Skip to main content

A job management system for python

Project description

xqute

A job management system for Python, designed to simplify job scheduling and execution with support for multiple schedulers and plugins.

Features

  • Written in async for high performance
  • Plugin system for extensibility
  • Scheduler adaptor for various backends
  • Job retrying and pipeline halting on failure
  • Support for cloud-based working directories
  • Built-in support for Google Batch Jobs, Slurm, SGE, SSH, and container schedulers

Installation

pip install xqute

A Toy Example

import asyncio
from xqute import Xqute

async def main():
    # Initialize Xqute with 3 jobs allowed to run concurrently
    xqute = Xqute(forks=3)
    for _ in range(10):
        await xqute.feed(['sleep', '1'])
    await xqute.run_until_complete()

if __name__ == '__main__':
    asyncio.run(main())

Daemon Mode (Keep Feeding)

You can also run Xqute in daemon mode, where jobs can be added continuously after starting:

import asyncio
from xqute import Xqute

async def main():
    xqute = Xqute(forks=3)

    # Add initial job
    await xqute.feed(['echo', 'Job 1'])

    # Start in keep_feeding mode (returns immediately)
    await xqute.run_until_complete(keep_feeding=True)

    # Continue adding jobs dynamically
    for i in range(2, 11):
        await xqute.feed(['sleep', '1'])
        await asyncio.sleep(0.1)  # Jobs can be added over time

    # Signal completion and wait for all jobs to finish
    await xqute.stop_feeding()

if __name__ == '__main__':
    asyncio.run(main())

Tip: Use xqute.is_feeding() to check if you need to call stop_feeding().

xqute

API Documentation

Full API documentation is available at: https://pwwang.github.io/xqute/

Usage

Xqute Object

An Xqute object is initialized as follows:

xqute = Xqute(...)

Available arguments are:

  • scheduler: The scheduler class or name (default: local)
  • plugins: Plugins to enable/disable for this session
  • workdir: Directory for job metadata (default: ./.xqute/)
  • forks: Number of jobs allowed to run concurrently
  • error_strategy: Strategy for handling errors (e.g., halt, retry)
  • num_retries: Maximum number of retries when error_strategy is set to retry
  • submission_batch: Number of jobs to submit in a batch
  • scheduler_opts: Additional keyword arguments for the scheduler
  • jobname_prefix: Prefix for job names
  • recheck_interval: Interval (in seconds) to recheck job status

Note: The producer must be initialized within an event loop.

To add a job to the queue:

await xqute.feed(['echo', 'Hello, World!'])

To run until all jobs complete:

# Traditional mode - wait for all jobs to complete
await xqute.run_until_complete()

# Or daemon mode - add jobs continuously
await xqute.run_until_complete(keep_feeding=True)
# ... add more jobs ...
await xqute.stop_feeding()  # Signal completion and wait

Using SGE Scheduler

xqute = Xqute(
    scheduler='sge',
    forks=100,
    scheduler_opts={
        'qsub': '/path/to/qsub',
        'qdel': '/path/to/qdel',
        'qstat': '/path/to/qstat',
        'q': '1-day',  # or qsub_q='1-day'
    }
)

Keyword arguments starting with sge_ are interpreted as qsub options. For example:

'l': ['h_vmem=2G', 'gpu=1']

will be expanded in the job script as:

#$ -l h_vmem=2G
#$ -l gpu=1

Using Slurm Scheduler

xqute = Xqute(
    scheduler='slurm',
    forks=100,
    scheduler_opts={
        'sbatch': '/path/to/sbatch',
        'scancel': '/path/to/scancel',
        'squeue': '/path/to/squeue',
        'partition': '1-day',
        'time': '01:00:00',
    }
)

Using SSH Scheduler

xqute = Xqute(
    scheduler='ssh',
    forks=100,
    scheduler_opts={
        'ssh': '/path/to/ssh',
        'servers': {
            'server1': {
                'user': 'username',
                'port': 22,
                'keyfile': '/path/to/keyfile',
                'ctrl_persist': 600,
                'ctrl_dir': '/tmp',
            }
        }
    }
)

Note: SSH servers must share the same filesystem and use keyfile authentication.

Using Google Batch Jobs Scheduler

xqute = Xqute(
    scheduler='gbatch',
    forks=100,
    scheduler_opts={
        'project': 'your-gcp-project-id',
        'location': 'us-central1',
        'gcloud': '/path/to/gcloud',
        'taskGroups': [ ... ],
    }
)

Using Container Scheduler

xqute = Xqute(
    scheduler='container',
    forks=100,
    scheduler_opts={
        'image': 'docker://bash:latest',
        'entrypoint': '/usr/local/bin/bash',
        'bin': 'docker',
        'volumes': '/host/path:/container/path',
        'envs': {'MY_ENV_VAR': 'value'},
        'remove': True,
        'bin_args': ['--hostname', 'xqute-container'],
    }
)

Plugins

To create a plugin for xqute, implement the following hooks:

  • def on_init(scheduler): Called after the scheduler is initialized
  • def on_shutdown(scheduler, sig): Called when the scheduler shuts down
  • async def on_job_init(scheduler, job): Called when a job is initialized
  • async def on_job_queued(scheduler, job): Called when a job is queued
  • async def on_job_submitted(scheduler, job): Called when a job is submitted
  • async def on_job_started(scheduler, job): Called when a job starts running
  • async def on_job_polling(scheduler, job, counter): Called during job status polling
  • async def on_job_killing(scheduler, job): Called when a job is being killed
  • async def on_job_killed(scheduler, job): Called when a job is killed
  • async def on_job_failed(scheduler, job): Called when a job fails
  • async def on_job_succeeded(scheduler, job): Called when a job succeeds
  • def on_jobcmd_init(scheduler, job) -> str: Called during job command initialization
  • def on_jobcmd_prep(scheduler, job) -> str: Called before the job command runs
  • def on_jobcmd_end(scheduler, job) -> str: Called after the job command completes

To implement a hook, use the simplug plugin manager:

from xqute import simplug as pm

@pm.impl
def on_init(scheduler):
    ...

Implementing a Scheduler

To create a custom scheduler, subclass the Scheduler abstract class and implement the following methods:

from xqute import Scheduler

class MyScheduler(Scheduler):
    name = 'mysched'

    async def submit_job(self, job):
        """Submit a job and return its unique ID."""

    async def kill_job(self, job):
        """Kill a job."""

    async def job_is_running(self, job):
        """Check if a job is running."""

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

xqute-2.0.1.tar.gz (36.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

xqute-2.0.1-py3-none-any.whl (43.4 kB view details)

Uploaded Python 3

File details

Details for the file xqute-2.0.1.tar.gz.

File metadata

  • Download URL: xqute-2.0.1.tar.gz
  • Upload date:
  • Size: 36.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.2.1 CPython/3.12.3 Linux/6.11.0-1018-azure

File hashes

Hashes for xqute-2.0.1.tar.gz
Algorithm Hash digest
SHA256 8f8cdcc79068b3f503d1644ad0af2403a139083a9b32972648c1c020041efd64
MD5 ce41fed9372c8f59ba897707ab4dbfbf
BLAKE2b-256 56f6479ebea4f0d7f54d1b7a60fbecd2c9e532af493e4ebbf30833077f590d04

See more details on using hashes here.

File details

Details for the file xqute-2.0.1-py3-none-any.whl.

File metadata

  • Download URL: xqute-2.0.1-py3-none-any.whl
  • Upload date:
  • Size: 43.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.2.1 CPython/3.12.3 Linux/6.11.0-1018-azure

File hashes

Hashes for xqute-2.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 3b855579aa531a40fd5a04182b976a7c71b8cbb8044fbf8923b29034180f1d5b
MD5 a607a153060ad31bb5df678702654bc1
BLAKE2b-256 a6f58d221a3ae4839e0458b89b574039aa8ffa7718e86921cb11164c63db3e6a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page