A lightweight task processing library written in pure Python
Project description
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 atluigi.Task
s, 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, Const, Cache
class Choose(Task):
""" Compute the binomial coefficient. """
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 `.get_result()`:
return self.prev1.get_result() + self.prev2.get_result()
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 Composition
The arguments of the __init__
method can be anything JSON serializable + Future
s,
where Future
is either Task
or Const
.
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 FutureList
and FutureDict
:
from taskproc import FutureList, FutureDict
class SummarizeScores(Task):
def __init__(self, task_dict: dict[str, Future[float]]):
self.score_list = FutureList([MyScore(i) for i in range(10)])
self.score_dict = FutureDict(task_dict)
def run_task(self) -> float:
# `.get_result()` evaluates `FutureList[float]` and `FutureDict[str, float]` into
# `list[float]` and `dict[str, float]`, respectively.
return sum(self.score_dict.get_result().values()) / len(self.score_dict.get_result())
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 list or a dictionary, one can directly access its element by indexing.
The result is also a Future
.
class MultiOutputTask(Task):
def run_task(self) -> dict[str, int]:
return {'foo': 42, ...}
class DownstreamTask(Task):
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
- Better UX
- Add option to not cache result (need to address timestamp peeking and value passing).
- Validate non-Future task argument with type hint.
- Optional
- Simple task graph visualizer.
- Pydantic/dataclass support in task arguments (as an incompatible, but better-UX object with TypedDict).
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