Reproducible research and reusable acyclic workflows in Python. Execute code on HPC systems as if you executed them on your personal computer!

Overview

Reproducible research and reusable acyclic workflows in Python. Execute code on HPC systems as if you executed them on your machine!

Motivation

Would you like fully reproducible research or reusable workflows that seamlessly run on HPC clusters? Tired of writing and managing large Slurm submission scripts? Do you have comment out large parts of your pipeline whenever its results have been generated? Don't waste your precious time! awflow allows you to directly describe complex pipelines in Python, that run on your personal computer and large HPC clusters.

import awflow as aw
import glob
import numpy as np

n = 100000
tasks = 10

@aw.cpus(4)  # Request 4 CPU cores
@aw.memory("4GB")  # Request 4 GB of RAM
@aw.postcondition(aw.num_files('pi-*.npy', 10))
@aw.tasks(tasks)  # Requests '10' parallel tasks
def estimate(task_index):
    print("Executing task {} / {}.".format(task_index + 1, tasks))
    x = np.random.random(n)
    y = np.random.random(n)
    pi_estimate = (x**2 + y**2 <= 1)
    np.save('pi-' + str(task_index) + '.npy', pi_estimate)

@aw.dependency(estimate)
def merge():
    files = glob.glob('pi-*.npy')
    stack = np.vstack([np.load(f) for f in files])
    np.save('pi.npy', stack.sum() / (n * tasks) * 4)

@aw.dependency(merge)
@aw.postcondition(aw.exists('pi.npy'))  # Prevent execution if postcondition is satisfied.
def show_result():
    print("Pi:", np.load('pi.npy'))

aw.execute()

Executing this Python program (python examples/pi.py) on a Slurm HPC cluster will launch the following jobs.

           1803299       all    merge username PD       0:00      1 (Dependency)
           1803300       all show_res username PD       0:00      1 (Dependency)
     1803298_[6-9]       all estimate username PD       0:00      1 (Resources)
         1803298_3       all estimate username  R       0:01      1 compute-xx
         1803298_4       all estimate username  R       0:01      1 compute-xx
         1803298_5       all estimate username  R       0:01      1 compute-xx

Check the examples directory and guide to explore the functionality.

Installation

The awflow package is available on PyPi, which means it is installable via pip.

[email protected]:~ $ pip install awflow

If you would like the latest features, you can install it using this Git repository.

[email protected]:~ $ pip install git+https://github.com/JoeriHermans/awflow

If you would like to run the examples as well, be sure to install the optional example dependencies.

[email protected]:~ $ pip install 'awflow[examples]'

Usage

The core concept in awflow is the notion of a task. Essentially, this is a method that will be executed in your workflow. Tasks are represented as a node in a directed graph. In doing so, we can easily specify (task) dependencies. In addition, we can attribute properties to tasks using decorators defined by awflow. This allows you to specify things like CPU cores, GPU's and even postconditions. Follow the guide for additional examples and descriptions.

Decorators

TODO

Workflow storage

By default, workflows will be stored in the current working direction within the ./workflows folder. If desired, a central storage directory can be used by specifying the AWFLOW_STORAGE environment variable.

The awflow utility

This package comes with a utility program to manage submitted, failed, and pending workflows. Its functionality can be inspected by executing awflow -h. In addition, to streamline the management of workflows, we recommend to give every workflow as specific name to easily identify a workflow. This name does not have to be unique for every distinct workflow execution.

aw.execute(name=r'Some name')

Executing awflow list after submitting the pipeline with python pipeline.py [args] will yield.

[email protected]:~ $ awflow list
  Postconditions      Status      Backend     Name          Location
 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
  50%                 Running     Slurm       Some name     /home/jhermans/awflow/examples/.workflows/tmpntmc712a

Modules

[email protected]:~ $ awflow cancel [workflow] TODO

[email protected]:~ $ awflow clear TODO

[email protected]:~ $ awflow list TODO

[email protected]:~ $ awflow inspect [workflow] TODO

Contributing

See CONTRIBUTING.md.

Roadmap

  • Documentation
  • README

License

As described in the LICENSE file.

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Comments
  • [BUG] conda activation crashes standalone execution

    [BUG] conda activation crashes standalone execution

    Issue description

    In the standalone backend on Unix systems, the os.system(command) used here

    https://github.com/JoeriHermans/awflow/blob/1fcf255debfbc18d39a6b2baa387bbc85050209d/awflow/backends/standalone/executor.py#L53-L60

    actually calls /bin/sh. For some OS, like Ubuntu, sh links to dash which does not support the scripting features required by conda activations. This results in runtime errors like

    sh: 5: /home/username/miniconda3/envs/envname/etc/conda/activate.d/activate-binutils_linux-64.sh: Syntax error: "(" unexpected
    

    Proposed solution

    A solution would be to change the shell with which the commands are called. This is possible thanks to the subprocess package. A good default would be bash as almost all Unix systems use it.

        if node.tasks > 1:
            for task_index in range(node.tasks):
                task_command = command + ' ' + str(task_index)
                return_code = subprocess.call(task_command, shell=True, executable='/bin/bash')
        else:
            return_code = subprocess.call(command, shell=True, executable='/bin/bash')
    

    One could also add a way to change this default. Additionally, wouldn't it be better to launch the tasks as background jobs for the standalone backend (simply add & at the end of the command) ?

    bug 
    opened by francois-rozet 1
  • [BUG] pip install fails for version 0.0.4

    [BUG] pip install fails for version 0.0.4

    $ pip install awflow==0.0.4
    Collecting awflow==0.0.4
      Using cached awflow-0.0.4.tar.gz (19 kB)
        ERROR: Command errored out with exit status 1:
         command: /home/francois/awf/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-ou4rxs3q/awflow/setup.py'"'"'; __file__='"'"'/tmp/pip-install-ou4rxs3q/awflow/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-install-ou4rxs3q/awflow/pip-egg-info
             cwd: /tmp/pip-install-ou4rxs3q/awflow/
        Complete output (7 lines):
        Traceback (most recent call last):
          File "<string>", line 1, in <module>
          File "/tmp/pip-install-ou4rxs3q/awflow/setup.py", line 54, in <module>
            'examples': _load_requirements('requirements_examples.txt')
          File "/tmp/pip-install-ou4rxs3q/awflow/setup.py", line 17, in _load_requirements
            with open(file_name, 'r') as file:
        FileNotFoundError: [Errno 2] No such file or directory: 'requirements_examples.txt'
        ----------------------------------------
    ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
    
    bug high priority 
    opened by francois-rozet 1
  • Jobs submitted with awflow doesn't work with Multiprocessing.pool

    Jobs submitted with awflow doesn't work with Multiprocessing.pool

    Hi,

    I tried submitting a few jobs with awflow but somehow each time I run it with slurm backend it never produces a pool.starmap and the process simply times out on cluster. `0 0 8196756 5.1g 85664 S 0.0 1.0 2:12.27 python 790517 rnath 20 0 7953388 5.0g 12020 S 0.0 1.0 0:01.66 python

    790518 rnath 20 0 7953388 5.0g 12020 S 0.0 1.0 0:01.45 python

    790519 rnath 20 0 7953388 5.0g 12020 S 0.0 1.0 0:01.76 python

    790520 rnath 20 0 7953388 5.0g 12020 S 0.0 1.0 0:02.02 python

    790521 rnath 20 0 7953388 5.0g 12020 S 0.0 1.0 0:01.99 python `

    An example of what happens in the cluster where the processes are spawned but each process uses 0 % of the cpu slurmstepd: error: *** JOB 1933332 ON compute-04 CANCELLED AT 2022-04-08T19:33:26 DUE TO TIME LIMIT ***

    opened by digirak 0
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