: filestring = 'pi200m.ascii.%(i)02dof20'
 
     # Create the list of files to process.
     In [6]: files = [filestring % {'i':i} for i in range(1,16)]
 
     In [7]: files
     Out[7]: 
     ['pi200m.ascii.01of20',
      'pi200m.ascii.02of20',
      'pi200m.ascii.03of20',
      'pi200m.ascii.04of20',
      'pi200m.ascii.05of20',
      'pi200m.ascii.06of20',
      'pi200m.ascii.07of20',
      'pi200m.ascii.08of20',
      'pi200m.ascii.09of20',
      'pi200m.ascii.10of20',
      'pi200m.ascii.11of20',
      'pi200m.ascii.12of20',
      'pi200m.ascii.13of20',
      'pi200m.ascii.14of20',
      'pi200m.ascii.15of20']
     
     # download the data files if they don't already exist:
     In [8]: v.map(fetch_pi_file, files)
     
     # This is the parallel calculation using the Client.map method 
     # which applies compute_two_digit_freqs to each file in files in parallel.
     In [9]: freqs_all = v.map(compute_two_digit_freqs, files)
 
     # Add up the frequencies from each engine.
     In [10]: freqs = reduce_freqs(freqs_all)
 
     In [11]: plot_two_digit_freqs(freqs)
     Out[11]: <matplotlib.image.AxesImage object at 0x18beb110>
 
     In [12]: plt.title('2 digit counts of 150m digits of pi')
     Out[12]: <matplotlib.text.Text object at 0x18d1f9b0>
 
 The resulting plot generated by Matplotlib is shown below. The colors indicate
 which two digit sequences are more (red) or less (blue) likely to occur in the
 first 150 million digits of pi. We clearly see that the sequence "41" is 
 most likely and that "06" and "07" are least likely. Further analysis would
 show that the relative size of the statistical fluctuations have decreased
 compared to the 10,000 digit calculation.
 
 .. image:: two_digit_counts.*
 
 
 Parallel options pricing
 ========================
 
 An option is a financial contract that gives the buyer of the contract the
 right to buy (a "call") or sell (a "put") a secondary asset (a stock for
 example) at a particular date in the future (the expiration date) for a
 pre-agreed upon price (the strike price). For this right, the buyer pays the
 seller a premium (the option price). There are a wide variety of flavors of
 options (American, European, Asian, etc.) that are useful for different
 purposes: hedging against risk, speculation, etc.
 
 Much of modern finance is driven by the need to price these contracts
 accurately based on what is known about the properties (such as volatility) of
 the underlying asset. One method of pricing options is to use a Monte Carlo
 simulation of the underlying asset price. In this example we use this approach
 to price both European and Asian (path dependent) options for various strike
 prices and volatilities.
 
 The code for this example can be found in the :file:`docs/examples/newparallel`
 directory of the IPython source. The function :func:`price_options` in
 :file:`mcpricer.py` implements the basic Monte Carlo pricing algorithm using
 the NumPy package and is shown here:
 
 .. literalinclude:: ../../examples/newparallel/mcpricer.py
    :language: python
 
 To run this code in parallel, we will use IPython's :class:`LoadBalancedView` class,
 which distributes work to the engines using dynamic load balancing. This
 view is a wrapper of the :class:`Client` class shown in
 the previous example. The parallel calculation using :class:`LoadBalancedView` can
 be found in the file :file:`mcpricer.py`. The code in this file creates a
 :class:`TaskClient` instance and then submits a set of tasks using
 :meth:`TaskClient.run` that calculate the option prices for different
 volatilities and strike prices. The results are then plotted as a 2D contour
 plot using Matplotlib.
 
 .. literalinclude:: ../../examples/newparallel/mcdriver.py
    :language: python
 
-To use this code, start an IPython cluster using :command:`ipclusterz`, open
+To use this code, start an IPython cluster using :command:`ipcluster`, open
 IPython in the pylab mode with the file :file:`mcdriver.py` in your current
 working directory and then type:
 
 .. sourcecode:: ipython
 
     In [7]: run mcdriver.py
     Submitted tasks:  [0, 1, 2, ...]
 
 Once all the tasks have finished, the results can be plotted using the
 :func:`plot_options` function. Here we make contour plots of the Asian
 call and Asian put options as function of the volatility and strike price:
 
 .. sourcecode:: ipython
 
     In [8]: plot_options(sigma_vals, K_vals, prices['acall'])
 
     In [9]: plt.figure()
     Out[9]: <matplotlib.figure.Figure object at 0x18c178d0>
 
     In [10]: plot_options(sigma_vals, K_vals, prices['aput'])
 
 These results are shown in the two figures below. On a 8 core cluster the
 entire calculation (10 strike prices, 10 volatilities, 100,000 paths for each)
 took 30 seconds in parallel, giving a speedup of 7.7x, which is comparable
 to the speedup observed in our previous example.
 
 .. image:: asian_call.*
 
 .. image:: asian_put.*
 
 Conclusion
 ==========
 
 To conclude these examples, we summarize the key features of IPython's
 parallel architecture that have been demonstrated:
 
 * Serial code can be parallelized often with only a few extra lines of code.
   We have used the :class:`DirectView` and :class:`LoadBalancedView` classes
   for this purpose.
 * The resulting parallel code can be run without ever leaving the IPython's
   interactive shell.
 * Any data computed in parallel can be explored interactively through 
   visualization or further numerical calculations.
 * We have run these examples on a cluster running Windows HPC Server 2008.
   IPython's built in support for the Windows HPC job scheduler makes it
   easy to get started with IPython's parallel capabilities.
 
 .. note::
 
     The newparallel code has never been run on Windows HPC Server, so the last
     conclusion is untested.

docs/source/parallel/parallel_intro.txt

@@ -1,253 +1,253 b''
 .. _ip1par:
 
 ============================
 Overview and getting started
 ============================
 
 Introduction
 ============
 
 This section gives an overview of IPython's sophisticated and powerful
 architecture for parallel and distributed computing. This architecture
 abstracts out parallelism in a very general way, which enables IPython to
 support many different styles of parallelism including:
 
 * Single program, multiple data (SPMD) parallelism.
 * Multiple program, multiple data (MPMD) parallelism.
 * Message passing using MPI.
 * Task farming.
 * Data parallel.
 * Combinations of these approaches.
 * Custom user defined approaches.
 
 Most importantly, IPython enables all types of parallel applications to
 be developed, executed, debugged and monitored *interactively*. Hence,
 the ``I`` in IPython.  The following are some example usage cases for IPython:
 
 * Quickly parallelize algorithms that are embarrassingly parallel
   using a number of simple approaches.  Many simple things can be
   parallelized interactively in one or two lines of code.
 
 * Steer traditional MPI applications on a supercomputer from an
   IPython session on your laptop.
 
 * Analyze and visualize large datasets (that could be remote and/or
   distributed) interactively using IPython and tools like
   matplotlib/TVTK.
 
 * Develop, test and debug new parallel algorithms 
   (that may use MPI) interactively.
 
 * Tie together multiple MPI jobs running on different systems into
   one giant distributed and parallel system.
 
 * Start a parallel job on your cluster and then have a remote 	
   collaborator connect to it and pull back data into their 
   local IPython session for plotting and analysis.
 
 * Run a set of tasks on a set of CPUs using dynamic load balancing.
 
 Architecture overview
 =====================
 
 The IPython architecture consists of four components:
 
 * The IPython engine.
 * The IPython hub.
 * The IPython schedulers.
 * The controller client.
 
 These components live in the :mod:`IPython.parallel` package and are
 installed with IPython.  They do, however, have additional dependencies
 that must be installed.  For more information, see our
 :ref:`installation documentation <install_index>`.
 
 .. TODO: include zmq in install_index
 
 IPython engine
 ---------------
 
 The IPython engine is a Python instance that takes Python commands over a
 network connection. Eventually, the IPython engine will be a full IPython
 interpreter, but for now, it is a regular Python interpreter. The engine
 can also handle incoming and outgoing Python objects sent over a network
 connection.  When multiple engines are started, parallel and distributed
 computing becomes possible. An important feature of an IPython engine is
 that it blocks while user code is being executed. Read on for how the
 IPython controller solves this problem to expose a clean asynchronous API
 to the user.
 
 IPython controller
 ------------------
 
 The IPython controller processes provide an interface for working with a set of engines.
 At a general level, the controller is a collection of processes to which IPython engines
 and clients can connect. The controller is composed of a :class:`Hub` and a collection of
 :class:`Schedulers`. These Schedulers are typically run in separate processes but on the
 same machine as the Hub, but can be run anywhere from local threads or on remote machines.
 
 The controller also provides a single point of contact for users who wish to
 utilize the engines connected to the controller. There are different ways of
 working with a controller. In IPython, all of these models are implemented via
 the client's :meth:`.View.apply` method, with various arguments, or
 constructing :class:`.View` objects to represent subsets of engines. The two
 primary models for interacting with engines are:
 
 * A **Direct** interface, where engines are addressed explicitly.
 * A **LoadBalanced** interface, where the Scheduler is trusted with assigning work to
   appropriate engines.
 
 Advanced users can readily extend the View models to enable other
 styles of parallelism. 
 
 .. note:: 
 
     A single controller and set of engines can be used with multiple models
     simultaneously. This opens the door for lots of interesting things.
 
 
 The Hub
 *******
 
 The center of an IPython cluster is the Hub. This is the process that keeps
 track of engine connections, schedulers, clients, as well as all task requests and
 results. The primary role of the Hub is to facilitate queries of the cluster state, and
 minimize the necessary information required to establish the many connections involved in
 connecting new clients and engines.
 
 
 Schedulers
 **********
 
 All actions that can be performed on the engine go through a Scheduler. While the engines
 themselves block when user code is run, the schedulers hide that from the user to provide
 a fully asynchronous interface to a set of engines.
 
 
 IPython client and views
 ------------------------
 
 There is one primary object, the :class:`~.parallel.Client`, for connecting to a cluster.
 For each execution model, there is a corresponding :class:`~.parallel.view.View`. These views
 allow users to interact with a set of engines through the interface. Here are the two default
 views:
 
 * The :class:`DirectView` class for explicit addressing.
 * The :class:`LoadBalancedView` class for destination-agnostic scheduling.
 
 Security
 --------
 
 IPython uses ZeroMQ for networking, which has provided many advantages, but
 one of the setbacks is its utter lack of security [ZeroMQ]_. By default, no IPython
 connections are encrypted, but open ports only listen on localhost. The only
 source of security for IPython is via ssh-tunnel. IPython supports both shell
 (`openssh`) and `paramiko` based tunnels for connections.  There is a key necessary
 to submit requests, but due to the lack of encryption, it does not provide
 significant security if loopback traffic is compromised.
 
 In our architecture, the controller is the only process that listens on
 network ports, and is thus the main point of vulnerability. The standard model
 for secure connections is to designate that the controller listen on
 localhost, and use ssh-tunnels to connect clients and/or
 engines.
 
 To connect and authenticate to the controller an engine or client needs
 some information that the controller has stored in a JSON file.
 Thus, the JSON files need to be copied to a location where
 the clients and engines can find them. Typically, this is the
-:file:`~/.ipython/clusterz_default/security` directory on the host where the 
+:file:`~/.ipython/cluster_default/security` directory on the host where the 
 client/engine is running (which could be a different host than the controller). 
 Once the JSON files are copied over, everything should work fine.
 
 Currently, there are two JSON files that the controller creates:
 
 ipcontroller-engine.json
     This JSON file has the information necessary for an engine to connect
     to a controller.
 
 ipcontroller-client.json
     The client's connection information.  This may not differ from the engine's,
     but since the controller may listen on different ports for clients and
     engines, it is stored separately.
 
 More details of how these JSON files are used are given below.
 
 A detailed description of the security model and its implementation in IPython
 can be found :ref:`here <parallelsecurity>`.
 
 .. warning::
 
     Even at its most secure, the Controller listens on ports on localhost, and
     every time you make a tunnel, you open a localhost port on the connecting
     machine that points to the Controller. If localhost on the Controller's
     machine, or the machine of any client or engine, is untrusted, then your
     Controller is insecure. There is no way around this with ZeroMQ.
 
 
 
 Getting Started
 ===============
 
 To use IPython for parallel computing, you need to start one instance of the
 controller and one or more instances of the engine. Initially, it is best to
 simply start a controller and engines on a single host using the
-:command:`ipclusterz` command. To start a controller and 4 engines on your
+:command:`ipcluster` command. To start a controller and 4 engines on your
 localhost, just do::
 
-    $ ipclusterz start -n 4
+    $ ipcluster start -n 4
 
 More details about starting the IPython controller and engines can be found
 :ref:`here <parallel_process>`
 
 Once you have started the IPython controller and one or more engines, you
 are ready to use the engines to do something useful. To make sure
 everything is working correctly, try the following commands:
 
 .. sourcecode:: ipython
 
 	In [1]: from IPython.parallel import Client
 	
 	In [2]: c = Client()
 	
 	In [4]: c.ids
 	Out[4]: set([0, 1, 2, 3])
 	
 	In [5]: c[:].apply_sync(lambda : "Hello, World")
 	Out[5]: [ 'Hello, World', 'Hello, World', 'Hello, World', 'Hello, World' ]
 
 
 When a client is created with no arguments, the client tries to find the corresponding JSON file
-in the local `~/.ipython/clusterz_default/security` directory. Or if you specified a profile,
+in the local `~/.ipython/cluster_default/security` directory. Or if you specified a profile,
 you can use that with the Client.  This should cover most cases:
 
 .. sourcecode:: ipython
 
     In [2]: c = Client(profile='myprofile')
 
 If you have put the JSON file in a different location or it has a different name, create the
 client like this:
 
 .. sourcecode:: ipython
 
     In [2]: c = Client('/path/to/my/ipcontroller-client.json')
 
 Remember, a client needs to be able to see the Hub's ports to connect. So if they are on a
 different machine, you may need to use an ssh server to tunnel access to that machine,
 then you would connect to it with:
 
 .. sourcecode:: ipython
 
     In [2]: c = Client(sshserver='myhub.example.com')
 
 Where 'myhub.example.com' is the url or IP address of the machine on
 which the Hub process is running (or another machine that has direct access to the Hub's ports).
 
 The SSH server may already be specified in ipcontroller-client.json, if the controller was
 instructed at its launch time.
 
 You are now ready to learn more about the :ref:`Direct
 <parallel_multiengine>` and :ref:`LoadBalanced <parallel_task>` interfaces to the
 controller.
 
 .. [ZeroMQ] ZeroMQ.  http://www.zeromq.org

docs/source/parallel/parallel_mpi.txt

@@ -1,156 +1,156 b''
 .. _parallelmpi:
 
 =======================
 Using MPI with IPython
 =======================
 
 .. note::
 
     Not adapted to zmq yet
     This is out of date wrt ipcluster in general as well
 
 Often, a parallel algorithm will require moving data between the engines. One
 way of accomplishing this is by doing a pull and then a push using the
 multiengine client. However, this will be slow as all the data has to go
 through the controller to the client and then back through the controller, to
 its final destination.
 
 A much better way of moving data between engines is to use a message passing
 library, such as the Message Passing Interface (MPI) [MPI]_. IPython's
 parallel computing architecture has been designed from the ground up to
 integrate with MPI. This document describes how to use MPI with IPython.
 
 Additional installation requirements
 ====================================
 
 If you want to use MPI with IPython, you will need to install:
 
 * A standard MPI implementation such as OpenMPI [OpenMPI]_ or MPICH.
 * The mpi4py [mpi4py]_ package.
 
 .. note::
 
     The mpi4py package is not a strict requirement. However, you need to
     have *some* way of calling MPI from Python. You also need some way of
     making sure that :func:`MPI_Init` is called when the IPython engines start
     up. There are a number of ways of doing this and a good number of
     associated subtleties. We highly recommend just using mpi4py as it
     takes care of most of these problems. If you want to do something
     different, let us know and we can help you get started.
 
 Starting the engines with MPI enabled
 =====================================
 
 To use code that calls MPI, there are typically two things that MPI requires.
 
 1. The process that wants to call MPI must be started using
    :command:`mpiexec` or a batch system (like PBS) that has MPI support.
 2. Once the process starts, it must call :func:`MPI_Init`.
 
 There are a couple of ways that you can start the IPython engines and get
 these things to happen.
 
-Automatic starting using :command:`mpiexec` and :command:`ipclusterz`
+Automatic starting using :command:`mpiexec` and :command:`ipcluster`
 --------------------------------------------------------------------
 
-The easiest approach is to use the `mpiexec` mode of :command:`ipclusterz`,
+The easiest approach is to use the `mpiexec` mode of :command:`ipcluster`,
 which will first start a controller and then a set of engines using
 :command:`mpiexec`::
 
-    $ ipclusterz mpiexec -n 4
+    $ ipcluster mpiexec -n 4
 
-This approach is best as interrupting :command:`ipclusterz` will automatically
+This approach is best as interrupting :command:`ipcluster` will automatically
 stop and clean up the controller and engines.
 
 Manual starting using :command:`mpiexec`
 ----------------------------------------
 
 If you want to start the IPython engines using the :command:`mpiexec`, just
 do::
 
-    $ mpiexec -n 4 ipenginez --mpi=mpi4py
+    $ mpiexec -n 4 ipengine --mpi=mpi4py
 
 This requires that you already have a controller running and that the FURL
 files for the engines are in place. We also have built in support for
 PyTrilinos [PyTrilinos]_, which can be used (assuming is installed) by
 starting the engines with::
 
-    $ mpiexec -n 4 ipenginez --mpi=pytrilinos
+    $ mpiexec -n 4 ipengine --mpi=pytrilinos
 
-Automatic starting using PBS and :command:`ipclusterz`
+Automatic starting using PBS and :command:`ipcluster`
 ------------------------------------------------------
 
-The :command:`ipclusterz` command also has built-in integration with PBS. For
-more information on this approach, see our documentation on :ref:`ipclusterz
+The :command:`ipcluster` command also has built-in integration with PBS. For
+more information on this approach, see our documentation on :ref:`ipcluster
 <parallel_process>`.
 
 Actually using MPI
 ==================
 
 Once the engines are running with MPI enabled, you are ready to go. You can
 now call any code that uses MPI in the IPython engines. And, all of this can
 be done interactively. Here we show a simple example that uses mpi4py
 [mpi4py]_ version 1.1.0 or later.
 
 First, lets define a simply function that uses MPI to calculate the sum of a
 distributed array. Save the following text in a file called :file:`psum.py`:
 
 .. sourcecode:: python
 
     from mpi4py import MPI
     import numpy as np
 
     def psum(a):
         s = np.sum(a)
         rcvBuf = np.array(0.0,'d')
         MPI.COMM_WORLD.Allreduce([s, MPI.DOUBLE],
             [rcvBuf, MPI.DOUBLE],
             op=MPI.SUM)
         return rcvBuf
 
 Now, start an IPython cluster::
 
-    $ ipclusterz start -p mpi -n 4
+    $ ipcluster start -p mpi -n 4
     
 .. note::
 
     It is assumed here that the mpi profile has been set up, as described :ref:`here
     <parallel_process>`.
 
 Finally, connect to the cluster and use this function interactively. In this
 case, we create a random array on each engine and sum up all the random arrays
 using our :func:`psum` function:
 
 .. sourcecode:: ipython
 
     In [1]: from IPython.parallel import Client
     
     In [2]: %load_ext parallel_magic
     
     In [3]: c = Client(profile='mpi')
     
     In [4]: view = c[:]
     
     In [5]: view.activate()
     
     # run the contents of the file on each engine:
     In [6]: view.run('psum.py')
     
     In [6]: px a = np.random.rand(100)
     Parallel execution on engines: [0,1,2,3]
     
     In [8]: px s = psum(a)
     Parallel execution on engines: [0,1,2,3]
     
     In [9]: view['s']
     Out[9]: [187.451545803,187.451545803,187.451545803,187.451545803]
 
 Any Python code that makes calls to MPI can be used in this manner, including
 compiled C, C++ and Fortran libraries that have been exposed to Python.
 
 .. [MPI] Message Passing Interface.  http://www-unix.mcs.anl.gov/mpi/
 .. [mpi4py] MPI for Python. mpi4py: http://mpi4py.scipy.org/
 .. [OpenMPI] Open MPI. http://www.open-mpi.org/
 .. [PyTrilinos] PyTrilinos. http://trilinos.sandia.gov/packages/pytrilinos/
 
 

docs/source/parallel/parallel_multiengine.txt

@@ -1,843 +1,843 b''
 .. _parallel_multiengine:
 
 ==========================
 IPython's Direct interface
 ==========================
 
 The direct, or multiengine, interface represents one possible way of working with a set of
 IPython engines. The basic idea behind the multiengine interface is that the
 capabilities of each engine are directly and explicitly exposed to the user.
 Thus, in the multiengine interface, each engine is given an id that is used to
 identify the engine and give it work to do. This interface is very intuitive
 and is designed with interactive usage in mind, and is the best place for
 new users of IPython to begin.
 
 Starting the IPython controller and engines
 ===========================================
 
 To follow along with this tutorial, you will need to start the IPython
 controller and four IPython engines. The simplest way of doing this is to use
-the :command:`ipclusterz` command::
+the :command:`ipcluster` command::
 
-    $ ipclusterz start -n 4
+    $ ipcluster start -n 4
     
 For more detailed information about starting the controller and engines, see
 our :ref:`introduction <ip1par>` to using IPython for parallel computing.
 
 Creating a ``Client`` instance
 ==============================
 
 The first step is to import the IPython :mod:`IPython.parallel`
 module and then create a :class:`.Client` instance:
 
 .. sourcecode:: ipython
 
     In [1]: from IPython.parallel import Client
     
     In [2]: rc = Client()
 
 This form assumes that the default connection information (stored in
-:file:`ipcontroller-client.json` found in :file:`IPYTHON_DIR/clusterz_default/security`) is
+:file:`ipcontroller-client.json` found in :file:`IPYTHON_DIR/cluster_default/security`) is
 accurate. If the controller was started on a remote machine, you must copy that connection
 file to the client machine, or enter its contents as arguments to the Client constructor:
 
 .. sourcecode:: ipython
 
     # If you have copied the json connector file from the controller:
     In [2]: rc = Client('/path/to/ipcontroller-client.json')
     # or to connect with a specific profile you have set up:
     In [3]: rc = Client(profile='mpi')
     
 
 To make sure there are engines connected to the controller, users can get a list
 of engine ids:
 
 .. sourcecode:: ipython
 
     In [3]: rc.ids
     Out[3]: [0, 1, 2, 3]
 
 Here we see that there are four engines ready to do work for us.
 
 For direct execution, we will make use of a :class:`DirectView` object, which can be
 constructed via list-access to the client:
 
 .. sourcecode:: ipython
 
     In [4]: dview = rc[:] # use all engines
 
 .. seealso::
 
     For more information, see the in-depth explanation of :ref:`Views <parallel_details>`.
 
 
 Quick and easy parallelism
 ==========================
 
 In many cases, you simply want to apply a Python function to a sequence of
 objects, but *in parallel*. The client interface provides a simple way
 of accomplishing this: using the DirectView's :meth:`~DirectView.map` method.
 
 Parallel map
 ------------
 
 Python's builtin :func:`map` functions allows a function to be applied to a
 sequence element-by-element. This type of code is typically trivial to
 parallelize. In fact, since IPython's interface is all about functions anyway,
 you can just use the builtin :func:`map` with a :class:`RemoteFunction`, or a 
 DirectView's :meth:`map` method:
 
 .. sourcecode:: ipython
 
     In [62]: serial_result = map(lambda x:x**10, range(32))
     
     In [63]: parallel_result = dview.map_sync(lambda x: x**10, range(32))
 
     In [67]: serial_result==parallel_result
     Out[67]: True
 
 
 .. note::
 
     The :class:`DirectView`'s version of :meth:`map` does
     not do dynamic load balancing. For a load balanced version, use a
     :class:`LoadBalancedView`.
 
 .. seealso::
     
     :meth:`map` is implemented via :class:`ParallelFunction`.
 
 Remote function decorators
 --------------------------
 
 Remote functions are just like normal functions, but when they are called,
 they execute on one or more engines, rather than locally. IPython provides
 two decorators:
 
 .. sourcecode:: ipython
 
     In [10]: @dview.remote(block=True)
         ...: def getpid():
         ...:     import os
         ...:     return os.getpid()
         ...: 
 
     In [11]: getpid()
     Out[11]: [12345, 12346, 12347, 12348]
 
 The ``@parallel`` decorator creates parallel functions, that break up an element-wise
 operations and distribute them, reconstructing the result.
 
 .. sourcecode:: ipython
 
     In [12]: import numpy as np
     
     In [13]: A = np.random.random((64,48))
     
     In [14]: @dview.parallel(block=True)
         ...: def pmul(A,B):
         ...:     return A*B
     
     In [15]: C_local = A*A
     
     In [16]: C_remote = pmul(A,A)
     
     In [17]: (C_local == C_remote).all()
     Out[17]: True
 
 .. seealso::
 
     See the docstrings for the :func:`parallel` and :func:`remote` decorators for
     options.
 
 Calling Python functions
 ========================
 
 The most basic type of operation that can be performed on the engines is to
 execute Python code or call Python functions. Executing Python code can be
 done in blocking or non-blocking mode (non-blocking is default) using the
 :meth:`.View.execute` method, and calling functions can be done via the
 :meth:`.View.apply` method.
 
 apply
 -----
 
 The main method for doing remote execution (in fact, all methods that
 communicate with the engines are built on top of it), is :meth:`View.apply`.
 
 We strive to provide the cleanest interface we can, so `apply` has the following
 signature:
 
 .. sourcecode:: python
 
     view.apply(f, *args, **kwargs)
 
 There are various ways to call functions with IPython, and these flags are set as
 attributes of the View.  The ``DirectView`` has just two of these flags:
 
 dv.block : bool
     whether to wait for the result, or return an :class:`AsyncResult` object
     immediately
 dv.track : bool
     whether to instruct pyzmq to track when 
     This is primarily useful for non-copying sends of numpy arrays that you plan to
     edit in-place.  You need to know when it becomes safe to edit the buffer
     without corrupting the message.
 
 
 Creating a view is simple: index-access on a client creates a :class:`.DirectView`.
 
 .. sourcecode:: ipython
 
     In [4]: view = rc[1:3]
     Out[4]: <DirectView [1, 2]>
 
     In [5]: view.apply<tab>
     view.apply  view.apply_async  view.apply_sync
 
 For convenience, you can set block temporarily for a single call with the extra sync/async methods.
 
 Blocking execution
 ------------------
 
 In blocking mode, the :class:`.DirectView` object (called ``dview`` in
 these examples) submits the command to the controller, which places the
 command in the engines' queues for execution. The :meth:`apply` call then
 blocks until the engines are done executing the command:
 
 .. sourcecode:: ipython
 
     In [2]: dview = rc[:] # A DirectView of all engines
     In [3]: dview.block=True
     In [4]: dview['a'] = 5
 
     In [5]: dview['b'] = 10
 
     In [6]: dview.apply(lambda x: a+b+x, 27)
     Out[6]: [42, 42, 42, 42]
 
 You can also select blocking execution on a call-by-call basis with the :meth:`apply_sync`
 method:
 
     In [7]: dview.block=False
 
     In [8]: dview.apply_sync(lambda x: a+b+x, 27)
     Out[8]: [42, 42, 42, 42]
 
 Python commands can be executed as strings on specific engines by using a View's ``execute``
 method:
 
 .. sourcecode:: ipython
 
     In [6]: rc[::2].execute('c=a+b')
 
     In [7]: rc[1::2].execute('c=a-b')
 
     In [8]: dview['c'] # shorthand for dview.pull('c', block=True)
     Out[8]: [15, -5, 15, -5]
 
 
 Non-blocking execution
 ----------------------
 
 In non-blocking mode, :meth:`apply` submits the command to be executed and
 then returns a :class:`AsyncResult` object immediately. The
 :class:`AsyncResult` object gives you a way of getting a result at a later
 time through its :meth:`get` method.
 
 .. Note::
 
     The :class:`AsyncResult` object provides a superset of the interface in 
     :py:class:`multiprocessing.pool.AsyncResult`.  See the 
     `official Python documentation <http://docs.python.org/library/multiprocessing#multiprocessing.pool.AsyncResult>`_
     for more.
 
 
 This allows you to quickly submit long running commands without blocking your
 local Python/IPython session:
 
 .. sourcecode:: ipython
     
     # define our function
     In [6]: def wait(t):
        ...:     import time
        ...:     tic = time.time()
        ...:     time.sleep(t)
        ...:     return time.time()-tic
     
     # In non-blocking mode
     In [7]: ar = dview.apply_async(wait, 2)
 
     # Now block for the result
     In [8]: ar.get()
     Out[8]: [2.0006198883056641, 1.9997570514678955, 1.9996809959411621, 2.0003249645233154]
 
     # Again in non-blocking mode
     In [9]: ar = dview.apply_async(wait, 10)
 
     # Poll to see if the result is ready
     In [10]: ar.ready()
     Out[10]: False
     
     # ask for the result, but wait a maximum of 1 second:
     In [45]: ar.get(1)
     ---------------------------------------------------------------------------
     TimeoutError                              Traceback (most recent call last)
     /home/you/<ipython-input-45-7cd858bbb8e0> in <module>()
     ----> 1 ar.get(1)
 
     /path/to/site-packages/IPython/parallel/asyncresult.pyc in get(self, timeout)
          62                 raise self._exception
          63         else:
     ---> 64             raise error.TimeoutError("Result not ready.")
          65 
          66     def ready(self):
 
     TimeoutError: Result not ready.
 
 .. Note::
 
     Note the import inside the function. This is a common model, to ensure
     that the appropriate modules are imported where the task is run. You can
     also manually import modules into the engine(s) namespace(s) via 
     :meth:`view.execute('import numpy')`.
 
 Often, it is desirable to wait until a set of :class:`AsyncResult` objects
 are done. For this, there is a the method :meth:`wait`. This method takes a
 tuple of :class:`AsyncResult` objects (or `msg_ids` or indices to the client's History),
 and blocks until all of the associated results are ready:
 
 .. sourcecode:: ipython
 
     In [72]: dview.block=False
 
     # A trivial list of AsyncResults objects
     In [73]: pr_list = [dview.apply_async(wait, 3) for i in range(10)]
 
     # Wait until all of them are done
     In [74]: dview.wait(pr_list)
 
     # Then, their results are ready using get() or the `.r` attribute
     In [75]: pr_list[0].get()
     Out[75]: [2.9982571601867676, 2.9982588291168213, 2.9987530708312988, 2.9990990161895752]
 
 
 
 The ``block`` and ``targets`` keyword arguments and attributes
 --------------------------------------------------------------
 
 Most DirectView methods (excluding :meth:`apply` and :meth:`map`) accept ``block`` and
 ``targets`` as keyword arguments. As we have seen above, these keyword arguments control the
 blocking mode and which engines the command is applied to. The :class:`View` class also has
 :attr:`block` and :attr:`targets` attributes that control the default behavior when the keyword
 arguments are not provided. Thus the following logic is used for :attr:`block` and :attr:`targets`:
 
 * If no keyword argument is provided, the instance attributes are used.
 * Keyword argument, if provided override the instance attributes for
   the duration of a single call.
   
 The following examples demonstrate how to use the instance attributes:
 
 .. sourcecode:: ipython
 
     In [16]: dview.targets = [0,2]
     
     In [17]: dview.block = False
 
     In [18]: ar = dview.apply(lambda : 10)
 
     In [19]: ar.get()
     Out[19]: [10, 10]
 
     In [16]: dview.targets = v.client.ids # all engines (4)
     
     In [21]: dview.block = True
 
     In [22]: dview.apply(lambda : 42)
     Out[22]: [42, 42, 42, 42]
 
 The :attr:`block` and :attr:`targets` instance attributes of the
 :class:`.DirectView` also determine the behavior of the parallel magic commands.
 
 Parallel magic commands
 -----------------------
 
 .. warning::
 
     The magics have not been changed to work with the zeromq system. The
     magics do work, but *do not* print stdin/out like they used to in IPython.kernel.
 
 We provide a few IPython magic commands (``%px``, ``%autopx`` and ``%result``)
 that make it more pleasant to execute Python commands on the engines
 interactively. These are simply shortcuts to :meth:`execute` and
 :meth:`get_result` of the :class:`DirectView`. The ``%px`` magic executes a single 
 Python command on the engines specified by the :attr:`targets` attribute of the
 :class:`DirectView` instance:
 
 .. sourcecode:: ipython
 
     # load the parallel magic extension:
     In [21]: %load_ext parallelmagic
     
     # Create a DirectView for all targets
     In [22]: dv = rc[:]
     
     # Make this DirectView active for parallel magic commands
     In [23]: dv.activate()
 
     In [24]: dv.block=True
 
     In [25]: import numpy
 
     In [26]: %px import numpy
     Parallel execution on engines: [0, 1, 2, 3]
 
     In [27]: %px a = numpy.random.rand(2,2)
     Parallel execution on engines: [0, 1, 2, 3]
 
     In [28]: %px ev = numpy.linalg.eigvals(a)
     Parallel execution on engines: [0, 1, 2, 3]
 
     In [28]: dv['ev']
     Out[28]: [ array([ 1.09522024, -0.09645227]), 
                array([ 1.21435496, -0.35546712]), 
                array([ 0.72180653,  0.07133042]), 
                array([  1.46384341e+00,   1.04353244e-04])
              ]
 
 The ``%result`` magic gets the most recent result, or takes an argument
 specifying the index of the result to be requested. It is simply a shortcut to the
 :meth:`get_result` method:
 
 .. sourcecode:: ipython
     
     In [29]: dv.apply_async(lambda : ev)
     
     In [30]: %result
     Out[30]: [ [ 1.28167017  0.14197338],
                 [-0.14093616  1.27877273],
                 [-0.37023573  1.06779409],
                 [ 0.83664764 -0.25602658] ]
 
 The ``%autopx`` magic switches to a mode where everything you type is executed
 on the engines given by the :attr:`targets` attribute:
 
 .. sourcecode:: ipython
 
     In [30]: dv.block=False
 
     In [31]: %autopx
     Auto Parallel Enabled
     Type %autopx to disable
 
     In [32]: max_evals = []
     <IPython.parallel.asyncresult.AsyncResult object at 0x17b8a70>
 
     In [33]: for i in range(100):
        ....:     a = numpy.random.rand(10,10)
        ....:     a = a+a.transpose()
        ....:     evals = numpy.linalg.eigvals(a)
        ....:     max_evals.append(evals[0].real)
        ....:
        ....:
     <IPython.parallel.asyncresult.AsyncResult object at 0x17af8f0>
 
     In [34]: %autopx
     Auto Parallel Disabled
 
     In [35]: dv.block=True
 
     In [36]: px ans= "Average max eigenvalue is: %f"%(sum(max_evals)/len(max_evals))
     Parallel execution on engines: [0, 1, 2, 3]
     
     In [37]: dv['ans']
     Out[37]: [ 'Average max eigenvalue is:  10.1387247332',
                'Average max eigenvalue is:  10.2076902286',
                'Average max eigenvalue is:  10.1891484655',
                'Average max eigenvalue is:  10.1158837784',]
 
 
 Moving Python objects around
 ============================
 
 In addition to calling functions and executing code on engines, you can
 transfer Python objects to and from your IPython session and the engines. In
 IPython, these operations are called :meth:`push` (sending an object to the
 engines) and :meth:`pull` (getting an object from the engines).
 
 Basic push and pull
 -------------------
 
 Here are some examples of how you use :meth:`push` and :meth:`pull`:
 
 .. sourcecode:: ipython
 
     In [38]: dview.push(dict(a=1.03234,b=3453))
     Out[38]: [None,None,None,None]
 
     In [39]: dview.pull('a')
     Out[39]: [ 1.03234, 1.03234, 1.03234, 1.03234]
 
     In [40]: dview.pull('b', targets=0)
     Out[40]: 3453
 
     In [41]: dview.pull(('a','b'))
     Out[41]: [ [1.03234, 3453], [1.03234, 3453], [1.03234, 3453], [1.03234, 3453] ]
     
     In [43]: dview.push(dict(c='speed'))
     Out[43]: [None,None,None,None]
 
 In non-blocking mode :meth:`push` and :meth:`pull` also return
 :class:`AsyncResult` objects:
 
 .. sourcecode:: ipython
 
     In [48]: ar = dview.pull('a', block=False)
 
     In [49]: ar.get()
     Out[49]: [1.03234, 1.03234, 1.03234, 1.03234]
 
 
 Dictionary interface
 --------------------
 
 Since a Python namespace is just a :class:`dict`, :class:`DirectView` objects provide
 dictionary-style access by key and methods such as :meth:`get` and
 :meth:`update` for convenience. This make the remote namespaces of the engines
 appear as a local dictionary. Underneath, these methods call :meth:`apply`:
 
 .. sourcecode:: ipython
 
     In [51]: dview['a']=['foo','bar']
 
     In [52]: dview['a']
     Out[52]: [ ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'] ]
 
 Scatter and gather
 ------------------
 
 Sometimes it is useful to partition a sequence and push the partitions to
 different engines. In MPI language, this is know as scatter/gather and we
 follow that terminology. However, it is important to remember that in
 IPython's :class:`Client` class, :meth:`scatter` is from the
 interactive IPython session to the engines and :meth:`gather` is from the
 engines back to the interactive IPython session. For scatter/gather operations
 between engines, MPI should be used:
 
 .. sourcecode:: ipython
 
     In [58]: dview.scatter('a',range(16))
     Out[58]: [None,None,None,None]
 
     In [59]: dview['a']
     Out[59]: [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15] ]
 
     In [60]: dview.gather('a')
     Out[60]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
 
 Other things to look at
 =======================
 
 How to do parallel list comprehensions
 --------------------------------------
 
 In many cases list comprehensions are nicer than using the map function. While
 we don't have fully parallel list comprehensions, it is simple to get the
 basic effect using :meth:`scatter` and :meth:`gather`:
 
 .. sourcecode:: ipython
 
     In [66]: dview.scatter('x',range(64))
 
     In [67]: %px y = [i**10 for i in x]
     Parallel execution on engines: [0, 1, 2, 3]
     Out[67]:
 
     In [68]: y = dview.gather('y')
 
     In [69]: print y
     [0, 1, 1024, 59049, 1048576, 9765625, 60466176, 282475249, 1073741824,...]
 
 Remote imports
 --------------
 
 Sometimes you will want to import packages both in your interactive session
 and on your remote engines.  This can be done with the :class:`ContextManager`
 created by a DirectView's :meth:`sync_imports` method:
 
 .. sourcecode:: ipython
 
     In [69]: with dview.sync_imports():
         ...:     import numpy
     importing numpy on engine(s)
 
 Any imports made inside the block will also be performed on the view's engines.
 sync_imports also takes a `local` boolean flag that defaults to True, which specifies
 whether the local imports should also be performed.  However, support for `local=False`
 has not been implemented, so only packages that can be imported locally will work
 this way.
 
 You can also specify imports via the ``@require`` decorator.  This is a decorator
 designed for use in Dependencies, but can be used to handle remote imports as well.
 Modules or module names passed to ``@require`` will be imported before the decorated
 function is called.  If they cannot be imported, the decorated function will never
 execution, and will fail with an UnmetDependencyError.
 
 .. sourcecode:: ipython
 
     In [69]: from IPython.parallel import require
 
     In [70]: @requre('re'):
         ...: def findall(pat, x):
         ...:     # re is guaranteed to be available
         ...:     return re.findall(pat, x)
           
     # you can also pass modules themselves, that you already have locally:
     In [71]: @requre(time):
         ...: def wait(t):
         ...:     time.sleep(t)
         ...:     return t
 
 
 Parallel exceptions
 -------------------
 
 In the multiengine interface, parallel commands can raise Python exceptions,
 just like serial commands. But, it is a little subtle, because a single
 parallel command can actually raise multiple exceptions (one for each engine
 the command was run on). To express this idea, we have a
 :exc:`CompositeError` exception class that will be raised in most cases. The
 :exc:`CompositeError` class is a special type of exception that wraps one or
 more other types of exceptions. Here is how it works:
 
 .. sourcecode:: ipython
 
     In [76]: dview.block=True
 
     In [77]: dview.execute('1/0')
     ---------------------------------------------------------------------------
     CompositeError                            Traceback (most recent call last)
     /home/you/<ipython-input-10-15c2c22dec39> in <module>()
     ----> 1 dview.execute('1/0', block=True)
 
     /path/to/site-packages/IPython/parallel/view.py in execute(self, code, block)
         460                 default: self.block
         461         """
     --> 462         return self.apply_with_flags(util._execute, args=(code,), block=block)
         463 
         464     def run(self, filename, block=None):
 
     /home/you/<string> in apply_with_flags(self, f, args, kwargs, block, track)
 
     /path/to/site-packages/IPython/parallel/view.py in sync_results(f, self, *args, **kwargs)
          46 def sync_results(f, self, *args, **kwargs):
          47     """sync relevant results from self.client to our results attribute."""
     ---> 48     ret = f(self, *args, **kwargs)
          49     delta = self.outstanding.difference(self.client.outstanding)
          50     completed = self.outstanding.intersection(delta)
 
     /home/you/<string> in apply_with_flags(self, f, args, kwargs, block, track)
 
     /path/to/site-packages/IPython/parallel/view.py in save_ids(f, self, *args, **kwargs)
          35     n_previous = len(self.client.history)
          36     try:
     ---> 37         ret = f(self, *args, **kwargs)
          38     finally:
          39         nmsgs = len(self.client.history) - n_previous
 
     /path/to/site-packages/IPython/parallel/view.py in apply_with_flags(self, f, args, kwargs, block, track)
         398         if block:
         399             try:
     --> 400                 return ar.get()
         401             except KeyboardInterrupt:
         402                 pass
 
     /path/to/site-packages/IPython/parallel/asyncresult.pyc in get(self, timeout)
          87                 return self._result
          88             else:
     ---> 89                 raise self._exception
          90         else:
          91             raise error.TimeoutError("Result not ready.")
 
     CompositeError: one or more exceptions from call to method: _execute
     [0:apply]: ZeroDivisionError: integer division or modulo by zero
     [1:apply]: ZeroDivisionError: integer division or modulo by zero
     [2:apply]: ZeroDivisionError: integer division or modulo by zero
     [3:apply]: ZeroDivisionError: integer division or modulo by zero
 
 
 Notice how the error message printed when :exc:`CompositeError` is raised has
 information about the individual exceptions that were raised on each engine.
 If you want, you can even raise one of these original exceptions:
 
 .. sourcecode:: ipython
 
     In [80]: try:
        ....:     dview.execute('1/0')
        ....: except client.CompositeError, e:
        ....:     e.raise_exception()
        ....:     
        ....:     
     ---------------------------------------------------------------------------
     ZeroDivisionError                         Traceback (most recent call last)
 
     /ipython1-client-r3021/docs/examples/<ipython console> in <module>()
 
     /ipython1-client-r3021/ipython1/kernel/error.pyc in raise_exception(self, excid)
         156             raise IndexError("an exception with index %i does not exist"%excid)
         157         else:
     --> 158             raise et, ev, etb
         159 
         160 def collect_exceptions(rlist, method):
 
     ZeroDivisionError: integer division or modulo by zero
 
 If you are working in IPython, you can simple type ``%debug`` after one of
 these :exc:`CompositeError` exceptions is raised, and inspect the exception
 instance:
 
 .. sourcecode:: ipython
 
     In [81]: dview.execute('1/0')
     ---------------------------------------------------------------------------
     CompositeError                            Traceback (most recent call last)
     /home/you/<ipython-input-10-15c2c22dec39> in <module>()
     ----> 1 dview.execute('1/0', block=True)
 
     /path/to/site-packages/IPython/parallel/view.py in execute(self, code, block)
         460                 default: self.block
         461         """
     --> 462         return self.apply_with_flags(util._execute, args=(code,), block=block)
         463 
         464     def run(self, filename, block=None):
 
     /home/you/<string> in apply_with_flags(self, f, args, kwargs, block, track)
 
     /path/to/site-packages/IPython/parallel/view.py in sync_results(f, self, *args, **kwargs)
          46 def sync_results(f, self, *args, **kwargs):
          47     """sync relevant results from self.client to our results attribute."""
     ---> 48     ret = f(self, *args, **kwargs)
          49     delta = self.outstanding.difference(self.client.outstanding)
          50     completed = self.outstanding.intersection(delta)
 
     /home/you/<string> in apply_with_flags(self, f, args, kwargs, block, track)
 
     /path/to/site-packages/IPython/parallel/view.py in save_ids(f, self, *args, **kwargs)
          35     n_previous = len(self.client.history)
          36     try:
     ---> 37         ret = f(self, *args, **kwargs)
          38     finally:
          39         nmsgs = len(self.client.history) - n_previous
 
     /path/to/site-packages/IPython/parallel/view.py in apply_with_flags(self, f, args, kwargs, block, track)
         398         if block:
         399             try:
     --> 400                 return ar.get()
         401             except KeyboardInterrupt:
         402                 pass
 
     /path/to/site-packages/IPython/parallel/asyncresult.pyc in get(self, timeout)
          87                 return self._result
          88             else:
     ---> 89                 raise self._exception
          90         else:
          91             raise error.TimeoutError("Result not ready.")
 
     CompositeError: one or more exceptions from call to method: _execute
     [0:apply]: ZeroDivisionError: integer division or modulo by zero
     [1:apply]: ZeroDivisionError: integer division or modulo by zero
     [2:apply]: ZeroDivisionError: integer division or modulo by zero
     [3:apply]: ZeroDivisionError: integer division or modulo by zero
     
     In [82]: %debug
     > /path/to/site-packages/IPython/parallel/asyncresult.py(80)get()
          79             else:
     ---> 80                 raise self._exception
          81         else:
     
     
     # With the debugger running, e is the exceptions instance.  We can tab complete
     # on it and see the extra methods that are available.
     ipdb> e.
     e.__class__         e.__getitem__       e.__new__           e.__setstate__      e.args
     e.__delattr__       e.__getslice__      e.__reduce__        e.__str__           e.elist
     e.__dict__          e.__hash__          e.__reduce_ex__     e.__weakref__       e.message
     e.__doc__           e.__init__          e.__repr__          e._get_engine_str   e.print_tracebacks
     e.__getattribute__  e.__module__        e.__setattr__       e._get_traceback    e.raise_exception
     ipdb> e.print_tracebacks()
     [0:apply]: 
     Traceback (most recent call last):
       File "/path/to/site-packages/IPython/parallel/streamkernel.py", line 332, in apply_request
         exec code in working, working
       File "<string>", line 1, in <module>
       File "/path/to/site-packages/IPython/parallel/client.py", line 69, in _execute
         exec code in globals()
       File "<string>", line 1, in <module>
     ZeroDivisionError: integer division or modulo by zero
 
 
     [1:apply]: 
     Traceback (most recent call last):
       File "/path/to/site-packages/IPython/parallel/streamkernel.py", line 332, in apply_request
         exec code in working, working
       File "<string>", line 1, in <module>
       File "/path/to/site-packages/IPython/parallel/client.py", line 69, in _execute
         exec code in globals()
       File "<string>", line 1, in <module>
     ZeroDivisionError: integer division or modulo by zero
 
 
     [2:apply]: 
     Traceback (most recent call last):
       File "/path/to/site-packages/IPython/parallel/streamkernel.py", line 332, in apply_request
         exec code in working, working
       File "<string>", line 1, in <module>
       File "/path/to/site-packages/IPython/parallel/client.py", line 69, in _execute
         exec code in globals()
       File "<string>", line 1, in <module>
     ZeroDivisionError: integer division or modulo by zero
 
 
     [3:apply]: 
     Traceback (most recent call last):
       File "/path/to/site-packages/IPython/parallel/streamkernel.py", line 332, in apply_request
         exec code in working, working
       File "<string>", line 1, in <module>
       File "/path/to/site-packages/IPython/parallel/client.py", line 69, in _execute
         exec code in globals()
       File "<string>", line 1, in <module>
     ZeroDivisionError: integer division or modulo by zero
     
 
 .. note::
 
     TODO: The above tracebacks are not up to date
 
 
 All of this same error handling magic even works in non-blocking mode:
 
 .. sourcecode:: ipython
 
     In [83]: dview.block=False
 
     In [84]: ar = dview.execute('1/0')
 
     In [85]: ar.get()
     ---------------------------------------------------------------------------
     CompositeError                            Traceback (most recent call last)
     /Users/minrk/<ipython-input-3-8531eb3d26fb> in <module>()
     ----> 1 ar.get()
 
     /path/to/site-packages/IPython/parallel/asyncresult.pyc in get(self, timeout)
          78                 return self._result
          79             else:
     ---> 80                 raise self._exception
          81         else:
          82             raise error.TimeoutError("Result not ready.")
 
     CompositeError: one or more exceptions from call to method: _execute
     [0:apply]: ZeroDivisionError: integer division or modulo by zero
     [1:apply]: ZeroDivisionError: integer division or modulo by zero
     [2:apply]: ZeroDivisionError: integer division or modulo by zero
     [3:apply]: ZeroDivisionError: integer division or modulo by zero
 

docs/source/parallel/parallel_process.txt

@@ -1,506 +1,506 b''
 .. _parallel_process:
 
 ===========================================
 Starting the IPython controller and engines
 ===========================================
 
 To use IPython for parallel computing, you need to start one instance of
 the controller and one or more instances of the engine. The controller
 and each engine can run on different machines or on the same machine.
 Because of this, there are many different possibilities.
 
 Broadly speaking, there are two ways of going about starting a controller and engines:
 
-* In an automated manner using the :command:`ipclusterz` command.
-* In a more manual way using the :command:`ipcontrollerz` and 
-  :command:`ipenginez` commands.
+* In an automated manner using the :command:`ipcluster` command.
+* In a more manual way using the :command:`ipcontroller` and 
+  :command:`ipengine` commands.
 
 This document describes both of these methods. We recommend that new users
-start with the :command:`ipclusterz` command as it simplifies many common usage
+start with the :command:`ipcluster` command as it simplifies many common usage
 cases.
 
 General considerations
 ======================
 
 Before delving into the details about how you can start a controller and
 engines using the various methods, we outline some of the general issues that
 come up when starting the controller and engines. These things come up no
 matter which method you use to start your IPython cluster.
 
 Let's say that you want to start the controller on ``host0`` and engines on
 hosts ``host1``-``hostn``. The following steps are then required:
 
-1. Start the controller on ``host0`` by running :command:`ipcontrollerz` on
+1. Start the controller on ``host0`` by running :command:`ipcontroller` on
    ``host0``.
 2. Move the JSON file (:file:`ipcontroller-engine.json`) created by the 
    controller from ``host0`` to hosts ``host1``-``hostn``.
 3. Start the engines on hosts ``host1``-``hostn`` by running 
-   :command:`ipenginez`.  This command has to be told where the JSON file 
+   :command:`ipengine`.  This command has to be told where the JSON file 
    (:file:`ipcontroller-engine.json`) is located.
 
 At this point, the controller and engines will be connected. By default, the JSON files
-created by the controller are put into the :file:`~/.ipython/clusterz_default/security`
+created by the controller are put into the :file:`~/.ipython/cluster_default/security`
 directory. If the engines share a filesystem with the controller, step 2 can be skipped as
 the engines will automatically look at that location.
 
 The final step required to actually use the running controller from a client is to move
 the JSON file :file:`ipcontroller-client.json` from ``host0`` to any host where clients
-will be run. If these file are put into the :file:`~/.ipython/clusterz_default/security`
+will be run. If these file are put into the :file:`~/.ipython/cluster_default/security`
 directory of the client's host, they will be found automatically. Otherwise, the full path
 to them has to be passed to the client's constructor.
 
-Using :command:`ipclusterz`
+Using :command:`ipcluster`
 ===========================
 
-The :command:`ipclusterz` command provides a simple way of starting a
+The :command:`ipcluster` command provides a simple way of starting a
 controller and engines in the following situations:
 
 1. When the controller and engines are all run on localhost. This is useful
    for testing or running on a multicore computer.
 2. When engines are started using the :command:`mpirun` command that comes 
    with most MPI [MPI]_ implementations
 3. When engines are started using the PBS [PBS]_ batch system 
    (or other `qsub` systems, such as SGE).
 4. When the controller is started on localhost and the engines are started on 
    remote nodes using :command:`ssh`.
 5. When engines are started using the Windows HPC Server batch system.
 
 .. note::
 
-    Currently :command:`ipclusterz` requires that the
+    Currently :command:`ipcluster` requires that the
     :file:`~/.ipython/cluster_<profile>/security` directory live on a shared filesystem that is
     seen by both the controller and engines. If you don't have a shared file
-    system you will need to use :command:`ipcontrollerz` and
-    :command:`ipenginez` directly.
+    system you will need to use :command:`ipcontroller` and
+    :command:`ipengine` directly.
 
-Under the hood, :command:`ipclusterz` just uses :command:`ipcontrollerz`
-and :command:`ipenginez` to perform the steps described above.
+Under the hood, :command:`ipcluster` just uses :command:`ipcontroller`
+and :command:`ipengine` to perform the steps described above.
 
-The simplest way to use ipclusterz requires no configuration, and will
+The simplest way to use ipcluster requires no configuration, and will
 launch a controller and a number of engines on the local machine. For instance, 
 to start one controller and 4 engines on localhost, just do::
 
-    $ ipclusterz start -n 4
+    $ ipcluster start -n 4
 
 To see other command line options for the local mode, do::
 
-    $ ipclusterz -h
+    $ ipcluster -h
 
 
 Configuring an IPython cluster
 ==============================
 
 Cluster configurations are stored as `profiles`.  You can create a new profile with::
 
-    $ ipclusterz create -p myprofile
+    $ ipcluster create -p myprofile
 
-This will create the directory :file:`IPYTHONDIR/clusterz_myprofile`, and populate it
+This will create the directory :file:`IPYTHONDIR/cluster_myprofile`, and populate it
 with the default configuration files for the three IPython cluster commands. Once
-you edit those files, you can continue to call ipclusterz/ipcontrollerz/ipenginez
+you edit those files, you can continue to call ipcluster/ipcontroller/ipengine
 with no arguments beyond ``-p myprofile``, and any configuration will be maintained.
 
 There is no limit to the number of profiles you can have, so you can maintain a profile for each
 of your common use cases. The default profile will be used whenever the
-profile argument is not specified, so edit :file:`IPYTHONDIR/clusterz_default/*_config.py` to
+profile argument is not specified, so edit :file:`IPYTHONDIR/cluster_default/*_config.py` to
 represent your most common use case.
 
 The configuration files are loaded with commented-out settings and explanations,
 which should cover most of the available possibilities.
 
-Using various batch systems with :command:`ipclusterz`
+Using various batch systems with :command:`ipcluster`
 ------------------------------------------------------
 
-:command:`ipclusterz` has a notion of Launchers that can start controllers
+:command:`ipcluster` has a notion of Launchers that can start controllers
 and engines with various remote execution schemes.  Currently supported
 models include `mpiexec`, PBS-style (Torque, SGE), and Windows HPC Server.
 
 .. note::
 
     The Launchers and configuration are designed in such a way that advanced
     users can subclass and configure them to fit their own system that we
     have not yet supported (such as Condor)
 
-Using :command:`ipclusterz` in mpiexec/mpirun mode
+Using :command:`ipcluster` in mpiexec/mpirun mode
 --------------------------------------------------
 
 
 The mpiexec/mpirun mode is useful if you:
 
 1. Have MPI installed.
 2. Your systems are configured to use the :command:`mpiexec` or
    :command:`mpirun` commands to start MPI processes.
 
 If these are satisfied, you can create a new profile::
 
-    $ ipclusterz create -p mpi
+    $ ipcluster create -p mpi
 
-and edit the file :file:`IPYTHONDIR/clusterz_mpi/ipclusterz_config.py`.
+and edit the file :file:`IPYTHONDIR/cluster_mpi/ipcluster_config.py`.
 
-There, instruct ipclusterz to use the MPIExec launchers by adding the lines:
+There, instruct ipcluster to use the MPIExec launchers by adding the lines:
 
 .. sourcecode:: python
 
     c.Global.engine_launcher = 'IPython.parallel.launcher.MPIExecEngineSetLauncher'
 
 If the default MPI configuration is correct, then you can now start your cluster, with::
 
-    $ ipclusterz start -n 4 -p mpi
+    $ ipcluster start -n 4 -p mpi
 
 This does the following:
 
 1. Starts the IPython controller on current host.
 2. Uses :command:`mpiexec` to start 4 engines.
 
 If you have a reason to also start the Controller with mpi, you can specify:
 
 .. sourcecode:: python
 
     c.Global.controller_launcher = 'IPython.parallel.launcher.MPIExecControllerLauncher'
 
 .. note::
 
     The Controller *will not* be in the same MPI universe as the engines, so there is not
     much reason to do this unless sysadmins demand it.
 
 On newer MPI implementations (such as OpenMPI), this will work even if you
 don't make any calls to MPI or call :func:`MPI_Init`. However, older MPI
 implementations actually require each process to call :func:`MPI_Init` upon
 starting. The easiest way of having this done is to install the mpi4py
-[mpi4py]_ package and then specify the ``c.MPI.use`` option in :file:`ipenginez_config.py`:
+[mpi4py]_ package and then specify the ``c.MPI.use`` option in :file:`ipengine_config.py`:
 
 .. sourcecode:: python
 
     c.MPI.use = 'mpi4py'
 
 Unfortunately, even this won't work for some MPI implementations. If you are
 having problems with this, you will likely have to use a custom Python
 executable that itself calls :func:`MPI_Init` at the appropriate time.
 Fortunately, mpi4py comes with such a custom Python executable that is easy to
 install and use. However, this custom Python executable approach will not work
-with :command:`ipclusterz` currently.
+with :command:`ipcluster` currently.
 
 More details on using MPI with IPython can be found :ref:`here <parallelmpi>`.
 
 
-Using :command:`ipclusterz` in PBS mode
+Using :command:`ipcluster` in PBS mode
 ---------------------------------------
 
 The PBS mode uses the Portable Batch System [PBS]_ to start the engines. 
 
 As usual, we will start by creating a fresh profile::
 
-    $ ipclusterz create -p pbs
+    $ ipcluster create -p pbs
 
-And in :file:`ipclusterz_config.py`, we will select the PBS launchers for the controller
+And in :file:`ipcluster_config.py`, we will select the PBS launchers for the controller
 and engines:
 
 .. sourcecode:: python
 
     c.Global.controller_launcher = 'IPython.parallel.launcher.PBSControllerLauncher'
     c.Global.engine_launcher = 'IPython.parallel.launcher.PBSEngineSetLauncher'
 
 IPython does provide simple default batch templates for PBS and SGE, but you may need
 to specify your own. Here is a sample PBS script template:
 
 .. sourcecode:: bash
 
     #PBS -N ipython
     #PBS -j oe
     #PBS -l walltime=00:10:00
     #PBS -l nodes=${n/4}:ppn=4
     #PBS -q $queue
 
     cd $$PBS_O_WORKDIR
     export PATH=$$HOME/usr/local/bin
     export PYTHONPATH=$$HOME/usr/local/lib/python2.7/site-packages
-    /usr/local/bin/mpiexec -n ${n} ipenginez --cluster_dir=${cluster_dir}
+    /usr/local/bin/mpiexec -n ${n} ipengine --cluster_dir=${cluster_dir}
 
 There are a few important points about this template:
 
 1. This template will be rendered at runtime using IPython's :mod:`Itpl` 
    template engine.
 
 2. Instead of putting in the actual number of engines, use the notation
    ``${n}`` to indicate the number of engines to be started. You can also uses
    expressions like ``${n/4}`` in the template to indicate the number of 
    nodes. There will always be a ${n} and ${cluster_dir} variable passed to the template.
    These allow the batch system to know how many engines, and where the configuration
    files reside. The same is true for the batch queue, with the template variable ``$queue``.
 
 3. Because ``$`` is a special character used by the template engine, you must  
    escape any ``$`` by using ``$$``.  This is important when referring to 
    environment variables in the template, or in SGE, where the config lines start
    with ``#$``, which will have to be ``#$$``.
 
-4. Any options to :command:`ipenginez` can be given in the batch script 
-   template, or in :file:`ipenginez_config.py`.
+4. Any options to :command:`ipengine` can be given in the batch script 
+   template, or in :file:`ipengine_config.py`.
 
 5. Depending on the configuration of you system, you may have to set 
    environment variables in the script template.
 
 The controller template should be similar, but simpler:
 
 .. sourcecode:: bash
 
     #PBS -N ipython
     #PBS -j oe
     #PBS -l walltime=00:10:00
     #PBS -l nodes=1:ppn=4
     #PBS -q $queue
 
     cd $$PBS_O_WORKDIR
     export PATH=$$HOME/usr/local/bin
     export PYTHONPATH=$$HOME/usr/local/lib/python2.7/site-packages
-    ipcontrollerz --cluster_dir=${cluster_dir}
+    ipcontroller --cluster_dir=${cluster_dir}
 
 
 Once you have created these scripts, save them with names like
-:file:`pbs.engine.template`. Now you can load them into the :file:`ipclusterz_config` with:
+:file:`pbs.engine.template`. Now you can load them into the :file:`ipcluster_config` with:
 
 .. sourcecode:: python
 
     c.PBSEngineSetLauncher.batch_template_file = "pbs.engine.template"
     
     c.PBSControllerLauncher.batch_template_file = "pbs.controller.template"
         
 
-Alternately, you can just define the templates as strings inside :file:`ipclusterz_config`.
+Alternately, you can just define the templates as strings inside :file:`ipcluster_config`.
 
 Whether you are using your own templates or our defaults, the extra configurables available are
 the number of engines to launch (``$n``, and the batch system queue to which the jobs are to be
 submitted (``$queue``)). These are configurables, and can be specified in
-:file:`ipclusterz_config`:
+:file:`ipcluster_config`:
 
 .. sourcecode:: python
 
     c.PBSLauncher.queue = 'veryshort.q'
     c.PBSEngineSetLauncher.n = 64
 
 Note that assuming you are running PBS on a multi-node cluster, the Controller's default behavior
 of listening only on localhost is likely too restrictive.  In this case, also assuming the
 nodes are safely behind a firewall, you can simply instruct the Controller to listen for
-connections on all its interfaces, by adding in :file:`ipcontrollerz_config`:
+connections on all its interfaces, by adding in :file:`ipcontroller_config`:
 
 .. sourcecode:: python
 
     c.RegistrationFactory.ip = '*'
 
 You can now run the cluster with::
 
-    $ ipclusterz start -p pbs -n 128
+    $ ipcluster start -p pbs -n 128
 
-Additional configuration options can be found in the PBS section of :file:`ipclusterz_config`.
+Additional configuration options can be found in the PBS section of :file:`ipcluster_config`.
 
 .. note::
 
     Due to the flexibility of configuration, the PBS launchers work with simple changes
     to the template for other :command:`qsub`-using systems, such as Sun Grid Engine,
     and with further configuration in similar batch systems like Condor.
 
 
-Using :command:`ipclusterz` in SSH mode
+Using :command:`ipcluster` in SSH mode
 ---------------------------------------
 
 
-The SSH mode uses :command:`ssh` to execute :command:`ipenginez` on remote
-nodes and :command:`ipcontrollerz` can be run remotely as well, or on localhost.
+The SSH mode uses :command:`ssh` to execute :command:`ipengine` on remote
+nodes and :command:`ipcontroller` can be run remotely as well, or on localhost.
 
 .. note::
 
     When using this mode it highly recommended that you have set up SSH keys
     and are using ssh-agent [SSH]_ for password-less logins.
 
 As usual, we start by creating a clean profile::
 
-    $ ipclusterz create -p ssh
+    $ ipcluster create -p ssh
 
-To use this mode, select the SSH launchers in :file:`ipclusterz_config.py`:
+To use this mode, select the SSH launchers in :file:`ipcluster_config.py`:
 
 .. sourcecode:: python
 
     c.Global.engine_launcher = 'IPython.parallel.launcher.SSHEngineSetLauncher'
     # and if the Controller is also to be remote:
     c.Global.controller_launcher = 'IPython.parallel.launcher.SSHControllerLauncher'
     
 
 The controller's remote location and configuration can be specified:
 
 .. sourcecode:: python
 
     # Set the user and hostname for the controller
     # c.SSHControllerLauncher.hostname = 'controller.example.com'
     # c.SSHControllerLauncher.user = os.environ.get('USER','username')
 
-    # Set the arguments to be passed to ipcontrollerz
-    # note that remotely launched ipcontrollerz will not get the contents of 
-    # the local ipcontrollerz_config.py unless it resides on the *remote host*
+    # Set the arguments to be passed to ipcontroller
+    # note that remotely launched ipcontroller will not get the contents of 
+    # the local ipcontroller_config.py unless it resides on the *remote host*
     # in the location specified by the --cluster_dir argument.
     # c.SSHControllerLauncher.program_args = ['-r', '-ip', '0.0.0.0', '--cluster_dir', '/path/to/cd']
 
 .. note::
 
     SSH mode does not do any file movement, so you will need to distribute configuration
     files manually.  To aid in this, the `reuse_files` flag defaults to True for ssh-launched
     Controllers, so you will only need to do this once, unless you override this flag back
     to False.
 
 Engines are specified in a dictionary, by hostname and the number of engines to be run
 on that host.
 
 .. sourcecode:: python
 
     c.SSHEngineSetLauncher.engines = { 'host1.example.com' : 2,
                 'host2.example.com' : 5,
                 'host3.example.com' : (1, ['--cluster_dir', '/home/different/location']),
                 'host4.example.com' : 8 }
 
 * The `engines` dict, where the keys are the host we want to run engines on and
   the value is the number of engines to run on that host.
 * on host3, the value is a tuple, where the number of engines is first, and the arguments
-  to be passed to :command:`ipenginez` are the second element.
+  to be passed to :command:`ipengine` are the second element.
 
 For engines without explicitly specified arguments, the default arguments are set in
 a single location:
 
 .. sourcecode:: python
 
-    c.SSHEngineSetLauncher.engine_args = ['--cluster_dir', '/path/to/clusterz_ssh']
+    c.SSHEngineSetLauncher.engine_args = ['--cluster_dir', '/path/to/cluster_ssh']
 
-Current limitations of the SSH mode of :command:`ipclusterz` are:
+Current limitations of the SSH mode of :command:`ipcluster` are:
 
 * Untested on Windows. Would require a working :command:`ssh` on Windows.
   Also, we are using shell scripts to setup and execute commands on remote
   hosts.
 * No file movement - 
 
-Using the :command:`ipcontrollerz` and :command:`ipenginez` commands
+Using the :command:`ipcontroller` and :command:`ipengine` commands
 ====================================================================
 
-It is also possible to use the :command:`ipcontrollerz` and :command:`ipenginez`
+It is also possible to use the :command:`ipcontroller` and :command:`ipengine`
 commands to start your controller and engines. This approach gives you full
 control over all aspects of the startup process.
 
 Starting the controller and engine on your local machine
 --------------------------------------------------------
 
-To use :command:`ipcontrollerz` and :command:`ipenginez` to start things on your
+To use :command:`ipcontroller` and :command:`ipengine` to start things on your
 local machine, do the following.
 
 First start the controller::
 
-    $ ipcontrollerz
+    $ ipcontroller
     
 Next, start however many instances of the engine you want using (repeatedly)
 the command::
 
-    $ ipenginez
+    $ ipengine
 
 The engines should start and automatically connect to the controller using the
-JSON files in :file:`~/.ipython/clusterz_default/security`. You are now ready to use the
+JSON files in :file:`~/.ipython/cluster_default/security`. You are now ready to use the
 controller and engines from IPython.
 
 .. warning:: 
     
     The order of the above operations may be important.  You *must*
     start the controller before the engines, unless you are reusing connection
     information (via `-r`), in which case ordering is not important.
 
 .. note::
 
     On some platforms (OS X), to put the controller and engine into the
     background you may need to give these commands in the form ``(ipcontroller
     &)`` and ``(ipengine &)`` (with the parentheses) for them to work
     properly.
 
 Starting the controller and engines on different hosts
 ------------------------------------------------------
 
 When the controller and engines are running on different hosts, things are
 slightly more complicated, but the underlying ideas are the same:
 
-1. Start the controller on a host using :command:`ipcontrollerz`.
+1. Start the controller on a host using :command:`ipcontroller`.
 2. Copy :file:`ipcontroller-engine.json` from :file:`~/.ipython/cluster_<profile>/security` on
    the controller's host to the host where the engines will run.
-3. Use :command:`ipenginez` on the engine's hosts to start the engines.
+3. Use :command:`ipengine` on the engine's hosts to start the engines.
 
-The only thing you have to be careful of is to tell :command:`ipenginez` where
+The only thing you have to be careful of is to tell :command:`ipengine` where
 the :file:`ipcontroller-engine.json` file is located. There are two ways you
 can do this:
 
 * Put :file:`ipcontroller-engine.json` in the :file:`~/.ipython/cluster_<profile>/security`
   directory on the engine's host, where it will be found automatically.
-* Call :command:`ipenginez` with the ``--file=full_path_to_the_file``
+* Call :command:`ipengine` with the ``--file=full_path_to_the_file``
   flag.
 
 The ``--file`` flag works like this::
 
     $ ipengine --file=/path/to/my/ipcontroller-engine.json
 
 .. note::
     
     If the controller's and engine's hosts all have a shared file system  
     (:file:`~/.ipython/cluster_<profile>/security` is the same on all of them), then things
     will just work!
 
 Make JSON files persistent
 --------------------------
 
 At fist glance it may seem that that managing the JSON files is a bit
 annoying. Going back to the house and key analogy, copying the JSON around
 each time you start the controller is like having to make a new key every time
 you want to unlock the door and enter your house. As with your house, you want
 to be able to create the key (or JSON file) once, and then simply use it at
 any point in the future.
 
 To do this, the only thing you have to do is specify the `-r` flag, so that
 the connection information in the JSON files remains accurate::
 
-    $ ipcontrollerz -r
+    $ ipcontroller -r
 
 Then, just copy the JSON files over the first time and you are set. You can
 start and stop the controller and engines any many times as you want in the
 future, just make sure to tell the controller to reuse the file.
 
 .. note::
 
     You may ask the question: what ports does the controller listen on if you
     don't tell is to use specific ones? The default is to use high random port
     numbers. We do this for two reasons: i) to increase security through
     obscurity and ii) to multiple controllers on a given host to start and
     automatically use different ports.
 
 Log files
 ---------
 
 All of the components of IPython have log files associated with them.
 These log files can be extremely useful in debugging problems with
 IPython and can be found in the directory :file:`~/.ipython/cluster_<profile>/log`.
 Sending the log files to us will often help us to debug any problems.
 
 
-Configuring `ipcontrollerz`
+Configuring `ipcontroller`
 ---------------------------
 
 Ports and addresses
 *******************
 
 
 Database Backend
 ****************
 
 
 .. seealso::
 
     
 
-Configuring `ipenginez`
+Configuring `ipengine`
 -----------------------
 
 .. note::
 
     TODO
 
 
 
 .. [PBS] Portable Batch System.  http://www.openpbs.org/
 .. [SSH] SSH-Agent http://en.wikipedia.org/wiki/ssh-agent

docs/source/parallel/parallel_security.txt

@@ -1,324 +1,324 b''
 .. _parallelsecurity:
 
 ===========================
 Security details of IPython
 ===========================
 
 .. note::
 
     This section is not thorough, and IPython.zmq needs a thorough security
     audit.
 
 IPython's :mod:`IPython.zmq` package exposes the full power of the
 Python interpreter over a TCP/IP network for the purposes of parallel
 computing. This feature brings up the important question of IPython's security
 model. This document gives details about this model and how it is implemented
 in IPython's architecture.
 
 Processs and network topology
 =============================
 
 To enable parallel computing, IPython has a number of different processes that
 run. These processes are discussed at length in the IPython documentation and
 are summarized here:
 
 * The IPython *engine*.  This process is a full blown Python
   interpreter in which user code is executed.  Multiple
   engines are started to make parallel computing possible.
 * The IPython *hub*.  This process monitors a set of 
   engines and schedulers, and keeps track of the state of the processes. It listens
   for registration connections from engines and clients, and monitor connections
   from schedulers.
 * The IPython *schedulers*. This is a set of processes that relay commands and results
   between clients and engines. They are typically on the same machine as the controller,
   and listen for connections from engines and clients, but connect to the Hub.
 * The IPython *client*.  This process is typically an
   interactive Python process that is used to coordinate the
   engines to get a parallel computation done.
 
 Collectively, these processes are called the IPython *kernel*, and the hub and schedulers
 together are referred to as the *controller*.
 
 .. note::
 
     Are these really still referred to as the Kernel?  It doesn't seem so to me.  'cluster'
     seems more accurate.
     
     -MinRK
 
 These processes communicate over any transport supported by ZeroMQ (tcp,pgm,infiniband,ipc)
 with a well defined topology. The IPython hub and schedulers listen on sockets. Upon
 starting, an engine connects to a hub and registers itself, which then informs the engine
 of the connection information for the schedulers, and the engine then connects to the
 schedulers. These engine/hub and engine/scheduler connections persist for the
 lifetime of each engine.
 
 The IPython client also connects to the controller processes using a number of socket
 connections. As of writing, this is one socket per scheduler (4), and 3 connections to the
 hub for a total of 7. These connections persist for the lifetime of the client only.
 
 A given IPython controller and set of engines engines typically has a relatively
 short lifetime. Typically this lifetime corresponds to the duration of a single parallel
 simulation performed by a single user. Finally, the hub, schedulers, engines, and client
 processes typically execute with the permissions of that same user. More specifically, the
 controller and engines are *not* executed as root or with any other superuser permissions.
 
 Application logic
 =================
 
 When running the IPython kernel to perform a parallel computation, a user
 utilizes the IPython client to send Python commands and data through the
 IPython schedulers to the IPython engines, where those commands are executed
 and the data processed. The design of IPython ensures that the client is the
 only access point for the capabilities of the engines. That is, the only way
 of addressing the engines is through a client.
 
 A user can utilize the client to instruct the IPython engines to execute
 arbitrary Python commands. These Python commands can include calls to the
 system shell, access the filesystem, etc., as required by the user's
 application code. From this perspective, when a user runs an IPython engine on
 a host, that engine has the same capabilities and permissions as the user
 themselves (as if they were logged onto the engine's host with a terminal).
 
 Secure network connections
 ==========================
 
 Overview
 --------
 
 ZeroMQ provides exactly no security. For this reason, users of IPython must be very
 careful in managing connections, because an open TCP/IP socket presents access to
 arbitrary execution as the user on the engine machines. As a result, the default behavior
 of controller processes is to only listen for clients on the loopback interface, and the
 client must establish SSH tunnels to connect to the controller processes.
 
 .. warning:: 
 
     If the controller's loopback interface is untrusted, then IPython should be considered
     vulnerable, and this extends to the loopback of all connected clients, which have
     opened a loopback port that is redirected to the controller's loopback port.
 
 
 SSH
 ---
 
 Since ZeroMQ provides no security, SSH tunnels are the primary source of secure
 connections. A connector file, such as `ipcontroller-client.json`, will contain
 information for connecting to the controller, possibly including the address of an
 ssh-server through with the client is to tunnel. The Client object then creates tunnels
 using either [OpenSSH]_ or [Paramiko]_, depending on the platform. If users do not wish to
 use OpenSSH or Paramiko, or the tunneling utilities are insufficient, then they may
 construct the tunnels themselves, and simply connect clients and engines as if the
 controller were on loopback on the connecting machine.
 
 .. note::
 
     There is not currently tunneling available for engines.
 
 Authentication
 --------------
 
 To protect users of shared machines, an execution key is used to authenticate all messages.
 
 The Session object that handles the message protocol uses a unique key to verify valid
 messages. This can be any value specified by the user, but the default behavior is a
 pseudo-random 128-bit number, as generated by `uuid.uuid4()`. This key is checked on every
 message everywhere it is unpacked (Controller, Engine, and Client) to ensure that it came
 from an authentic user, and no messages that do not contain this key are acted upon in any
 way.
 
 There is exactly one key per cluster - it must be the same everywhere. Typically, the
 controller creates this key, and stores it in the private connection files
 `ipython-{engine|client}.json`. These files are typically stored in the
-`~/.ipython/clusterz_<profile>/security` directory, and are maintained as readable only by
+`~/.ipython/cluster_<profile>/security` directory, and are maintained as readable only by
 the owner, just as is common practice with a user's keys in their `.ssh` directory.
 
 .. warning::
 
     It is important to note that the key authentication, as emphasized by the use of
     a uuid rather than generating a key with a cryptographic library, provides a 
     defense against *accidental* messages more than it does against malicious attacks.
     If loopback is compromised, it would be trivial for an attacker to intercept messages
     and deduce the key, as there is no encryption.
 
 
 
 Specific security vulnerabilities
 =================================
 
 There are a number of potential security vulnerabilities present in IPython's
 architecture. In this section we discuss those vulnerabilities and detail how
 the security architecture described above prevents them from being exploited.
 
 Unauthorized clients
 --------------------
 
 The IPython client can instruct the IPython engines to execute arbitrary
 Python code with the permissions of the user who started the engines. If an
 attacker were able to connect their own hostile IPython client to the IPython
 controller, they could instruct the engines to execute code.
 
 
 On the first level, this attack is prevented by requiring access to the controller's
 ports, which are recommended to only be open on loopback if the controller is on an
 untrusted local network. If the attacker does have access to the Controller's ports, then
 the attack is prevented by the capabilities based client authentication of the execution
 key. The relevant authentication information is encoded into the JSON file that clients
 must present to gain access to the IPython controller. By limiting the distribution of
 those keys, a user can grant access to only authorized persons, just as with SSH keys.
 
 It is highly unlikely that an execution key could be guessed by an attacker
 in a brute force guessing attack. A given instance of the IPython controller
 only runs for a relatively short amount of time (on the order of hours). Thus
 an attacker would have only a limited amount of time to test a search space of
 size 2**128.  
 
 .. warning::
 
     If the attacker has gained enough access to intercept loopback connections on
     *either* the controller or client, then the key is easily deduced from network
     traffic.
 
 
 Unauthorized engines
 --------------------
 
 If an attacker were able to connect a hostile engine to a user's controller,
 the user might unknowingly send sensitive code or data to the hostile engine.
 This attacker's engine would then have full access to that code and data.
 
 This type of attack is prevented in the same way as the unauthorized client
 attack, through the usage of the capabilities based authentication scheme.
 
 Unauthorized controllers
 ------------------------
 
 It is also possible that an attacker could try to convince a user's IPython
 client or engine to connect to a hostile IPython controller. That controller
 would then have full access to the code and data sent between the IPython
 client and the IPython engines.
 
 Again, this attack is prevented through the capabilities in a connection file, which
 ensure that a client or engine connects to the correct controller. It is also important to
 note that the connection files also encode the IP address and port that the controller is
 listening on, so there is little chance of mistakenly connecting to a controller running
 on a different IP address and port.
 
 When starting an engine or client, a user must specify the key to use
 for that connection. Thus, in order to introduce a hostile controller, the
 attacker must convince the user to use the key associated with the
 hostile controller. As long as a user is diligent in only using keys from
 trusted sources, this attack is not possible.
 
 .. note::
 
     I may be wrong, the unauthorized controller may be easier to fake than this.
 
 Other security measures
 =======================
 
 A number of other measures are taken to further limit the security risks
 involved in running the IPython kernel.
 
 First, by default, the IPython controller listens on random port numbers.
 While this can be overridden by the user, in the default configuration, an
 attacker would have to do a port scan to even find a controller to attack.
 When coupled with the relatively short running time of a typical controller
 (on the order of hours), an attacker would have to work extremely hard and
 extremely *fast* to even find a running controller to attack.
 
 Second, much of the time, especially when run on supercomputers or clusters,
 the controller is running behind a firewall. Thus, for engines or client to
 connect to the controller:
 
 * The different processes have to all be behind the firewall.
 
 or:
 
 * The user has to use SSH port forwarding to tunnel the
   connections through the firewall.
    
 In either case, an attacker is presented with additional barriers that prevent
 attacking or even probing the system.
 
 Summary
 =======
 
 IPython's architecture has been carefully designed with security in mind. The
 capabilities based authentication model, in conjunction with SSH tunneled
 TCP/IP channels, address the core potential vulnerabilities in the system,
 while still enabling user's to use the system in open networks.
 
 Other questions
 ===============
 
 .. note::
 
     this does not apply to ZMQ, but I am sure there will be questions.
 
 About keys
 ----------
 
 Can you clarify the roles of the certificate and its keys versus the FURL,
 which is also called a key?
 
 The certificate created by IPython processes is a standard public key x509
 certificate, that is used by the SSL handshake protocol to setup encrypted
 channel between the controller and the IPython engine or client. This public
 and private key associated with this certificate are used only by the SSL
 handshake protocol in setting up this encrypted channel.
 
 The FURL serves a completely different and independent purpose from the
 key pair associated with the certificate. When we refer to a FURL as a
 key, we are using the word "key" in the capabilities based security model
 sense. This has nothing to do with "key" in the public/private key sense used
 in the SSL protocol.
 
 With that said the FURL is used as an cryptographic key, to grant
 IPython engines and clients access to particular capabilities that the
 controller offers.
 
 Self signed certificates
 ------------------------
 
 Is the controller creating a self-signed certificate? Is this created for per
 instance/session, one-time-setup or each-time the controller is started?
 
 The Foolscap network protocol, which handles the SSL protocol details, creates
 a self-signed x509 certificate using OpenSSL for each IPython process. The
 lifetime of the certificate is handled differently for the IPython controller
 and the engines/client.
 
 For the IPython engines and client, the certificate is only held in memory for
 the lifetime of its process. It is never written to disk.
 
 For the controller, the certificate can be created anew each time the
 controller starts or it can be created once and reused each time the
 controller starts. If at any point, the certificate is deleted, a new one is
 created the next time the controller starts.
 
 SSL private key
 ---------------
 
 How the private key (associated with the certificate) is distributed?
 
 In the usual implementation of the SSL protocol, the private key is never
 distributed. We follow this standard always.
 
 SSL versus Foolscap authentication
 ----------------------------------
 
 Many SSL connections only perform one sided authentication (the server to the
 client). How is the client authentication in IPython's system related to SSL
 authentication?
 
 We perform a two way SSL handshake in which both parties request and verify
 the certificate of their peer. This mutual authentication is handled by the
 SSL handshake and is separate and independent from the additional
 authentication steps that the CLIENT and SERVER perform after an encrypted
 channel is established.
 
 .. [RFC5246] <http://tools.ietf.org/html/rfc5246>
 
 .. [OpenSSH] <http://www.openssh.com/>
 .. [Paramiko] <http://www.lag.net/paramiko/>

docs/source/parallel/parallel_task.txt

@@ -1,418 +1,418 b''
 .. _parallel_task:
 
 ==========================
 The IPython task interface
 ==========================
 
 The task interface to the cluster presents the engines as a fault tolerant,
 dynamic load-balanced system of workers. Unlike the multiengine interface, in
 the task interface the user have no direct access to individual engines. By
 allowing the IPython scheduler to assign work, this interface is simultaneously
 simpler and more powerful.
 
 Best of all, the user can use both of these interfaces running at the same time
 to take advantage of their respective strengths. When the user can break up
 the user's work into segments that do not depend on previous execution, the
 task interface is ideal. But it also has more power and flexibility, allowing
 the user to guide the distribution of jobs, without having to assign tasks to
 engines explicitly.
 
 Starting the IPython controller and engines
 ===========================================
 
 To follow along with this tutorial, you will need to start the IPython
 controller and four IPython engines. The simplest way of doing this is to use
-the :command:`ipclusterz` command::
+the :command:`ipcluster` command::
 
-	$ ipclusterz start -n 4
+	$ ipcluster start -n 4
 	
 For more detailed information about starting the controller and engines, see
 our :ref:`introduction <ip1par>` to using IPython for parallel computing.
 
 Creating a ``Client`` instance
 ==============================
 
 The first step is to import the IPython :mod:`IPython.parallel.client`
 module and then create a :class:`.Client` instance, and we will also be using
 a :class:`LoadBalancedView`, here called `lview`:
 
 .. sourcecode:: ipython
 
     In [1]: from IPython.parallel import Client
 
     In [2]: rc = Client()
     
 
 This form assumes that the controller was started on localhost with default
 configuration. If not, the location of the controller must be given as an
 argument to the constructor:
 
 .. sourcecode:: ipython
 
     # for a visible LAN controller listening on an external port:
     In [2]: rc = Client('tcp://192.168.1.16:10101')
     # or to connect with a specific profile you have set up:
     In [3]: rc = Client(profile='mpi')
 
 For load-balanced execution, we will make use of a :class:`LoadBalancedView` object, which can
 be constructed via the client's :meth:`load_balanced_view` method:
 
 .. sourcecode:: ipython
 
     In [4]: lview = rc.load_balanced_view() # default load-balanced view
 
 .. seealso::
 
     For more information, see the in-depth explanation of :ref:`Views <parallel_details>`.
 
 
 Quick and easy parallelism
 ==========================
 
 In many cases, you simply want to apply a Python function to a sequence of
 objects, but *in parallel*. Like the multiengine interface, these can be
 implemented via the task interface. The exact same tools can perform these
 actions in load-balanced ways as well as multiplexed ways: a parallel version
 of :func:`map` and :func:`@parallel` function decorator. If one specifies the
 argument `balanced=True`, then they are dynamically load balanced. Thus, if the
 execution time per item varies significantly, you should use the versions in
 the task interface.
 
 Parallel map
 ------------
 
 To load-balance :meth:`map`,simply use a LoadBalancedView:
 
 .. sourcecode:: ipython
     
     In [62]: lview.block = True
     
 	In [63]: serial_result = map(lambda x:x**10, range(32))
 
 	In [64]: parallel_result = lview.map(lambda x:x**10, range(32))
 
 	In [65]: serial_result==parallel_result
 	Out[65]: True
 
 Parallel function decorator
 ---------------------------
 
 Parallel functions are just like normal function, but they can be called on
 sequences and *in parallel*. The multiengine interface provides a decorator
 that turns any Python function into a parallel function:
 
 .. sourcecode:: ipython
 
     In [10]: @lview.parallel()
        ....: def f(x):
        ....:     return 10.0*x**4
        ....:
 
     In [11]: f.map(range(32))    # this is done in parallel
     Out[11]: [0.0,10.0,160.0,...]
 
 .. _parallel_dependencies:
 
 Dependencies
 ============
 
 Often, pure atomic load-balancing is too primitive for your work. In these cases, you
 may want to associate some kind of `Dependency` that describes when, where, or whether
 a task can be run.  In IPython, we provide two types of dependencies: 
 `Functional Dependencies`_ and `Graph Dependencies`_
 
 .. note::
 
     It is important to note that the pure ZeroMQ scheduler does not support dependencies,
     and you will see errors or warnings if you try to use dependencies with the pure
     scheduler.
 
 Functional Dependencies
 -----------------------
 
 Functional dependencies are used to determine whether a given engine is capable of running
 a particular task.  This is implemented via a special :class:`Exception` class, 
 :class:`UnmetDependency`, found in `IPython.parallel.error`.  Its use is very simple: 
 if a task fails with an UnmetDependency exception, then the scheduler, instead of relaying
 the error up to the client like any other error, catches the error, and submits the task
 to a different engine.  This will repeat indefinitely, and a task will never be submitted
 to a given engine a second time.
 
 You can manually raise the :class:`UnmetDependency` yourself, but IPython has provided 
 some decorators for facilitating this behavior.
 
 There are two decorators and a class used for functional dependencies:
 
 .. sourcecode:: ipython
 
     In [9]: from IPython.parallel.dependency import depend, require, dependent
 
 @require
 ********
 
 The simplest sort of dependency is requiring that a Python module is available. The
 ``@require`` decorator lets you define a function that will only run on engines where names
 you specify are importable:
 
 .. sourcecode:: ipython
 
     In [10]: @require('numpy', 'zmq')
         ...: def myfunc():
         ...:     return dostuff()
 
 Now, any time you apply :func:`myfunc`, the task will only run on a machine that has
 numpy and pyzmq available, and when :func:`myfunc` is called, numpy and zmq will be imported.
 
 @depend
 *******
 
 The ``@depend`` decorator lets you decorate any function with any *other* function to
 evaluate the dependency. The dependency function will be called at the start of the task,
 and if it returns ``False``, then the dependency will be considered unmet, and the task
 will be assigned to another engine. If the dependency returns *anything other than
 ``False``*, the rest of the task will continue.
 
 .. sourcecode:: ipython
 
     In [10]: def platform_specific(plat):
         ...:    import sys
         ...:    return sys.platform == plat
     
     In [11]: @depend(platform_specific, 'darwin')
         ...: def mactask():
         ...:    do_mac_stuff()
 
     In [12]: @depend(platform_specific, 'nt')
         ...: def wintask():
         ...:    do_windows_stuff()
     
 In this case, any time you apply ``mytask``, it will only run on an OSX machine.
 ``@depend`` is just like ``apply``, in that it has a ``@depend(f,*args,**kwargs)``
 signature.
 
 dependents
 **********
 
 You don't have to use the decorators on your tasks, if for instance you may want
 to run tasks with a single function but varying dependencies, you can directly construct
 the :class:`dependent` object that the decorators use:
 
 .. sourcecode::ipython
 
     In [13]: def mytask(*args):
         ...:    dostuff()
     
     In [14]: mactask = dependent(mytask, platform_specific, 'darwin')
     # this is the same as decorating the declaration of mytask with @depend
     # but you can do it again:
     
     In [15]: wintask = dependent(mytask, platform_specific, 'nt')
     
     # in general:
     In [16]: t = dependent(f, g, *dargs, **dkwargs)
     
     # is equivalent to:
     In [17]: @depend(g, *dargs, **dkwargs)
         ...: def t(a,b,c):
         ...:     # contents of f
 
 Graph Dependencies
 ------------------
 
 Sometimes you want to restrict the time and/or location to run a given task as a function
 of the time and/or location of other tasks. This is implemented via a subclass of
 :class:`set`, called a :class:`Dependency`. A Dependency is just a set of `msg_ids`
 corresponding to tasks, and a few attributes to guide how to decide when the Dependency
 has been met.
 
 The switches we provide for interpreting whether a given dependency set has been met:
 
 any|all
     Whether the dependency is considered met if *any* of the dependencies are done, or
     only after *all* of them have finished.  This is set by a Dependency's :attr:`all`
     boolean attribute, which defaults to ``True``.
 
 success [default: True]
     Whether to consider tasks that succeeded as fulfilling dependencies.
 
 failure [default : False]
     Whether to consider tasks that failed as fulfilling dependencies.
     using `failure=True,success=False` is useful for setting up cleanup tasks, to be run
     only when tasks have failed.
 
 Sometimes you want to run a task after another, but only if that task succeeded. In this case,
 ``success`` should be ``True`` and ``failure`` should be ``False``. However sometimes you may
 not care whether the task succeeds, and always want the second task to run, in which case you
 should use `success=failure=True`. The default behavior is to only use successes.
 
 There are other switches for interpretation that are made at the *task* level.  These are
 specified via keyword arguments to the client's :meth:`apply` method.
 
 after,follow
     You may want to run a task *after* a given set of dependencies have been run and/or
     run it *where* another set of dependencies are met. To support this, every task has an
     `after` dependency to restrict time, and a `follow` dependency to restrict
     destination.
 
 timeout
     You may also want to set a time-limit for how long the scheduler should wait before a
     task's dependencies are met. This is done via a `timeout`, which defaults to 0, which
     indicates that the task should never timeout. If the timeout is reached, and the
     scheduler still hasn't been able to assign the task to an engine, the task will fail
     with a :class:`DependencyTimeout`.
 
 .. note::
 
     Dependencies only work within the task scheduler. You cannot instruct a load-balanced
     task to run after a job submitted via the MUX interface.
 
 The simplest form of Dependencies is with `all=True,success=True,failure=False`. In these cases,
 you can skip using Dependency objects, and just pass msg_ids or AsyncResult objects as the
 `follow` and `after` keywords to :meth:`client.apply`:
 
 .. sourcecode:: ipython
 
     In [14]: client.block=False
 
     In [15]: ar = lview.apply(f, args, kwargs)
 
     In [16]: ar2 = lview.apply(f2)
 
     In [17]: ar3 = lview.apply_with_flags(f3, after=[ar,ar2])
 
     In [17]: ar4 = lview.apply_with_flags(f3, follow=[ar], timeout=2.5)
 
 
 .. seealso::
 
     Some parallel workloads can be described as a `Directed Acyclic Graph
     <http://en.wikipedia.org/wiki/Directed_acyclic_graph>`_, or DAG. See :ref:`DAG
     Dependencies <dag_dependencies>` for an example demonstrating how to use map a NetworkX DAG
     onto task dependencies.
 
 
 
 Impossible Dependencies
 ***********************
 
 The schedulers do perform some analysis on graph dependencies to determine whether they
 are not possible to be met. If the scheduler does discover that a dependency cannot be
 met, then the task will fail with an :class:`ImpossibleDependency` error. This way, if the
 scheduler realized that a task can never be run, it won't sit indefinitely in the
 scheduler clogging the pipeline.
 
 The basic cases that are checked:
 
 * depending on nonexistent messages
 * `follow` dependencies were run on more than one machine and `all=True`
 * any dependencies failed and `all=True,success=True,failures=False`
 * all dependencies failed and `all=False,success=True,failure=False`
 
 .. warning::
 
     This analysis has not been proven to be rigorous, so it is likely possible for tasks
     to become impossible to run in obscure situations, so a timeout may be a good choice.
 
 .. _parallel_schedulers:
 
 Schedulers
 ==========
 
 There are a variety of valid ways to determine where jobs should be assigned in a 
 load-balancing situation.  In IPython, we support several standard schemes, and
 even make it easy to define your own.  The scheme can be selected via the ``--scheme``
-argument to :command:`ipcontrollerz`, or in the :attr:`HubFactory.scheme` attribute
+argument to :command:`ipcontroller`, or in the :attr:`HubFactory.scheme` attribute
 of a controller config object.
 
 The built-in routing schemes:
 
 To select one of these schemes, simply do::
 
-    $ ipcontrollerz --scheme <schemename>
+    $ ipcontroller --scheme <schemename>
     for instance:
-    $ ipcontrollerz --scheme lru
+    $ ipcontroller --scheme lru
 
 lru: Least Recently Used
 
     Always assign work to the least-recently-used engine.  A close relative of
     round-robin, it will be fair with respect to the number of tasks, agnostic 
     with respect to runtime of each task.
 
 plainrandom: Plain Random
 
     Randomly picks an engine on which to run.
 
 twobin: Two-Bin Random
 
     **Requires numpy**
     
      Pick two engines at random, and use the LRU of the two. This is known to be better
     than plain random in many cases, but requires a small amount of computation.
 
 leastload: Least Load
 
     **This is the default scheme**
 
     Always assign tasks to the engine with the fewest outstanding tasks (LRU breaks tie).
 
 weighted: Weighted Two-Bin Random
 
     **Requires numpy**
     
     Pick two engines at random using the number of outstanding tasks as inverse weights,
     and use the one with the lower load.
 
 
 Pure ZMQ Scheduler
 ------------------
 
 For maximum throughput, the 'pure' scheme is not Python at all, but a C-level
 :class:`MonitoredQueue` from PyZMQ, which uses a ZeroMQ ``XREQ`` socket to perform all
 load-balancing. This scheduler does not support any of the advanced features of the Python
 :class:`.Scheduler`.
 
 Disabled features when using the ZMQ Scheduler:
 
 * Engine unregistration
     Task farming will be disabled if an engine unregisters.  
     Further, if an engine is unregistered during computation, the scheduler may not recover.
 * Dependencies
     Since there is no Python logic inside the Scheduler, routing decisions cannot be made
     based on message content.
 * Early destination notification
     The Python schedulers know which engine gets which task, and notify the Hub.  This 
     allows graceful handling of Engines coming and going.  There is no way to know
     where ZeroMQ messages have gone, so there is no way to know what tasks are on which
     engine until they *finish*.  This makes recovery from engine shutdown very difficult.
 
 
 .. note::
 
     TODO: performance comparisons
 
 
 More details
 ============
 
 The :class:`LoadBalancedView` has many more powerful features that allow quite a bit
 of flexibility in how tasks are defined and run. The next places to look are
 in the following classes:
 
 * :class:`IPython.parallel.view.LoadBalancedView`
 * :class:`IPython.parallel.asyncresult.AsyncResult`
 * :meth:`IPython.parallel.view.LoadBalancedView.apply`
 * :mod:`IPython.parallel.dependency`
 
 The following is an overview of how to use these classes together:
 
 1. Create a :class:`Client` and :class:`LoadBalancedView`
 2. Define some functions to be run as tasks
 3. Submit your tasks to using the :meth:`apply` method of your
    :class:`LoadBalancedView` instance.
 4. Use :meth:`Client.get_result` to get the results of the
    tasks, or use the :meth:`AsyncResult.get` method of the results to wait
    for and then receive the results.
 
 .. seealso::
 
     A demo of :ref:`DAG Dependencies <dag_dependencies>` with NetworkX and IPython.

docs/source/parallel/parallel_winhpc.txt

@@ -1,334 +1,334 b''
 ============================================
 Getting started with Windows HPC Server 2008
 ============================================
 
 .. note::
 
     Not adapted to zmq yet
 
 Introduction
 ============
 
 The Python programming language is an increasingly popular language for
 numerical computing. This is due to a unique combination of factors. First,
 Python is a high-level and *interactive* language that is well matched to
 interactive numerical work. Second, it is easy (often times trivial) to
 integrate legacy C/C++/Fortran code into Python. Third, a large number of
 high-quality open source projects provide all the needed building blocks for
 numerical computing: numerical arrays (NumPy), algorithms (SciPy), 2D/3D
 Visualization (Matplotlib, Mayavi, Chaco), Symbolic Mathematics (Sage, Sympy)
 and others.
 
 The IPython project is a core part of this open-source toolchain and is
 focused on creating a comprehensive environment for interactive and
 exploratory computing in the Python programming language. It enables all of
 the above tools to be used interactively and consists of two main components:
 
 * An enhanced interactive Python shell with support for interactive plotting
   and visualization.
 * An architecture for interactive parallel computing.
 
 With these components, it is possible to perform all aspects of a parallel
 computation interactively. This type of workflow is particularly relevant in
 scientific and numerical computing where algorithms, code and data are
 continually evolving as the user/developer explores a problem. The broad
 treads in computing (commodity clusters, multicore, cloud computing, etc.)
 make these capabilities of IPython particularly relevant.
 
 While IPython is a cross platform tool, it has particularly strong support for
 Windows based compute clusters running Windows HPC Server 2008. This document
 describes how to get started with IPython on Windows HPC Server 2008. The
 content and emphasis here is practical: installing IPython, configuring
 IPython to use the Windows job scheduler and running example parallel programs
 interactively. A more complete description of IPython's parallel computing
 capabilities can be found in IPython's online documentation
 (http://ipython.scipy.org/moin/Documentation).
 
 Setting up your Windows cluster
 ===============================
 
 This document assumes that you already have a cluster running Windows
 HPC Server 2008. Here is a broad overview of what is involved with setting up
 such a cluster:
 
 1. Install Windows Server 2008 on the head and compute nodes in the cluster.
 2. Setup the network configuration on each host. Each host should have a 
    static IP address.
 3. On the head node, activate the "Active Directory Domain Services" role 
    and make the head node the domain controller.
 4. Join the compute nodes to the newly created Active Directory (AD) domain.
 5. Setup user accounts in the domain with shared home directories.
 6. Install the HPC Pack 2008 on the head node to create a cluster.
 7. Install the HPC Pack 2008 on the compute nodes.
 
 More details about installing and configuring Windows HPC Server 2008 can be
 found on the Windows HPC Home Page (http://www.microsoft.com/hpc). Regardless
 of what steps you follow to set up your cluster, the remainder of this
 document will assume that:
 
 * There are domain users that can log on to the AD domain and submit jobs
   to the cluster scheduler.
 * These domain users have shared home directories. While shared home
   directories are not required to use IPython, they make it much easier to
   use IPython.
 
 Installation of IPython and its dependencies
 ============================================
 
 IPython and all of its dependencies are freely available and open source.
 These packages provide a powerful and cost-effective approach to numerical and
 scientific computing on Windows. The following dependencies are needed to run
 IPython on Windows:
 
 * Python 2.6 or 2.7 (http://www.python.org)
 * pywin32 (http://sourceforge.net/projects/pywin32/)
 * PyReadline (https://launchpad.net/pyreadline)
 * pyzmq (http://github.com/zeromq/pyzmq/downloads)
 * IPython (http://ipython.scipy.org)
 
 In addition, the following dependencies are needed to run the demos described
 in this document.
 
 * NumPy and SciPy (http://www.scipy.org)
 * Matplotlib (http://matplotlib.sourceforge.net/)
 
 The easiest way of obtaining these dependencies is through the Enthought
 Python Distribution (EPD) (http://www.enthought.com/products/epd.php). EPD is
 produced by Enthought, Inc. and contains all of these packages and others in a
 single installer and is available free for academic users. While it is also
 possible to download and install each package individually, this is a tedious
 process. Thus, we highly recommend using EPD to install these packages on
 Windows.
 
 Regardless of how you install the dependencies, here are the steps you will
 need to follow:
 
 1. Install all of the packages listed above, either individually or using EPD
    on the head node, compute nodes and user workstations.
 
 2. Make sure that :file:`C:\\Python27` and :file:`C:\\Python27\\Scripts` are
    in the system :envvar:`%PATH%` variable on each node.
 
 3. Install the latest development version of IPython. This can be done by
    downloading the the development version from the IPython website
    (http://ipython.scipy.org) and following the installation instructions.
 
 Further details about installing IPython or its dependencies can be found in
 the online IPython documentation (http://ipython.scipy.org/moin/Documentation)
 Once you are finished with the installation, you can try IPython out by
 opening a Windows Command Prompt and typing ``ipython``. This will
 start IPython's interactive shell and you should see something like the
 following screenshot:
 
 .. image:: ipython_shell.*
 
 Starting an IPython cluster
 ===========================
 
 To use IPython's parallel computing capabilities, you will need to start an
 IPython cluster. An IPython cluster consists of one controller and multiple
 engines:
 
 IPython controller
     The IPython controller manages the engines and acts as a gateway between 
     the engines and the client, which runs in the user's interactive IPython
     session. The controller is started using the :command:`ipcontroller`
     command.
 
 IPython engine
     IPython engines run a user's Python code in parallel on the compute nodes. 
     Engines are starting using the :command:`ipengine` command.
 
 Once these processes are started, a user can run Python code interactively and
 in parallel on the engines from within the IPython shell using an appropriate
 client. This includes the ability to interact with, plot and visualize data
 from the engines.
 
-IPython has a command line program called :command:`ipclusterz` that automates
+IPython has a command line program called :command:`ipcluster` that automates
 all aspects of starting the controller and engines on the compute nodes.
-:command:`ipclusterz` has full support for the Windows HPC job scheduler,
-meaning that :command:`ipclusterz` can use this job scheduler to start the
+:command:`ipcluster` has full support for the Windows HPC job scheduler,
+meaning that :command:`ipcluster` can use this job scheduler to start the
 controller and engines. In our experience, the Windows HPC job scheduler is
 particularly well suited for interactive applications, such as IPython. Once
-:command:`ipclusterz` is configured properly, a user can start an IPython
+:command:`ipcluster` is configured properly, a user can start an IPython
 cluster from their local workstation almost instantly, without having to log
 on to the head node (as is typically required by Unix based job schedulers).
 This enables a user to move seamlessly between serial and parallel
 computations.
 
-In this section we show how to use :command:`ipclusterz` to start an IPython
+In this section we show how to use :command:`ipcluster` to start an IPython
 cluster using the Windows HPC Server 2008 job scheduler. To make sure that
-:command:`ipclusterz` is installed and working properly, you should first try
+:command:`ipcluster` is installed and working properly, you should first try
 to start an IPython cluster on your local host. To do this, open a Windows
 Command Prompt and type the following command::
 
-    ipclusterz start -n 2
+    ipcluster start -n 2
 
 You should see a number of messages printed to the screen, ending with 
 "IPython cluster: started". The result should look something like the following
 screenshot:
 
 .. image:: ipcluster_start.*
 
 At this point, the controller and two engines are running on your local host.
 This configuration is useful for testing and for situations where you want to
 take advantage of multiple cores on your local computer. 
 
-Now that we have confirmed that :command:`ipclusterz` is working properly, we
+Now that we have confirmed that :command:`ipcluster` is working properly, we
 describe how to configure and run an IPython cluster on an actual compute
 cluster running Windows HPC Server 2008. Here is an outline of the needed
 steps:
 
-1. Create a cluster profile using: ``ipclusterz create -p mycluster``
+1. Create a cluster profile using: ``ipcluster create -p mycluster``
 
 2. Edit configuration files in the directory :file:`.ipython\\cluster_mycluster`
 
 3. Start the cluster using: ``ipcluser start -p mycluster -n 32``
 
 Creating a cluster profile
 --------------------------
 
 In most cases, you will have to create a cluster profile to use IPython on a
 cluster. A cluster profile is a name (like "mycluster") that is associated
 with a particular cluster configuration. The profile name is used by
-:command:`ipclusterz` when working with the cluster.
+:command:`ipcluster` when working with the cluster.
 
 Associated with each cluster profile is a cluster directory. This cluster
 directory is a specially named directory (typically located in the
 :file:`.ipython` subdirectory of your home directory) that contains the
 configuration files for a particular cluster profile, as well as log files and
 security keys. The naming convention for cluster directories is:
 :file:`cluster_<profile name>`. Thus, the cluster directory for a profile named
 "foo" would be :file:`.ipython\\cluster_foo`.
 
 To create a new cluster profile (named "mycluster") and the associated cluster
 directory, type the following command at the Windows Command Prompt::
 
-    ipclusterz create -p mycluster
+    ipcluster create -p mycluster
 
 The output of this command is shown in the screenshot below. Notice how
-:command:`ipclusterz` prints out the location of the newly created cluster
+:command:`ipcluster` prints out the location of the newly created cluster
 directory.
 
 .. image:: ipcluster_create.*
 
 Configuring a cluster profile
 -----------------------------
 
 Next, you will need to configure the newly created cluster profile by editing
 the following configuration files in the cluster directory:
 
-* :file:`ipclusterz_config.py`
+* :file:`ipcluster_config.py`
 * :file:`ipcontroller_config.py`
 * :file:`ipengine_config.py`
 
-When :command:`ipclusterz` is run, these configuration files are used to 
+When :command:`ipcluster` is run, these configuration files are used to 
 determine how the engines and controller will be started. In most cases,
 you will only have to set a few of the attributes in these files.
 
-To configure :command:`ipclusterz` to use the Windows HPC job scheduler, you
+To configure :command:`ipcluster` to use the Windows HPC job scheduler, you
 will need to edit the following attributes in the file
-:file:`ipclusterz_config.py`::
+:file:`ipcluster_config.py`::
 
-    # Set these at the top of the file to tell ipclusterz to use the
+    # Set these at the top of the file to tell ipcluster to use the
     # Windows HPC job scheduler.
     c.Global.controller_launcher = \
         'IPython.parallel.launcher.WindowsHPCControllerLauncher'
     c.Global.engine_launcher = \
         'IPython.parallel.launcher.WindowsHPCEngineSetLauncher'
 
     # Set these to the host name of the scheduler (head node) of your cluster.
     c.WindowsHPCControllerLauncher.scheduler = 'HEADNODE'
     c.WindowsHPCEngineSetLauncher.scheduler = 'HEADNODE'
 
 There are a number of other configuration attributes that can be set, but
 in most cases these will be sufficient to get you started.
 
 .. warning::
     If any of your configuration attributes involve specifying the location
     of shared directories or files, you must make sure that you use UNC paths
     like :file:`\\\\host\\share`. It is also important that you specify
     these paths using raw Python strings: ``r'\\host\share'`` to make sure
     that the backslashes are properly escaped.
 
 Starting the cluster profile
 ----------------------------
 
 Once a cluster profile has been configured, starting an IPython cluster using
 the profile is simple::
 
-    ipclusterz start -p mycluster -n 32
+    ipcluster start -p mycluster -n 32
 
-The ``-n`` option tells :command:`ipclusterz` how many engines to start (in
+The ``-n`` option tells :command:`ipcluster` how many engines to start (in
 this case 32). Stopping the cluster is as simple as typing Control-C.
 
 Using the HPC Job Manager
 -------------------------
 
-When ``ipclusterz start`` is run the first time, :command:`ipclusterz` creates
+When ``ipcluster start`` is run the first time, :command:`ipcluster` creates
 two XML job description files in the cluster directory:
 
 * :file:`ipcontroller_job.xml`
 * :file:`ipengineset_job.xml`
 
 Once these files have been created, they can be imported into the HPC Job
 Manager application. Then, the controller and engines for that profile can be
-started using the HPC Job Manager directly, without using :command:`ipclusterz`.
-However, anytime the cluster profile is re-configured, ``ipclusterz start``
+started using the HPC Job Manager directly, without using :command:`ipcluster`.
+However, anytime the cluster profile is re-configured, ``ipcluster start``
 must be run again to regenerate the XML job description files. The
 following screenshot shows what the HPC Job Manager interface looks like
 with a running IPython cluster.
 
 .. image:: hpc_job_manager.*
 
 Performing a simple interactive parallel computation
 ====================================================
 
 Once you have started your IPython cluster, you can start to use it. To do
 this, open up a new Windows Command Prompt and start up IPython's interactive
 shell by typing::
 
     ipython
 
 Then you can create a :class:`MultiEngineClient` instance for your profile and
 use the resulting instance to do a simple interactive parallel computation. In
 the code and screenshot that follows, we take a simple Python function and
 apply it to each element of an array of integers in parallel using the
 :meth:`MultiEngineClient.map` method:
 
 .. sourcecode:: ipython
 
     In [1]: from IPython.parallel import *
 
     In [2]: c = MultiEngineClient(profile='mycluster')
 
     In [3]: mec.get_ids()
     Out[3]: [0, 1, 2, 3, 4, 5, 67, 8, 9, 10, 11, 12, 13, 14]
 
     In [4]: def f(x):
        ...:     return x**10
 
     In [5]: mec.map(f, range(15))  # f is applied in parallel
     Out[5]:
     [0,
      1,
      1024,
      59049,
      1048576,
      9765625,
      60466176,
      282475249,
      1073741824,
      3486784401L,
      10000000000L,
      25937424601L,
      61917364224L,
      137858491849L,
      289254654976L]
 
 The :meth:`map` method has the same signature as Python's builtin :func:`map`
 function, but runs the calculation in parallel. More involved examples of using
 :class:`MultiEngineClient` are provided in the examples that follow.
 
 .. image:: mec_simple.*
 

setup.py

@@ -1,252 +1,252 b''
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 """Setup script for IPython.
 
 Under Posix environments it works like a typical setup.py script. 
 Under Windows, the command sdist is not supported, since IPython 
 requires utilities which are not available under Windows."""
 
 #-----------------------------------------------------------------------------
 #  Copyright (c) 2008-2010, IPython Development Team.
 #  Copyright (c) 2001-2007, Fernando Perez <[e-mail]>
 #  Copyright (c) 2001, Janko Hauser <[e-mail]>
 #  Copyright (c) 2001, Nathaniel Gray <[e-mail]>
 #
 #  Distributed under the terms of the Modified BSD License.
 #
 #  The full license is in the file COPYING.txt, distributed with this software.
 #-----------------------------------------------------------------------------
 
 #-----------------------------------------------------------------------------
 # Minimal Python version sanity check
 #-----------------------------------------------------------------------------
 
 import sys
 
 # This check is also made in IPython/__init__, don't forget to update both when
 # changing Python version requirements.
 if sys.version[0:3] < '2.6':
     error = """\
 ERROR: 'IPython requires Python Version 2.6 or above.'
 Exiting."""
     print >> sys.stderr, error
     sys.exit(1)
 
 # At least we're on the python version we need, move on.
 
 #-------------------------------------------------------------------------------
 # Imports
 #-------------------------------------------------------------------------------
 
 # Stdlib imports
 import os
 import shutil
 
 from glob import glob
 
 # BEFORE importing distutils, remove MANIFEST. distutils doesn't properly
 # update it when the contents of directories change.
 if os.path.exists('MANIFEST'): os.remove('MANIFEST')
 
 from distutils.core import setup
 
 # Our own imports
 from IPython.utils.path import target_update
 
 from setupbase import (
     setup_args, 
     find_packages, 
     find_package_data, 
     find_scripts,
     find_data_files,
     check_for_dependencies,
     record_commit_info,
 )
 
 isfile = os.path.isfile
 pjoin = os.path.join
 
 #-----------------------------------------------------------------------------
 # Function definitions
 #-----------------------------------------------------------------------------
 
 def cleanup():
     """Clean up the junk left around by the build process"""
     if "develop" not in sys.argv:
         try:
             shutil.rmtree('ipython.egg-info')
         except:
             try:
                 os.unlink('ipython.egg-info')
             except:
                 pass
 
 #-------------------------------------------------------------------------------
 # Handle OS specific things
 #-------------------------------------------------------------------------------
 
 if os.name == 'posix':
     os_name = 'posix'
 elif os.name in ['nt','dos']:
     os_name = 'windows'
 else:
     print 'Unsupported operating system:',os.name
     sys.exit(1)
 
 # Under Windows, 'sdist' has not been supported.  Now that the docs build with
 # Sphinx it might work, but let's not turn it on until someone confirms that it
 # actually works.
 if os_name == 'windows' and 'sdist' in sys.argv:
     print 'The sdist command is not available under Windows.  Exiting.'
     sys.exit(1)
 
 #-------------------------------------------------------------------------------
 # Things related to the IPython documentation
 #-------------------------------------------------------------------------------
 
 # update the manuals when building a source dist
 if len(sys.argv) >= 2 and sys.argv[1] in ('sdist','bdist_rpm'):
     import textwrap
 
     # List of things to be updated. Each entry is a triplet of args for
     # target_update()
     to_update = [
                   # FIXME - Disabled for now: we need to redo an automatic way
                   # of generating the magic info inside the rst.                
                   #('docs/magic.tex',
                   #['IPython/Magic.py'],
                   #"cd doc && ./update_magic.sh" ),
 
                  ('docs/man/ipcluster.1.gz',
                   ['docs/man/ipcluster.1'],
                   'cd docs/man && gzip -9c ipcluster.1 > ipcluster.1.gz'),
 
                  ('docs/man/ipcontroller.1.gz',
                   ['docs/man/ipcontroller.1'],
                   'cd docs/man && gzip -9c ipcontroller.1 > ipcontroller.1.gz'),
 
                  ('docs/man/ipengine.1.gz',
                   ['docs/man/ipengine.1'],
                   'cd docs/man && gzip -9c ipengine.1 > ipengine.1.gz'),
 
                  ('docs/man/ipython.1.gz',
                   ['docs/man/ipython.1'],
                   'cd docs/man && gzip -9c ipython.1 > ipython.1.gz'),
 
                  ('docs/man/ipython-wx.1.gz',
                   ['docs/man/ipython-wx.1'],
                   'cd docs/man && gzip -9c ipython-wx.1 > ipython-wx.1.gz'),
 
                  ('docs/man/ipythonx.1.gz',
                   ['docs/man/ipythonx.1'],
                   'cd docs/man && gzip -9c ipythonx.1 > ipythonx.1.gz'),
 
                  ('docs/man/irunner.1.gz',
                   ['docs/man/irunner.1'],
                   'cd docs/man && gzip -9c irunner.1 > irunner.1.gz'),
 
                  ('docs/man/pycolor.1.gz',
                   ['docs/man/pycolor.1'],
                   'cd docs/man && gzip -9c pycolor.1 > pycolor.1.gz'),
                  ]
 
     # Only build the docs if sphinx is present
     try:
         import sphinx
     except ImportError:
         pass
     else:
         # The Makefile calls the do_sphinx scripts to build html and pdf, so
         # just one target is enough to cover all manual generation
 
         # First, compute all the dependencies that can force us to rebuild the
         # docs.  Start with the main release file that contains metadata
         docdeps = ['IPython/core/release.py']
         # Inculde all the reST sources
         pjoin = os.path.join
         for dirpath,dirnames,filenames in os.walk('docs/source'):
             if dirpath in ['_static','_templates']:
                 continue
             docdeps += [ pjoin(dirpath,f) for f in filenames
                          if f.endswith('.txt') ]
         # and the examples
         for dirpath,dirnames,filenames in os.walk('docs/example'):
             docdeps += [ pjoin(dirpath,f) for f in filenames
                          if not f.endswith('~') ]
         # then, make them all dependencies for the main PDF (the html will get
         # auto-generated as well).
         to_update.append(
             ('docs/dist/ipython.pdf',
              docdeps,
              "cd docs && make dist")
             )
         
     [ target_update(*t) for t in to_update ]
     
 #---------------------------------------------------------------------------
 # Find all the packages, package data, scripts and data_files
 #---------------------------------------------------------------------------
 
 packages = find_packages()
 package_data = find_package_data()
 scripts = find_scripts()
 data_files = find_data_files()
 
 #---------------------------------------------------------------------------
 # Handle dependencies and setuptools specific things
 #---------------------------------------------------------------------------
 
 # For some commands, use setuptools.  Note that we do NOT list install here!
 # If you want a setuptools-enhanced install, just run 'setupegg.py install'
 if len(set(('develop', 'sdist', 'release', 'bdist_egg', 'bdist_rpm',
            'bdist', 'bdist_dumb', 'bdist_wininst', 'install_egg_info',
            'build_sphinx', 'egg_info', 'easy_install', 'upload',
             )).intersection(sys.argv)) > 0:
     import setuptools
 
 # This dict is used for passing extra arguments that are setuptools 
 # specific to setup
 setuptools_extra_args = {}
 
 if 'setuptools' in sys.modules:
     setuptools_extra_args['zip_safe'] = False
     setuptools_extra_args['entry_points'] = {
         'console_scripts': [
             'ipython = IPython.frontend.terminal.ipapp:launch_new_instance',
             'ipython-qtconsole = IPython.frontend.qt.console.ipythonqt:main',
             'pycolor = IPython.utils.PyColorize:main',
-            'ipcontrollerz = IPython.parallel.ipcontrollerapp:launch_new_instance',
-            'ipenginez = IPython.parallel.ipengineapp:launch_new_instance',
-            'iploggerz = IPython.parallel.iploggerapp:launch_new_instance',
-            'ipclusterz = IPython.parallel.ipclusterapp:launch_new_instance',
+            'ipcontroller = IPython.parallel.ipcontrollerapp:launch_new_instance',
+            'ipengine = IPython.parallel.ipengineapp:launch_new_instance',
+            'iplogger = IPython.parallel.iploggerapp:launch_new_instance',
+            'ipcluster = IPython.parallel.ipclusterapp:launch_new_instance',
             'iptest = IPython.testing.iptest:main',
             'irunner = IPython.lib.irunner:main'
         ]
     }
     setup_args['extras_require'] = dict(
-        zmq = 'pyzmq>=2.0.10',
+        zmq = 'pyzmq>=2.0.10.1',
         doc='Sphinx>=0.3',
         test='nose>=0.10.1',
         security='pyOpenSSL>=0.6'
     )
 else:
     # If we are running without setuptools, call this function which will
     # check for dependencies an inform the user what is needed.  This is
     # just to make life easy for users.
     check_for_dependencies()
 
 #---------------------------------------------------------------------------
 # Do the actual setup now
 #---------------------------------------------------------------------------
 
 setup_args['cmdclass'] = {'build_py': record_commit_info('IPython')}
 setup_args['packages'] = packages
 setup_args['package_data'] = package_data
 setup_args['scripts'] = scripts
 setup_args['data_files'] = data_files
 setup_args.update(setuptools_extra_args)
 
 
 if __name__ == '__main__':
     setup(**setup_args)
     cleanup()

setupbase.py

@@ -1,380 +1,380 b''
 # encoding: utf-8
 """
 This module defines the things that are used in setup.py for building IPython
 
 This includes:
 
     * The basic arguments to setup
     * Functions for finding things like packages, package data, etc.
     * A function for checking dependencies.
 """
 from __future__ import print_function
 
 #-------------------------------------------------------------------------------
 #  Copyright (C) 2008  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-------------------------------------------------------------------------------
 
 #-------------------------------------------------------------------------------
 # Imports
 #-------------------------------------------------------------------------------
 import os
 import sys
 
 from ConfigParser import ConfigParser
 from distutils.command.build_py import build_py
 from glob import glob
 
 from setupext import install_data_ext
 
 #-------------------------------------------------------------------------------
 # Useful globals and utility functions
 #-------------------------------------------------------------------------------
 
 # A few handy globals
 isfile = os.path.isfile
 pjoin = os.path.join
 
 def oscmd(s):
     print(">", s)
     os.system(s)
 
 # A little utility we'll need below, since glob() does NOT allow you to do
 # exclusion on multiple endings!
 def file_doesnt_endwith(test,endings):
     """Return true if test is a file and its name does NOT end with any
     of the strings listed in endings."""
     if not isfile(test):
         return False
     for e in endings:
         if test.endswith(e):
             return False
     return True
 
 #---------------------------------------------------------------------------
 # Basic project information
 #---------------------------------------------------------------------------
 
 # release.py contains version, authors, license, url, keywords, etc.
 execfile(pjoin('IPython','core','release.py'))
 
 # Create a dict with the basic information
 # This dict is eventually passed to setup after additional keys are added.
 setup_args = dict(
       name             = name,
       version          = version,
       description      = description,
       long_description = long_description,
       author           = author,
       author_email     = author_email,
       url              = url,
       download_url     = download_url,
       license          = license,
       platforms        = platforms,
       keywords         = keywords,
       cmdclass         = {'install_data': install_data_ext},
       )
 
 
 #---------------------------------------------------------------------------
 # Find packages
 #---------------------------------------------------------------------------
 
 def add_package(packages,pname,config=False,tests=False,scripts=False,
                 others=None):
     """
     Add a package to the list of packages, including certain subpackages.
     """
     packages.append('.'.join(['IPython',pname]))
     if config:
         packages.append('.'.join(['IPython',pname,'config']))
     if tests:
         packages.append('.'.join(['IPython',pname,'tests']))
     if scripts:
         packages.append('.'.join(['IPython',pname,'scripts']))
     if others is not None:
         for o in others:
             packages.append('.'.join(['IPython',pname,o]))
 
 def find_packages():
     """
     Find all of IPython's packages.
     """
     packages = ['IPython']
     add_package(packages, 'config', tests=True, others=['default','profile'])
     add_package(packages, 'core', tests=True)
     add_package(packages, 'deathrow', tests=True)
     add_package(packages, 'extensions')
     add_package(packages, 'external')
     add_package(packages, 'external.argparse')
     add_package(packages, 'external.configobj')
     add_package(packages, 'external.decorator')
     add_package(packages, 'external.decorators')
     add_package(packages, 'external.guid')
     add_package(packages, 'external.Itpl')
     add_package(packages, 'external.mglob')
     add_package(packages, 'external.path')
     add_package(packages, 'external.pexpect')
     add_package(packages, 'external.pyparsing')
     add_package(packages, 'external.simplegeneric')
     add_package(packages, 'external.ssh')
     add_package(packages, 'external.validate')
     add_package(packages, 'kernel')
     add_package(packages, 'frontend')
     add_package(packages, 'frontend.qt')
     add_package(packages, 'frontend.qt.console', tests=True)
     add_package(packages, 'frontend.terminal', tests=True)    
     add_package(packages, 'lib', tests=True)
+    add_package(packages, 'parallel', tests=True)
     add_package(packages, 'quarantine', tests=True)
     add_package(packages, 'scripts')
     add_package(packages, 'testing', tests=True)
     add_package(packages, 'testing.plugin', tests=False)
     add_package(packages, 'utils', tests=True)
     add_package(packages, 'zmq')
     add_package(packages, 'zmq.pylab')
-    add_package(packages, 'parallel')
     return packages
 
 #---------------------------------------------------------------------------
 # Find package data
 #---------------------------------------------------------------------------
 
 def find_package_data():
     """
     Find IPython's package_data.
     """
     # This is not enough for these things to appear in an sdist.
     # We need to muck with the MANIFEST to get this to work
     package_data = {
         'IPython.config.userconfig' : ['*'],
         'IPython.testing' : ['*.txt']
     }
     return package_data
 
 
 #---------------------------------------------------------------------------
 # Find data files
 #---------------------------------------------------------------------------
 
 def make_dir_struct(tag,base,out_base):
     """Make the directory structure of all files below a starting dir.
 
     This is just a convenience routine to help build a nested directory
     hierarchy because distutils is too stupid to do this by itself.
 
     XXX - this needs a proper docstring!
     """
     
     # we'll use these a lot below
     lbase = len(base)
     pathsep = os.path.sep
     lpathsep = len(pathsep)
     
     out = []
     for (dirpath,dirnames,filenames) in os.walk(base):
         # we need to strip out the dirpath from the base to map it to the
         # output (installation) path.  This requires possibly stripping the
         # path separator, because otherwise pjoin will not work correctly
         # (pjoin('foo/','/bar') returns '/bar').
         
         dp_eff = dirpath[lbase:]
         if dp_eff.startswith(pathsep):
             dp_eff = dp_eff[lpathsep:]
         # The output path must be anchored at the out_base marker    
         out_path = pjoin(out_base,dp_eff)
         # Now we can generate the final filenames. Since os.walk only produces
         # filenames, we must join back with the dirpath to get full valid file
         # paths:
         pfiles = [pjoin(dirpath,f) for f in filenames]
         # Finally, generate the entry we need, which is a pari of (output
         # path, files) for use as a data_files parameter in install_data.
         out.append((out_path, pfiles))
 
     return out
     
 
 def find_data_files():
     """
     Find IPython's data_files.
 
     Most of these are docs.
     """
     
     docdirbase  = pjoin('share', 'doc', 'ipython')
     manpagebase = pjoin('share', 'man', 'man1')
     
     # Simple file lists can be made by hand
     manpages  = filter(isfile, glob(pjoin('docs','man','*.1.gz')))
     igridhelpfiles = filter(isfile,
                             glob(pjoin('IPython','extensions','igrid_help.*')))
 
     # For nested structures, use the utility above
     example_files = make_dir_struct(
         'data',
         pjoin('docs','examples'),
         pjoin(docdirbase,'examples')
     )
     manual_files = make_dir_struct(
         'data',
         pjoin('docs','dist'),
         pjoin(docdirbase,'manual')
     )
 
     # And assemble the entire output list
     data_files = [ (manpagebase, manpages),
                    (pjoin(docdirbase, 'extensions'), igridhelpfiles),
                    ] + manual_files + example_files
 
     return data_files
 
 
 def make_man_update_target(manpage):
     """Return a target_update-compliant tuple for the given manpage.
 
     Parameters
     ----------
     manpage : string
       Name of the manpage, must include the section number (trailing number).
 
     Example
     -------
 
     >>> make_man_update_target('ipython.1') #doctest: +NORMALIZE_WHITESPACE
     ('docs/man/ipython.1.gz',
      ['docs/man/ipython.1'],
      'cd docs/man && gzip -9c ipython.1 > ipython.1.gz')
     """
     man_dir = pjoin('docs', 'man')
     manpage_gz = manpage + '.gz'
     manpath = pjoin(man_dir, manpage)
     manpath_gz = pjoin(man_dir, manpage_gz)
     gz_cmd = ( "cd %(man_dir)s && gzip -9c %(manpage)s > %(manpage_gz)s" %
                locals() )
     return (manpath_gz, [manpath], gz_cmd)
     
 #---------------------------------------------------------------------------
 # Find scripts
 #---------------------------------------------------------------------------
 
 def find_scripts():
     """
     Find IPython's scripts.
     """
     parallel_scripts = pjoin('IPython','parallel','scripts')
     main_scripts = pjoin('IPython','scripts')
     scripts = [
-               pjoin(parallel_scripts, 'ipenginez'),
-               pjoin(parallel_scripts, 'ipcontrollerz'),
-               pjoin(parallel_scripts, 'ipclusterz'),
-               pjoin(parallel_scripts, 'iploggerz'),
+               pjoin(parallel_scripts, 'ipengine'),
+               pjoin(parallel_scripts, 'ipcontroller'),
+               pjoin(parallel_scripts, 'ipcluster'),
+               pjoin(parallel_scripts, 'iplogger'),
                pjoin(main_scripts, 'ipython'),
                pjoin(main_scripts, 'ipython-qtconsole'),
                pjoin(main_scripts, 'pycolor'),
                pjoin(main_scripts, 'irunner'),
                pjoin(main_scripts, 'iptest')
               ]
     
     # Script to be run by the windows binary installer after the default setup
     # routine, to add shortcuts and similar windows-only things.  Windows
     # post-install scripts MUST reside in the scripts/ dir, otherwise distutils
     # doesn't find them.
     if 'bdist_wininst' in sys.argv:
         if len(sys.argv) > 2 and \
                ('sdist' in sys.argv or 'bdist_rpm' in sys.argv):
             print("ERROR: bdist_wininst must be run alone. Exiting.",
                   file=sys.stderr)
             sys.exit(1)
         scripts.append(pjoin('scripts','ipython_win_post_install.py'))
 
     return scripts
 
 #---------------------------------------------------------------------------
 # Verify all dependencies
 #---------------------------------------------------------------------------
 
 def check_for_dependencies():
     """Check for IPython's dependencies.
     
     This function should NOT be called if running under setuptools!
     """
     from setupext.setupext import (
         print_line, print_raw, print_status,
         check_for_sphinx, check_for_pygments,
         check_for_nose, check_for_pexpect,
         check_for_pyzmq
     )
     print_line()
     print_raw("BUILDING IPYTHON")
     print_status('python', sys.version)
     print_status('platform', sys.platform)
     if sys.platform == 'win32':
         print_status('Windows version', sys.getwindowsversion())
     
     print_raw("")
     print_raw("OPTIONAL DEPENDENCIES")
 
     check_for_sphinx()
     check_for_pygments()
     check_for_nose()
     check_for_pexpect()
     check_for_pyzmq()
 
 
 def record_commit_info(pkg_dir, build_cmd=build_py):
     """ Return extended build command class for recording commit
 
     The extended command tries to run git to find the current commit, getting
     the empty string if it fails.  It then writes the commit hash into a file
     in the `pkg_dir` path, named ``.git_commit_info.ini``.
 
     In due course this information can be used by the package after it is
     installed, to tell you what commit it was installed from if known.
 
     To make use of this system, you need a package with a .git_commit_info.ini
     file - e.g. ``myproject/.git_commit_info.ini`` - that might well look like
     this::
 
         # This is an ini file that may contain information about the code state
         [commit hash]
         # The line below may contain a valid hash if it has been substituted
         # during 'git archive'
         archive_subst_hash=$Format:%h$
         # This line may be modified by the install process
         install_hash=
 
     The .git_commit_info file above is also designed to be used with git
     substitution - so you probably also want a ``.gitattributes`` file in the
     root directory of your working tree that contains something like this::
 
        myproject/.git_commit_info.ini export-subst
 
     That will cause the ``.git_commit_info.ini`` file to get filled in by ``git
     archive`` - useful in case someone makes such an archive - for example with
     via the github 'download source' button.
 
     Although all the above will work as is, you might consider having something
     like a ``get_info()`` function in your package to display the commit
     information at the terminal.  See the ``pkg_info.py`` module in the nipy
     package for an example.
     """
     class MyBuildPy(build_cmd):
         ''' Subclass to write commit data into installation tree '''
         def run(self):
             build_py.run(self)
             import subprocess
             proc = subprocess.Popen('git rev-parse --short HEAD',
                                     stdout=subprocess.PIPE,
                                     stderr=subprocess.PIPE,
                                     shell=True)
             repo_commit, _ = proc.communicate()
             # We write the installation commit even if it's empty
             cfg_parser = ConfigParser()
             cfg_parser.read(pjoin(pkg_dir, '.git_commit_info.ini'))
             cfg_parser.set('commit hash', 'install_hash', repo_commit)
             out_pth = pjoin(self.build_lib, pkg_dir, '.git_commit_info.ini')
             out_file = open(out_pth, 'wt')
             cfg_parser.write(out_file)
             out_file.close()
     return MyBuildPy