A robust DAG implementation for parallel programming
Project description
paradag
paradag
a robust DAG package for easy parallel execution.
paradag
is implemented in pure python and totally independent of any
third party packages.
Directed acyclic graph (DAG) is commonly used as a dependency graph. It could be used to describe the dependencies of tasks. The order of task executions must comply with the dependencies, where tasks with direct or indirect path must run in sequence, and tasks without any connection could run in parallel.
Installation
$ pip install paradag
Create a DAG
Before running tasks, first create a DAG, with each vertex representing a task. The vertex of DAG instance could be any hashable object, like integer, string, tuple of hashable objects, instance of user-defined class, etc.
from paradag import DAG
class Vtx(object):
def __init__(self, v):
self.__value = v
vtx = Vtx(999)
dag = DAG()
dag.add_vertex(123, 'abcde', 'xyz', ('a', 'b', 3), vtx)
dag.add_edge(123, 'abcde') # 123 -> 'abcde'
dag.add_edge('abcde', ('a', 'b', 3), vtx) # 'abcde' -> ('a', 'b', 3), 'abcde' -> vtx
add_edge
accepts one starting vertex and one or more ending vertices.
Please pay attention not to make a cycle with add_edge
,
which will raise a DAGCycleError
.
The common DAG properties are accessible:
print(dag.vertex_size())
print(dag.edge_size())
print(dag.successors('abcde'))
print(dag.predecessors(vtx))
print(dag.all_starts())
print(dag.all_terminals())
Run tasks in sequence
Write your executor and optionally a selector. The executor handles the real execution for each vertex.
from paradag import dag_run
from paradag import SequentialProcessor
class CustomExecutor:
def param(self, vertex):
return vertex
def execute(self, param):
print('Executing:', param)
print(dag_run(dag, processor=SequentialProcessor(), executor=CustomExecutor()))
dag_run
is the core function for task scheduling.
Run tasks in parallel
Run tasks in parallel is quite similar, while only change the processor
to MultiThreadProcessor
.
from paradag import MultiThreadProcessor
dag_run(dag, processor=MultiThreadProcessor(), executor=CustomExecutor())
The default selector FullSelector
will try to find as many tasks
as possible which could run in parallel.
This could be adjusted with custom selector.
The following selector will only allow at most 4 tasks running in parallel.
class CustomSelector(object):
def select(self, running, idle):
task_number = max(0, 4-len(running))
return list(idle)[:task_number]
dag_run(dag, processor=MultiThreadProcessor(), selector=CustomSelector(), executor=CustomExecutor())
Once you are using MultiThreadProcessor
, great attentions must be
paid that execute
of executor could run in parallel. Try not to modify
any variables outside the execute
function, and all parameters should
be passed by the param
argument. Also make sure that the return values
generated from param
function are independent.
Get task running status
The executor could also implement the optional methods which could get the task running status.
class CustomExecutor:
def param(self, vertex):
return vertex
def execute(self, param):
print('Executing:', param)
def report_start(self, vertices):
print('Start to run:', vertices)
def report_running(self, vertices):
print('Current running:', vertices)
def report_finish(self, vertices_result):
for vertex, result in vertices_result:
print('Finished running {0} with result: {1}'.format(vertex, result))
dag_run(dag, processor=MultiThreadProcessor(), executor=CustomExecutor())
Deliver result to descendants
In case the result for one task should be used for its descendants,
deliver
method could be implemented in executor.
class CustomExecutor:
def __init__(self):
self.__level = {}
def param(self, vertex):
return self.__level.get(vertex, 0)
def execute(self, param):
return param + 1
def report_finish(self, vertices_result):
for vertex, result in vertices_result:
print('Vertex {0} finished, level: {1}'.format(vertex, result))
def deliver(self, vertex, result):
self.__level[vertex] = result
The result from parent will be delivered to the vertex before execution.
Topological sorting
Topological sorting
could also be done by paradag.dag_run
function.
The return value of dag_run
could be considered as
the result of topological sorting.
A simple topological sorting without any execution:
from paradag import SingleSelector, RandomSelector, ShuffleSelector
dag = DAG()
dag.add_vertex(1, 2, 3, 4, 5)
dag.add_edge(1, 4)
dag.add_edge(4, 2, 5)
print(dag_run(dag))
print(dag_run(dag, selector=SingleSelector()))
print(dag_run(dag, selector=RandomSelector()))
print(dag_run(dag, selector=ShuffleSelector()))
The solution for topological sorting is not necessarily unique, and the final orders may vary with different selectors.
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