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dagdeps.py
119 lines | 3.5 KiB | text/x-python | PythonLexer
"""Example for generating an arbitrary DAG as a dependency map.
This demo uses networkx to generate the graph.
Authors
-------
* MinRK
"""
import networkx as nx
from random import randint, random
from IPython import parallel
def randomwait():
import time
from random import random
time.sleep(random())
return time.time()
def random_dag(nodes, edges):
"""Generate a random Directed Acyclic Graph (DAG) with a given number of nodes and edges."""
G = nx.DiGraph()
for i in range(nodes):
G.add_node(i)
while edges > 0:
a = randint(0,nodes-1)
b=a
while b==a:
b = randint(0,nodes-1)
G.add_edge(a,b)
if nx.is_directed_acyclic_graph(G):
edges -= 1
else:
# we closed a loop!
G.remove_edge(a,b)
return G
def add_children(G, parent, level, n=2):
"""Add children recursively to a binary tree."""
if level == 0:
return
for i in range(n):
child = parent+str(i)
G.add_node(child)
G.add_edge(parent,child)
add_children(G, child, level-1, n)
def make_bintree(levels):
"""Make a symmetrical binary tree with @levels"""
G = nx.DiGraph()
root = '0'
G.add_node(root)
add_children(G, root, levels, 2)
return G
def submit_jobs(view, G, jobs):
"""Submit jobs via client where G describes the time dependencies."""
results = {}
for node in nx.topological_sort(G):
with view.temp_flags(after=[ results[n] for n in G.predecessors(node) ]):
results[node] = view.apply(jobs[node])
return results
def validate_tree(G, results):
"""Validate that jobs executed after their dependencies."""
for node in G:
started = results[node].metadata.started
for parent in G.predecessors(node):
finished = results[parent].metadata.completed
assert started > finished, "%s should have happened after %s"%(node, parent)
def main(nodes, edges):
"""Generate a random graph, submit jobs, then validate that the
dependency order was enforced.
Finally, plot the graph, with time on the x-axis, and
in-degree on the y (just for spread). All arrows must
point at least slightly to the right if the graph is valid.
"""
from matplotlib import pyplot as plt
from matplotlib.dates import date2num
from matplotlib.cm import gist_rainbow
print "building DAG"
G = random_dag(nodes, edges)
jobs = {}
pos = {}
colors = {}
for node in G:
jobs[node] = randomwait
client = parallel.Client()
view = client.load_balanced_view()
print "submitting %i tasks with %i dependencies"%(nodes,edges)
results = submit_jobs(view, G, jobs)
print "waiting for results"
view.wait()
print "done"
for node in G:
md = results[node].metadata
start = date2num(md.started)
runtime = date2num(md.completed) - start
pos[node] = (start, runtime)
colors[node] = md.engine_id
validate_tree(G, results)
nx.draw(G, pos, node_list=colors.keys(), node_color=colors.values(), cmap=gist_rainbow,
with_labels=False)
x,y = zip(*pos.values())
xmin,ymin = map(min, (x,y))
xmax,ymax = map(max, (x,y))
xscale = xmax-xmin
yscale = ymax-ymin
plt.xlim(xmin-xscale*.1,xmax+xscale*.1)
plt.ylim(ymin-yscale*.1,ymax+yscale*.1)
return G,results
if __name__ == '__main__':
from matplotlib import pyplot as plt
# main(5,10)
main(32,96)
plt.show()