taskproc 0.17.0

A lightweight task processing library written in pure Python

taskproc

A lightweight pipeline building/execution/management tool written in pure Python. Internally, it depends on DiskCache, cloudpickle networkx and concurrent.futures.

Why taskproc?

I needed a pipeline-handling library that is thin and flexible as much as possible.

• Luigi is not flexible enough: The definition of the dependencies and the definition of the task computation is tightly coupled at luigi.Tasks, which is super cumbersome if one tries to edit the pipeline structure without changing the computation of each task.
• Airflow is too big and clumsy: It requires a message broker backend separately installed and run in background. It is also incompatible with non-pip package manager (such as Poetry).
• Most of the existing libraries tend to build their own ecosystems that unnecessarily forces the user to follow the specific way of handling pipelines.

taskproc aims to provide a language construct for defining computation by composition, ideally as simple as Python's built-in sytax of functions, with the support of automatic and configurable parallel execution and cache management.

Features

• Decomposing long and complex computation into tasks, i.e., smaller units of work with dependencies.
• Executing them in a distributed way, supporting multithreading/multiprocessing and local container/cluster-based dispatching.
• Automatically creating/discarding/reusing caches per task.

Nonfeatures

• Periodic scheduling
• Automatic retry
• External service integration (GCP, AWS, ...)
• Graphical user interface

Installation

pip install taskproc

Example

See here for a typical usage of taskproc.

Documentation

Defining task

Pipeline is a directed acyclic graph (DAG) of tasks with a single sink node (i.e., final task), where task is a unit of work represented with a class. Each task and its upstream dependencies are specified with a class definition like so:

from taskproc import Task, Requires, Const, Cache

class Choose(Task):
    """ Compute the binomial coefficient. """
    # Inside a task, we first declare the values that must be computed in upstream.
    # In this example, `Choose(n, k)` depends on `Choose(n - 1, k - 1)` and `Choose(n - 1, k)`,
    # so it requires two `int` values.
    prev1: Requires[int]
    prev2: Requires[int]

    def __init__(self, n: int, k: int):
        # The upstream tasks and the other instance attributes are prepared here.
        # It thus recursively defines all the tasks we need to run this task,
        # i.e., the entire upstream pipeline.

        if 0 < k < n:
            self.prev1 = Choose(n - 1, k - 1)
            self.prev2 = Choose(n - 1, k)
        elif k == 0 or k == n:
            # We can just pass a value to a requirement slot directly without running tasks.
            self.prev1 = Const(0)
            self.prev2 = Const(1)
        else:
            raise ValueError(f'{(n, k)}')

    def run_task(self) -> int:
        # Here we define the main computation of the task,
        # which is delayed until it is necessary.

        # The return values of the prerequisite tasks are accessible via the descriptors:
        return self.prev1 + self.prev2

with Cache('./cache'):
    # Construct a task with its upstreams.
    # Instantiation of `Task` should be inside `Cache`.
    task = Choose(6, 3)

# To run the task graph, use the `run_graph()` method.
ans, stats = task.run_graph()  # `ans` should be 6 Choose 3, which is 20.

# It greedily executes all the necessary tasks in the graph as parallel as possible
# and then produces the return value of the task on which we call `run_graph()`, as well as the execution stats.
# The return values of the intermediate tasks are cached at `./cache`
# and reused on the fly whenever possible.

Deleting cache

It is possible to selectively discard cache:

with Cache('./cache'):
    # After some modificaiton of `Choose(3, 3)`,
    # selectively discard the cache corresponding to the modification.
    Choose(3, 3).clear_task()

    # `ans` is recomputed tracing back to the computation of `Choose(3, 3)`.
    ans, _ = Choose(6, 3).run_graph()
    
    # Delete all the cache associated with `Choose`.
    Choose.clear_all_tasks()            

Task IO

The arguments of the __init__ method can be anything JSON serializable + Futures:

class MyTask(Task):
    def __init__(self, param1, param2):
        ...

with Cache('./cache'):
    MyTask(
        param1={
            'upstream_task0': UpstreamTask(),
            'other_params': [1, 2],
            ...
        },
        param2={ ... }
    }).run_graph()

List/dict of upstream tasks can be registered with RequiresList and RequiresDict:

from taskproc import RequiresList, RequiresDict

class SummarizeScores(Task):
    score_list: RequiresList[float]
    score_dict: RequiresDict[str, float]

    def __init__(self, task_dict: dict[str, Future[float]]):
        self.score_list = [MyScore(i) for i in range(10)]
        self.score_dict = task_dict

    def run_task(self) -> float:
        # At runtime `self.score_list` and `self.score_dict` are evaluated as
        # `list[float]` and `dict[str, float]`, respectively.
        return sum(self.score_dict.values()) / len(self.score_dict)

The output of the run_task method should be serializable with cloudpickle, which is then compressed with gzip. The compression level can be changed as follows (defaults to 9).

class NoCompressionTask(Task):
    _task_compress_level = 0
    ...

If the output is a dictionary, one can directly access its element:

class MultiOutputTask(Task):
    def run_task(self) -> dict[str, int]:
        return {'foo': 42, ...}

class DownstreamTask(Task):
    dep: Requires[int]

    def __init__(self):
        self.dep = MultiOutputTask()['foo']

Data directories

Use task.task_directory to get a fresh path dedicated to each task. The directory is automatically created and managed along with the task cache: The contents of the directory are cleared at each task call and persist until the task is cleared.

class TrainModel(Task):
    def run_task(self) -> str:
        ...
        model_path = self.task_directory / 'model.bin'
        model.save(model_path)
        return model_path

Job scheduling and prefixes

Tasks can be run with job schedulers using _task_prefix_command, which will be inserted just before each task call.

class TaskWithJobScheduler(Task):
    _task_prefix_command = 'jbsub -interactive -tty -queue x86_1h -cores 16+1 -mem 64g'
    ...

Execution policy configuration

One can control the task execution with concurrent.futures.Executor class:

from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor

class MyTask(Task):
    ...

with Cache('./cache'):
    # Limit the number of parallel workers
    MyTask().run_graph(executor=ProcessPoolExecutor(max_workers=2))
    
    # Thread-based parallelism
    MyTask().run_graph(executor=ThreadPoolExecutor())

One can also control the concurrency at a task/channel level:

class TaskUsingGPU(Task):
    _task_channel = 'gpu'
    ...

class AnotherTaskUsingGPU(Task):
    _task_channel = ['gpu', 'memory']
    ...

with Cache('./cache'):
    # Queue-level concurrency control
    SomeDownstreamTask().run_graph(rate_limits={'gpu': 1})
    SomeDownstreamTask().run_graph(rate_limits={'memory': 1})
    
    # Task-level concurrency control
    SomeDownstreamTask().run_graph(rate_limits={TaskUsingGPU.task_name: 1})

Commandline tool

Task have a utility classmethod to run with commandline arguments. For example,

# taskfile.py

class Main(Task):
    ...


if __name__ == '__main__':
    Main.cli()

Use --help option for more details.

Built-in properties/methods

Below is the list of the built-in properties/methods of Task. Do not override these attributes in the subclass.

Name	Owner	Type	Description
task_name	class	property	String id of the task class
task_id	instance	property	Integer id of the task, unique within the same task class
task_args	instance	property	The arguments of the task in JSON
task_directory	instance	property	Path to the data directory of the task
task_stdout	instance	property	Path to the task's stdout
task_stderr	instance	property	Path to the task's stderr
run_task	instance	method	Run the task
run_graph	instance	method	Run the task after necessary upstream tasks and save the results in the cache
get_task_result	instance	method	Directly get the result of the task (fails if the cache is missing)
to_json	instance	method	Serialize itself as a JSON dictionary
clear_task	instance	method	Clear the cache of the task instance
clear_all_tasks	class	method	Clear the cache of the task class
cli	class	method	run_graph with command line arguments

TODO

• Pydantic/dataclass support in task arguments.
• Validate argument with schema.
• Rethink of descriptor design: one cannot re-assign descriptor-ed Future in __init__.
• Simple task graph visualizer.