From 57c7e48fab0d30bf7163c15a0ec804fa66732ceb 2011-11-28 01:47:42 From: MinRK Date: 2011-11-28 01:47:42 Subject: [PATCH] update some parallel docs * add ssh ipcontroller/ipengine example (closes #1017) * generated new performance numbers with SGE cluster * fix some remaining stale references * removed some screenshots, in favor of editable text closes #980 --- diff --git a/docs/source/parallel/figs/ipcluster_create.pdf b/docs/source/parallel/figs/ipcluster_create.pdf deleted file mode 100644 index b0b8586ce1e27f141c3f20eca2313a557d54eb33..0000000000000000000000000000000000000000 GIT binary patch literal 0 Hc$@VZh^+Vapy8K z=ggeU_uc#RK2PtGwQIk%YSpe%$?p_P>G45xVgJpm^z>UpEGMB zX)10E3+~(DS_1(2N6(+NJvNlM9=bCJ`$%YL$pRO!MvKz121in5T4~tV9=mg zIKKZ6!c*ywj(Pv1059)E9Nx;d;%}K@2!Doo_G=~3l#P>fPsk%CuYgo8b z=|2_Uyrp87b9m}g@;|xMe{wl0Ln?M16*F54b9bsgo|1ET0{yc)F)^xtfcgJVqYC_` z)L-NIF9f@Whnf4Il0V_8*tKomKehc+S9Vzo8!Kz~KXm?MZ1zsB8qTKX7AS&$0m(fL z*45eRkGY|8clEINOY&qMc2$cft2n!RnA%gZOWL@*J=LY09GsmT|F|pgf1Ov9|DD(U 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computation. In these examples, we will be using IPython's "pylab" mode, which enables interactive plotting using the @@ -110,17 +106,15 @@ results. The code to run this calculation in parallel is contained in :file:`docs/examples/parallel/parallelpi.py`. This code can be run in parallel using IPython by following these steps: -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. +1. Use :command:`ipcluster` to start 15 engines. We used 16 cores of an SGE linux + cluster (1 controller + 15 engines). 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 +When run on our 16 cores, we observe a speedup of 14.2x. This is slightly +less than linear scaling (16x) because the controller is also running on one of the cores. To emphasize the interactive nature of IPython, we now show how the @@ -135,7 +129,7 @@ calculation can also be run by simply typing the commands from # 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]: v = c[:] In [3]: c.ids Out[3]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] @@ -209,12 +203,12 @@ 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/parallel` +The code for this example can be found in the :file:`docs/examples/parallel/options` directory of the IPython source. The function :func:`price_options` in -:file:`mcpricer.py` implements the basic Monte Carlo pricing algorithm using +:file:`mckernel.py` implements the basic Monte Carlo pricing algorithm using the NumPy package and is shown here: -.. literalinclude:: ../../examples/parallel/options/mcpricer.py +.. literalinclude:: ../../examples/parallel/options/mckernel.py :language: python To run this code in parallel, we will use IPython's :class:`LoadBalancedView` class, @@ -227,7 +221,7 @@ be found in the file :file:`mcpricer.py`. The code in this file creates a volatilities and strike prices. The results are then plotted as a 2D contour plot using Matplotlib. -.. literalinclude:: ../../examples/parallel/options/mckernel.py +.. literalinclude:: ../../examples/parallel/options/mcpricer.py :language: python To use this code, start an IPython cluster using :command:`ipcluster`, open @@ -236,8 +230,9 @@ working directory and then type: .. sourcecode:: ipython - In [7]: run mckernel.py - Submitted tasks: [0, 1, 2, ...] + In [7]: run mcpricer.py + + Submitted tasks: 30 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 @@ -245,16 +240,16 @@ 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 [8]: plot_options(sigma_vals, strike_vals, prices['acall']) In [9]: plt.figure() Out[9]: - In [10]: plot_options(sigma_vals, K_vals, prices['aput']) + In [10]: plot_options(sigma_vals, strike_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 +These results are shown in the two figures below. On our 15 engines, the +entire calculation (15 strike prices, 15 volatilities, 100,000 paths for each) +took 37 seconds in parallel, giving a speedup of 14.1x, which is comparable to the speedup observed in our previous example. .. image:: figs/asian_call.* @@ -274,11 +269,7 @@ parallel architecture that have been demonstrated: 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:: +* We have run these examples on a cluster running RHEL 5 and Sun GridEngine. + IPython's built in support for SGE (and other batch systems) makes it easy + to get started with IPython's parallel capabilities. - The new parallel code has never been run on Windows HPC Server, so the last - conclusion is untested. diff --git a/docs/source/parallel/parallel_process.txt b/docs/source/parallel/parallel_process.txt index b151f61..dc1b356 100644 --- a/docs/source/parallel/parallel_process.txt +++ b/docs/source/parallel/parallel_process.txt @@ -181,7 +181,7 @@ Assuming that the default MPI config is sufficient. have not yet supported (such as Condor) Using :command:`ipcluster` in mpiexec/mpirun mode --------------------------------------------------- +------------------------------------------------- The mpiexec/mpirun mode is useful if you: @@ -243,7 +243,7 @@ More details on using MPI with IPython can be found :ref:`here `. Using :command:`ipcluster` in PBS mode ---------------------------------------- +-------------------------------------- The PBS mode uses the Portable Batch System (PBS) to start the engines. @@ -364,7 +364,7 @@ Additional configuration options can be found in the PBS section of :file:`ipclu Using :command:`ipcluster` in SSH mode ---------------------------------------- +-------------------------------------- The SSH mode uses :command:`ssh` to execute :command:`ipengine` on remote @@ -401,7 +401,7 @@ The controller's remote location and configuration can be specified: # 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 `profile-dir` argument. - # c.SSHControllerLauncher.program_args = ['--reuse', '--ip=*', '--profile-dir=/path/to/cd'] + # c.SSHControllerLauncher.controller_args = ['--reuse', '--ip=*', '--profile-dir=/path/to/cd'] .. note:: @@ -438,10 +438,11 @@ Current limitations of the SSH mode of :command:`ipcluster` are: Also, we are using shell scripts to setup and execute commands on remote hosts. * No file movement - This is a regression from 0.10, which moved connection files - around with scp. This will be improved, but not before 0.11 release. + around with scp. This will be improved, Pull Requests are welcome. + Using the :command:`ipcontroller` and :command:`ipengine` commands -==================================================================== +================================================================== It is also possible to use the :command:`ipcontroller` and :command:`ipengine` commands to start your controller and engines. This approach gives you full @@ -487,7 +488,15 @@ slightly more complicated, but the underlying ideas are the same: 1. Start the controller on a host using :command:`ipcontroller`. The controller must be instructed to listen on an interface visible to the engine machines, via the ``ip`` - command-line argument or ``HubFactory.ip`` in :file:`ipcontroller_config.py`. + command-line argument or ``HubFactory.ip`` in :file:`ipcontroller_config.py`:: + + $ ipcontroller --ip=192.168.1.16 + + .. sourcecode:: python + + # in ipcontroller_config.py + HubFactory.ip = '192.168.1.16' + 2. Copy :file:`ipcontroller-engine.json` from :file:`~/.ipython/profile_/security` on the controller's host to the host where the engines will run. 3. Use :command:`ipengine` on the engine's hosts to start the engines. @@ -553,6 +562,62 @@ loopback, and ssh tunnels will be used to connect engines to the controller:: Or if you want to start many engines on each node, the command `ipcluster engines --n=4` without any configuration is equivalent to running ipengine 4 times. +An example using ipcontroller/engine with ssh +--------------------------------------------- + +No configuration files are necessary to use ipcontroller/engine in an SSH environment +without a shared filesystem. You simply need to make sure that the controller is listening +on an interface visible to the engines, and move the connection file from the controller to +the engines. + +1. start the controller, listening on an ip-address visible to the engine machines:: + + [controller.host] $ ipcontroller --ip=192.168.1.16 + + [IPControllerApp] Using existing profile dir: u'/Users/me/.ipython/profile_default' + [IPControllerApp] Hub listening on tcp://192.168.1.16:63320 for registration. + [IPControllerApp] Hub using DB backend: 'IPython.parallel.controller.dictdb.DictDB' + [IPControllerApp] hub::created hub + [IPControllerApp] writing connection info to /Users/me/.ipython/profile_default/security/ipcontroller-client.json + [IPControllerApp] writing connection info to /Users/me/.ipython/profile_default/security/ipcontroller-engine.json + [IPControllerApp] task::using Python leastload Task scheduler + [IPControllerApp] Heartmonitor started + [IPControllerApp] Creating pid file: /Users/me/.ipython/profile_default/pid/ipcontroller.pid + Scheduler started [leastload] + +2. on each engine, fetch the connection file with scp:: + + [engine.host.n] $ scp controller.host:.ipython/profile_default/security/ipcontroller-engine.json ./ + + .. note:: + + The log output of ipcontroller above shows you where the json files were written. + They will be in :file:`~/.ipython` (or :file:`~/.config/ipython`) under + :file:`profile_default/security/ipcontroller-engine.json` + +3. start the engines, using the connection file:: + + [engine.host.n] $ ipengine --file=./ipcontroller-engine.json + +A couple of notes: + +* You can avoid having to fetch the connection file every time by adding ``--reuse`` flag + to ipcontroller, which instructs the controller to read the previous connection file for + connection info, rather than generate a new one with randomized ports. + +* In step 2, if you fetch the connection file directly into the security dir of a profile, + then you need not specify its path directly, only the profile (assumes the path exists, + otherwise you must create it first):: + + [engine.host.n] $ scp controller.host:.ipython/profile_default/security/ipcontroller-engine.json ~/.ipython/profile_ssh/security/ + [engine.host.n] $ ipengine --profile=ssh + + Of course, if you fetch the file into the default profile, no arguments must be passed to + ipengine at all. + +* Note that ipengine *did not* specify the ip argument. In general, it is unlikely for any + connection information to be specified at the command-line to ipengine, as all of this + information should be contained in the connection file written by ipcontroller. Make JSON files persistent -------------------------- diff --git a/docs/source/parallel/parallel_winhpc.txt b/docs/source/parallel/parallel_winhpc.txt index 6d140ad..c52e285 100644 --- a/docs/source/parallel/parallel_winhpc.txt +++ b/docs/source/parallel/parallel_winhpc.txt @@ -2,10 +2,6 @@ Getting started with Windows HPC Server 2008 ============================================ -.. note:: - - Not adapted to zmq yet - Introduction ============ @@ -118,9 +114,23 @@ the online IPython documentation (http://ipython.org/documentation.html) 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: +following:: + + Microsoft Windows [Version 6.0.6001] + Copyright (c) 2006 Microsoft Corporation. All rights reserved. + + Z:\>ipython + Python 2.7.2 (default, Jun 12 2011, 15:08:59) [MSC v.1500 32 bit (Intel)] + Type "copyright", "credits" or "license" for more information. + + IPython 0.12.dev -- An enhanced Interactive Python. + ? -> Introduction and overview of IPython's features. + %quickref -> Quick reference. + help -> Python's own help system. + object? -> Details about 'object', use 'object??' for extra details. + + In [1]: -.. image:: figs/ipython_shell.* Starting an IPython cluster =========================== @@ -162,13 +172,24 @@ cluster using the Windows HPC Server 2008 job scheduler. To make sure that to start an IPython cluster on your local host. To do this, open a Windows Command Prompt and type the following command:: - ipcluster 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: +You should see a number of messages printed to the screen. +The result should look something like this:: -.. image:: figs/ipcluster_start.* + Microsoft Windows [Version 6.1.7600] + Copyright (c) 2009 Microsoft Corporation. All rights reserved. + + Z:\>ipcluster start --profile=mycluster + [IPClusterStart] Using existing profile dir: u'\\\\blue\\domainusers$\\bgranger\\.ipython\\profile_mycluster' + [IPClusterStart] Starting ipcluster with [daemon=False] + [IPClusterStart] Creating pid file: \\blue\domainusers$\bgranger\.ipython\profile_mycluster\pid\ipcluster.pid + [IPClusterStart] Writing job description file: \\blue\domainusers$\bgranger\.ipython\profile_mycluster\ipcontroller_job.xml + [IPClusterStart] Starting Win HPC Job: job submit /jobfile:\\blue\domainusers$\bgranger\.ipython\profile_mycluster\ipcontroller_job.xml /scheduler:HEADNODE + [IPClusterStart] Starting 15 engines + [IPClusterStart] Writing job description file: \\blue\domainusers$\bgranger\.ipython\profile_mycluster\ipcontroller_job.xml + [IPClusterStart] Starting Win HPC Job: job submit /jobfile:\\blue\domainusers$\bgranger\.ipython\profile_mycluster\ipengineset_job.xml /scheduler:HEADNODE + 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 @@ -179,11 +200,11 @@ 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: ``ipython profile create --parallel profile=mycluster`` +1. Create a cluster profile using: ``ipython profile create mycluster --parallel`` 2. Edit configuration files in the directory :file:`.ipython\\cluster_mycluster` -3. Start the cluster using: ``ipcluser start profile=mycluster n=32`` +3. Start the cluster using: ``ipcluster start --profile=mycluster -n 32`` Creating a cluster profile -------------------------- @@ -207,10 +228,17 @@ directory, type the following command at the Windows Command Prompt:: ipython profile create --parallel --profile=mycluster The output of this command is shown in the screenshot below. Notice how -:command:`ipcluster` prints out the location of the newly created cluster -directory. +:command:`ipcluster` prints out the location of the newly created profile +directory:: + + Z:\>ipython profile create mycluster --parallel + [ProfileCreate] Generating default config file: u'\\\\blue\\domainusers$\\bgranger\\.ipython\\profile_mycluster\\ipython_config.py' + [ProfileCreate] Generating default config file: u'\\\\blue\\domainusers$\\bgranger\\.ipython\\profile_mycluster\\ipcontroller_config.py' + [ProfileCreate] Generating default config file: u'\\\\blue\\domainusers$\\bgranger\\.ipython\\profile_mycluster\\ipengine_config.py' + [ProfileCreate] Generating default config file: u'\\\\blue\\domainusers$\\bgranger\\.ipython\\profile_mycluster\\ipcluster_config.py' + [ProfileCreate] Generating default config file: u'\\\\blue\\domainusers$\\bgranger\\.ipython\\profile_mycluster\\iplogger_config.py' -.. image:: figs/ipcluster_create.* + Z:\> Configuring a cluster profile ----------------------------- @@ -245,7 +273,7 @@ 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 + like :file:`\\\\host\\share`. It is helpful to specify these paths using raw Python strings: ``r'\\host\share'`` to make sure that the backslashes are properly escaped. @@ -262,7 +290,7 @@ this case 32). Stopping the cluster is as simple as typing Control-C. Using the HPC Job Manager ------------------------- - +føø When ``ipcluster start`` is run the first time, :command:`ipcluster` creates two XML job description files in the cluster directory: @@ -288,26 +316,28 @@ shell by typing:: ipython -Then you can create a :class:`MultiEngineClient` instance for your profile and +Then you can create a :class:`DirectView` 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: +:meth:`DirectView.map` method: .. sourcecode:: ipython In [1]: from IPython.parallel import * - In [2]: c = MultiEngineClient(profile='mycluster') + In [2]: c = Client(profile='mycluster') - In [3]: mec.get_ids() - Out[3]: [0, 1, 2, 3, 4, 5, 67, 8, 9, 10, 11, 12, 13, 14] + In [3]: view = c[:] - In [4]: def f(x): + In [4]: c.ids + Out[4]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] + + In [5]: def f(x): ...: return x**10 - In [5]: mec.map(f, range(15)) # f is applied in parallel - Out[5]: + In [6]: view.map(f, range(15)) # f is applied in parallel + Out[6]: [0, 1, 1024, @@ -326,7 +356,5 @@ apply it to each element of an array of integers in parallel using the 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:: figs/mec_simple.* +:class:`DirectView` are provided in the examples that follow.