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Directed Acyclic Workflow Graph Scheduling

Project description

Directed Acyclic Workflow Graph Scheduling

Would you like fully reproducible and reusable experiments that run on HPC clusters as seamlessly as on your machine? Do you have to comment out large parts of your pipelines whenever something failed? Tired of writing and submitting Slurm scripts? Then dawgz is made for you!

The dawgz package provides a lightweight and intuitive Python interface to declare jobs along with their dependencies, requirements, settings, etc. A single line of code is then needed to execute automatically all or part of the workflow, while complying to the dependencies. Importantly, dawgz can also hand over the execution to resource management backends like Slurm, which enables to execute the same workflow on your machine and HPC clusters.


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

pip install dawgz

Alternatively, if you need the latest features, you can install it using

pip install git+

Getting started

In dawgz, a job is a Python function decorated by @dawgz.job. This decorator allows to define the job's parameters, like its name, whether it is a job array, the resources it needs, etc. The job's dependencies are declared with the @dawgz.after decorator. At last, the dawgz.schedule function takes care of scheduling the jobs and their dependencies, with a selected backend. For more information, check out the interface and the examples.

Follows a small example demonstrating how one could use dawgz to calculate π (very roughly) using the Monte Carlo method. We define two jobs: generate and estimate. The former is a job array, meaning that it is executed concurrently for all values of i = 0 up to tasks - 1. It also defines a postcondition ensuring that the file pi_{i}.npy exists after the job's completion. The job estimate has generate as dependency, meaning it should only start after generate succeeded.

import glob
import numpy as np
import os

from dawgz import job, after, ensure, schedule

samples = 10000
tasks = 5

@ensure(lambda i: os.path.exists(f'pi_{i}.npy'))
@job(array=tasks, cpus=1, ram='2GB', time='5:00')
def generate(i: int):
    print(f'Task {i + 1} / {tasks}')

    x = np.random.random(samples)
    y = np.random.random(samples)
    within_circle = x**2 + y**2 <= 1'pi_{i}.npy', within_circle)

@job(cpus=2, ram='4GB', time='15:00')
def estimate():
    files = glob.glob('pi_*.npy')
    stack = np.vstack([np.load(f) for f in files])
    pi_estimate = stack.mean() * 4

    print(f'π ≈ {pi_estimate}')

schedule(estimate, name='', backend='async')

Running this script with the 'async' backend displays

$ python examples/
Task 1 / 5
Task 2 / 5
Task 3 / 5
Task 4 / 5
Task 5 / 5
π ≈ 3.141865

Alternatively, on a Slurm HPC cluster, changing the backend to 'slurm' results in the following job queue.

$ squeue -u username
           1868832       all estimate username PD       0:00      1 (Dependency)
     1868831_[2-4]       all generate username PD       0:00      1 (Resources)
         1868831_0       all generate username  R       0:01      1 node-x
         1868831_1       all generate username  R       0:01      1 node-y

In addition to the Python interface, dawgz provides a simple command-line interface (CLI) to list the scheduled workflows, the jobs of a workflow or the output(s) of a job.

$ dawgz
    Name    ID        Date                 Backend      Jobs    Errors
--  ------  --------  -------------------  ---------  ------  --------
 0   8094aa20  2022-02-28 16:37:58  async           2         0
 1   9cc409fd  2022-02-28 16:38:33  slurm           2         0
$ dawgz 1
    Name                ID  State
--  -------------  -------  -------
 0  generate[0-4]  1868831  MIXED
 1  estimate       1868832  PENDING
$ dawgz 1 0
    Name         State      Output
--  -----------  ---------  ----------
 0  generate[0]  COMPLETED  Task 1 / 5
 1  generate[1]  COMPLETED  Task 2 / 5
 2  generate[2]  RUNNING
 3  generate[3]  RUNNING
 4  generate[4]  PENDING



The package provides five decorators:

  • @dawgz.job registers a function as a job, with its settings (name, array, resources, ...). It should always be the first (lowest) decorator. In the following example, a is a job with the name 'A' and a time limit of one hour.

    @job(name='A', time='01:00:00')
    def a():
  • @dawgz.after adds one or more dependencies to a job. By default, the job waits for its dependencies to complete with success. The desired status can be set to 'success' (default), 'failure' or 'any'. In the following example, b waits for a to complete with 'failure'.

    @after(a, status='failure')
    def b():
  • @dawgz.waitfor declares whether the job has to wait for 'all' (default) or 'any' of its dependencies to be satisfied before starting. In the following example, c waits for either a or b to complete (with success).

    @after(a, b)
    def c():
  • @dawgz.ensure adds a postcondition to a job, i.e. a condition that must be True after the execution of the job. Not satisfying all postconditions after execution results in an AssertionError at runtime. In the following example, d ensures that the file log.txt exists.

    @ensure(lambda: os.path.exists('log.txt'))
    def d():

    Traditionally, postconditions are only necessary indicators that the job completed with success. In dawgz, they are considered both necessary and sufficient indicators. Therefore, postconditions can be used to detect jobs that have already been executed and prune them out from the workflow, if requested.

  • @dawgz.context specifies the context of a job, i.e. the values of (non-local) variables on which it depends. Providing a context prevents the global value of variables from affecting the job execution. In the following example, the variable var is set to always be 42 within e.

    def e():


Currently, dawgz.schedule supports three backends: async, dummy and slurm.

  • async waits asynchronously for dependencies to complete before executing each job. The jobs are executed by the current Python interpreter.
  • dummy is equivalent to async, but instead of executing the jobs, prints their name before and after a short (random) sleep time. The main use of dummy is debugging.
  • slurm submits the jobs to the Slurm workload manager by automatically generating sbatch submission scripts.

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