dawgz 0.2.2

Directed Acyclic Workflow Graph Scheduling

Directed Acyclic Workflow Graph Scheduling

Would you like fully reproducible and reusable workflows that run on HPC clusters as seamlessly as on your machine? Tired of writing and managing large Slurm submission scripts? Do you have to comment out large parts of your pipeline whenever something failed? Hate YAML and config files? Then dawgz is made for you!

The dawgz package allows you to define and execute complex workflows, directly in Python. It provides a lightweight interface that enables to define jobs along with their dependencies, requirements, postconditions, etc. After defining the workflow, you can schedule target jobs in a single line of code and all their dependencies will be automatically scheduled as well. Importantly, the scheduling backend can be changed with a single parameter, which enables to execute the same workflow on your personal computer and HPC clusters.

dawgz is the successor of awflow

Installation

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+https://github.com/francois-rozet/dawgz

Getting started

In dawgz, a job is a Python function decorated by the @dawgz.job decorator. This decorator allows to define the job's parameters, like its name, whether it is a job array, the resources it needs, etc. Importantly, a job can have other jobs as dependencies, which implicitely defines a workflow graph. The @dawgz.after decorator is used to declare such dependencies. Additionally, to ensure that the job completed successfuly, postconditions can be added with the @dawgz.ensure decorator.

Finally, dawgz provides the dawgz.schedule function in order to schedule target jobs with a selected backend. This function automatically takes care of scheduling the dependency graph of the target jobs.

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', timelimit='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

    np.save(f'pi_{i}.npy', within_circle)

@after(generate)
@job(cpus=2, ram='4GB', timelimit='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, backend='local')

In the preceding example, 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 verifying whether a file exists after the job's completion. If it is not the case, the job raises an AssertionError at runtime. The job estimate only starts after generate succeeded.

Executing this script with the 'local' backend displays

$ python examples/pi.py
Task 1 / 5
Task 2 / 5
Task 3 / 5
Task 4 / 5
Task 5 / 5
π ≈ 3.1418666666666666

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

$ squeue -u username
             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
           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 compute-xx
         1868831_1       all generate username  R       0:01      1 compute-xx

Check out the examples and the interface to discover the functionalities of dawgz.

Interface

Decorators

The package provides five decorators:

• @dawgz.job registers a function as a job, optionally with parameters (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', timelimit='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')
  @job
  def b():
  

• @dawgz.waitfor declares whether the job waits 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)
  @waitfor('any')
  @job
  def c():
  

• @dawgz.require adds a precondition to a job, i.e. a condition that must be True prior to the execution of the job. If preconditions are not satisfied, the job is never executed. In the following example, d requires tmp to be an existing directory.

  @require(lambda: os.path.isdir('tmp'))
  @job
  def d():
  

• @dawgz.ensure adds a postcondition to a job, i.e. a condition that must be True after the execution of the job. In the following example, e ensures that the file tmp/dump.log exists.

  @after(d)
  @ensure(lambda: os.path.exists('tmp/dump.log'))
  @job
  def e():
  

  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 graph.

Backends

Currently, dawgz.schedule only supports two backends: local and slurm.

• local schedules locally the jobs by waiting asynchronously for dependencies to finish before submitting each job. It does not take the required resources into account.
• slurm submits the jobs to the Slurm workload manager by generating automatically the sbatch submission scripts.