xqute 2.1.1

A job management system for python

xqute

An async-first job management and scheduling framework for Python.

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xqute schedules, submits, monitors, and manages batch jobs across local, HPC, cloud, and container backends — all through a single async Python API. It's built for bioinformatics pipelines, ML hyperparameter sweeps, batch data processing, and any workload that needs to fan out across heterogeneous compute.

✨ Features

• Blazingly fast — built on asyncio with uvloop; thousands of jobs, minimal overhead
• Six scheduler backends — local, SGE, Slurm, SSH, Google Cloud Batch, Docker/Podman/Apptainer
• Plugin system — 14 lifecycle hooks let you add logging, notifications, or custom logic without touching core code
• Error strategies — automatic retry with configurable limits, or halt-the-world on first failure
• File-based status tracking — jobs self-report via status files; survives network failures and scheduler quirks
• Daemon mode — keep_feeding lets you add jobs dynamically at any point
• Cloud storage — workdirs on GCS (gs://), Azure (az://), or S3 (s3://)
• Path translation — seamless SpecPath / MountedPath duality for cross-machine execution
• Timeouts — per-job timeout enforcement via coreutils timeout

📦 Installation

pip install xqute

With optional extras:

pip install 'xqute[gs]'      # Google Cloud Storage support
pip install 'xqute[cloudsh]'  # Cloud shell support

🚀 Quick start

Default (local scheduler)

import asyncio
from xqute import Xqute

async def main():
    xqute = Xqute(forks=3)
    for _ in range(10):
        await xqute.feed(["sleep", "1"])
    await xqute.run_until_complete()

asyncio.run(main())

Daemon mode — add jobs while running

xqute = Xqute(forks=3)

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

# Feed jobs dynamically
for i in range(100):
    await xqute.feed(["python", "train.py", str(i)])
    await asyncio.sleep(0.1)

# Signal done and wait for everything to finish
await xqute.stop_feeding()

🎯 Scheduler backends

xqute ships with six schedulers. Swap the scheduler argument to switch.

Slurm

xqute = Xqute(
    scheduler="slurm",
    forks=100,
    scheduler_opts={
        "partition": "gpu",
        "time": "24:00:00",
        "mem": "8G",
        "gres": "gpu:1",
    },
)

SGE (Sun Grid Engine)

xqute = Xqute(
    scheduler="sge",
    forks=100,
    scheduler_opts={
        "q": "1-day",
        "l": ["h_vmem=4G", "gpu=1"],
    },
)

SSH (multi-server)

xqute = Xqute(
    scheduler="ssh",
    forks=100,
    scheduler_opts={
        "servers": {
            "node1": {"user": "alice", "host": "node1.example.com", "keyfile": "/home/alice/.ssh/id_rsa"},
            "node2": {"user": "alice", "host": "node2.example.com", "keyfile": "/home/alice/.ssh/id_rsa"},
        }
    },
)

Note: SSH servers must share the same filesystem and use key-based auth.

Google Cloud Batch

xqute = Xqute(
    scheduler="gbatch",
    forks=100,
    scheduler_opts={
        "project": "my-gcp-project",
        "location": "us-central1",
        "taskGroups": [{
            "taskSpec": {
                "runnables": [{
                    "container": {"imageUri": "ubuntu", "entrypoint": "bash", "commands": ["-c", "..."]}
                }]
            },
            "taskCount": 500,
            "parallelism": 100,
        }],
    },
)

Container (Docker / Podman / Apptainer)

xqute = Xqute(
    scheduler="container",
    forks=10,
    scheduler_opts={
        "image": "docker://python:3.12",
        "entrypoint": "/bin/bash",
        "bin": "docker",
        "volumes": ["/data:/data"],
        "envs": {"TF_CPP_MIN_LOG_LEVEL": "2"},
    },
)

🔌 Plugins

14 lifecycle hooks via simplug. Example — send Slack notifications on failures:

from xqute import simplug as pm

@pm.impl
async def on_job_failed(scheduler, job):
    import requests
    requests.post(WEBHOOK, json={"text": f"Job {job.index} failed"})

See the Plugins page for the full list of hooks and more examples.

📖 Documentation

Full documentation is at pwwang.github.io/xqute:

• Quick Start — get running in minutes
• User Guide — initialization, error handling, monitoring
• Schedulers — all six backends with config reference
• Plugins — lifecycle hooks and plugin authoring
• Advanced — custom schedulers, Dask/Airflow integration, perf tuning
• API Reference — auto-generated from source

🛠️ Custom scheduler

Implement three async methods to add your own backend:

from xqute import Scheduler

class MyScheduler(Scheduler):
    name = "mycluster"

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

    async def kill_job(self, job):
        """Kill the job given its JID."""

    async def job_is_running(self, job):
        """Return True if the job is still running."""

Then pass it directly: Xqute(scheduler=MyScheduler, ...).

📊 Architecture

Jobs are wrapped in a bash template with an EXIT trap that writes status files (job.status, job.rc, job.stdout, job.stderr) into a per-job metadir. The polling loop reads these files — no scheduler API calls for status. This design makes xqute resilient to network hiccups and scheduler oddities.

INIT → QUEUED → SUBMITTED → RUNNING → FINISHED
                              ↓           ↓
                          KILLING →   FAILED

🤝 Contributing

Issues and PRs welcome on GitHub. See AGENTS.md for dev setup and conventions.

📝 License

MIT — see LICENSE.