About TaskGraph

TaskGraph is a library that was developed to help manage complicated computational software pipelines consisting of long running individual tasks. Many of these tasks could be executed in parallel, almost all of them wrote results to disk, and many times results could be reused from part of the pipeline. TaskGraph manages all of this for you. With it you can schedule tasks with dependencies, avoid recomputing results that have already been computed, and allot multiple CPU cores to execute tasks in parallel if desired.

TaskGraph Dependencies

Task Graph is written in pure Python, but if the psutils package is installed the distributed multiprocessing processes will be niced.

Example Use

Install TaskGraph with

pip install taskgraph

Then

def _create_list_on_disk(value, length, target_path):
    """Create a numpy array on disk filled with value of `size`."""
    target_list = [value] * length
    pickle.dump(target_list, open(target_path, 'wb'))


def _sum_lists_from_disk(list_a_path, list_b_path, target_path):
    """Read two lists, add them and save result."""
    list_a = pickle.load(open(list_a_path, 'rb'))
    list_b = pickle.load(open(list_b_path, 'rb'))
    target_list = []
    for a, b in zip(list_a, list_b):
        target_list.append(a+b)
    pickle.dump(target_list, open(target_path, 'wb'))

# create a taskgraph that uses 4 multiprocessing subprocesses when possible
task_graph = taskgraph.TaskGraph(self.workspace_dir, 4)
target_a_path = os.path.join(self.workspace_dir, 'a.dat')
target_b_path = os.path.join(self.workspace_dir, 'b.dat')
result_path = os.path.join(self.workspace_dir, 'result.dat')
result_2_path = os.path.join(self.workspace_dir, 'result2.dat')
value_a = 5
value_b = 10
list_len = 10
task_a = task_graph.add_task(
    target=_create_list_on_disk,
    args=(value_a, list_len, target_a_path),
    target_path_list=[target_a_path])
task_b = task_graph.add_task(
    target=_create_list_on_disk,
    args=(value_b, list_len, target_b_path),
    target_path_list=[target_b_path])
sum_task = task_graph.add_task(
    target=_sum_lists_from_disk,
    args=(target_a_path, target_b_path, result_path),
    target_path_list=[result_path],
    dependent_task_list=[task_a, task_b])

task_graph.close()
task_graph.join()

# expect that result is a list `list_len` long with `value_a+value_b` in it
result = pickle.load(open(result_path, 'rb'))

Running Tests

Taskgraph includes a tox configuration for automating builds across python versions 2.7, 3.6, and whether psutil is installed. To execute all tests on all platforms, run:

$ tox

Alternatively, if you're only trying to run tests on a single configuration (say, python 3.5 without psutil), you'd run:

$ tox -e py36

Or if you'd like to run the tests for the combination of Python 2.7 with psutil, you'd run:

$ tox -e py27-psutil