exca 0.2.5

Execution and caching tool for python

Exca - ⚔

Execute and cache seamlessly in python.

Quick install

pip install exca

Full documentation

Documentation is available at https://facebookresearch.github.io/exca/

Basic overview

exca provides simple decorators to:

• execute a (hierarchy of) computation(s) either locally or on distant nodes,
• cache the result.

The problem:

In ML pipelines, the use of a simple python function, such as my_task:

import numpy as np

def my_task(param: int = 12) -> float:
    return param * np.random.rand()

often requires cumbersome overheads to (1) configure the parameters, (2) submit the job on a cluster, (3) cache the results: e.g.

import pickle
from pathlib import Path
import submitit

# Configure
param = 12

# Check task has already been executed
filepath = tmp_path / f'result-{param}.npy'
if not filepath.exists():

    # Submit job on cluster
    executor = submitit.AutoExecutor(cluster=None, folder=tmp_path)
    job = executor.submit(my_task, param)
    result = job.result()

    # Cache result
    with filepath.open("wb") as f:
        pickle.dump(result, f)

These overheads lead to several issues, such as debugging, handling hierarchical execution and properly saving the results (ending in the classic 'result-parm12-v2_final_FIX.npy').

The solution:

exca can be used to decorate the method of a pydantic model so as to seamlessly configure its execution and caching:

import numpy as np
import pydantic
import exca as xk

class MyTask(pydantic.BaseModel):
    param: int = 12
    infra: xk.TaskInfra = xk.TaskInfra()

    @infra.apply
    def process(self) -> float:
        return self.param * np.random.rand()


task = MyTask(param=1, infra={"folder": tmp_path, "cluster": "auto"})
out = task.process()  # runs on slurm if available
# calling process again will load the cache and not a new random number
assert out == task.process()

See the API reference for all the details

Quick comparison

feature \ tool	lru_cache	hydra	submitit	exca
RAM cache	✔			✔
file cache				✔
remote compute		✔	✔	✔
pure python (vs commandline)	✔		✔	✔
hierarchical config		✔		✔

Contributing

See the CONTRIBUTING file for how to help out.

Citing

@misc{exca,
    author = {J. Rapin and J.-R. King},
    title = {{Exca - Execution and caching}},
    year = {2024},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/facebookresearch/exca}},
}

License

exca is MIT licensed, as found in the LICENSE file. Also check-out Meto Open Source Terms of Use and Privacy Policy.