py-cluster-api 0.3.0

Generic Python library for running jobs on HPC clusters

py-cluster-api

A Python library for submitting and monitoring jobs on HPC clusters. Supports running arbitrary executables (Nextflow pipelines, Python scripts, Java tools, etc.) on clusters and taking action when jobs complete via async callbacks.

Executors

• Local Subprocess
• IBM Platform LSF
• We will accept PRs that implement and test additional executors (SLURM, etc.)

Features

• Async-first — built on asyncio for non-blocking job submission and monitoring
• Local executor — run jobs as local subprocesses for development and testing, including array jobs
• Job monitoring — polls the scheduler and fires callbacks on job completion, failure, or cancellation
• Job arrays — submit array jobs with per-element log files
• Zombie detection — jobs that disappear from the scheduler are marked as failed
• YAML config with profiles — Nextflow-style config with per-environment profiles
• Callback chaining — register on_success, on_failure, or on_exit handlers on any job

Installation

Requires Python 3.10+.

pip install py-cluster-api

Or with Pixi:

pixi add --pypi py-cluster-api

Quick Start

Single Job

import asyncio
from cluster_api import create_executor, ResourceSpec, JobMonitor

async def main():
    executor = create_executor(profile="janelia_lsf")
    monitor = JobMonitor(executor)
    await monitor.start()

    job = await executor.submit(
        command="nextflow run nf-core/rnaseq --input samples.csv",
        name="rnaseq-run",
        resources=ResourceSpec(cpus=4, gpus=1, memory="32 GB", walltime="24:00", queue="long"),
        env={"NXF_WORK": "/scratch/work"},
    )
    job.on_success(lambda j: print(f"Done! Job {j.job_id}, peak mem: {j.max_mem}"))
    job.on_failure(lambda j: print(f"FAILED! Job {j.job_id}, exit={j.exit_code}"))

    await monitor.wait_for(job)
    await monitor.stop()

asyncio.run(main())

Job Array

async def run_array():
    executor = create_executor(profile="janelia_lsf")
    monitor = JobMonitor(executor)
    await monitor.start()

    job = await executor.submit_array(
        command="python process.py --index $LSB_JOBINDEX",
        name="batch-process",
        array_range=(1, 50),
        resources=ResourceSpec(cpus=1, memory="4 GB", walltime="01:00"),
    )
    job.on_exit(lambda j: print(f"Array finished: {j.job_id}"))

    await monitor.wait_for(job)
    await monitor.stop()

The array index environment variable depends on the executor: LSF uses $LSB_JOBINDEX, while the local executor uses $ARRAY_INDEX.

Reconnecting After Restart

If your process crashes or restarts, reconnect() rediscovers running jobs from the scheduler and resumes tracking them. Requires job_name_prefix to be set in config.

async def resume():
    executor = create_executor(profile="janelia_lsf")
    monitor = JobMonitor(executor)
    await monitor.start()

    recovered = await executor.reconnect()
    for job in recovered:
        print(f"Reconnected to {job.job_id} ({job.name}), status={job.status}")
        job.on_exit(lambda j: print(f"Job {j.job_id} finished: {j.status}"))

    if recovered:
        await monitor.wait_for(*recovered)
    await monitor.stop()

Local Testing

async def local_test():
    executor = create_executor(executor="local")
    monitor = JobMonitor(executor, poll_interval=1.0)
    await monitor.start()

    job = await executor.submit(command="echo hello world", name="test")
    job.on_success(lambda j: print("It worked!"))

    await monitor.wait_for(job, timeout=10.0)
    await monitor.stop()

Configuration

Configuration is loaded from YAML with optional profiles. The search order is:

1. Explicit config_path argument
2. $CLUSTER_API_CONFIG environment variable
3. ./cluster_api.yaml
4. ~/.config/cluster_api/config.yaml

Example cluster_api.yaml

executor: local
poll_interval: 10
job_name_prefix: "capi"

profiles:
  janelia_lsf:
    executor: lsf
    queue: normal
    gpus: 1
    memory: "8 GB"
    walltime: "04:00"
    script_prologue:
      - "module load java/11"

  local_dev:
    executor: local
    poll_interval: 2

Config Options

Option	Default	Description
executor	"local"	Backend: lsf or local
cpus	None	Default CPU count
gpus	None	Default GPU count
memory	None	Default memory (e.g. "8 GB")
walltime	None	Default wall time (e.g. "04:00")
queue	None	Default queue/partition
poll_interval	10.0	Seconds between status polls
job_name_prefix	None	Optional prefix prepended to job names. When set, polling filters by {prefix}-* and reconnect() is available; when unset, the user controls the full job name and polling queries all jobs
shebang	"#!/bin/bash"	Script shebang line
script_prologue	[]	Lines inserted before the command
script_epilogue	[]	Lines inserted after the command
extra_directives	[]	Additional scheduler directive lines appended verbatim to the script header (e.g. "#BSUB -P myproject")
directives_skip	[]	Substrings to filter out of directives
extra_args	[]	Extra CLI args appended to the submit command (e.g. bsub)
lsf_units	"MB"	LSF memory units (KB, MB, GB)
suppress_job_email	true	Set LSB_JOB_REPORT_MAIL=N
command_timeout	100.0	Timeout in seconds for scheduler commands
zombie_timeout_minutes	30.0	Mark jobs as failed if unseen for this long
completed_retention_minutes	10.0	Keep finished jobs in memory for this long

API Reference

See docs/API.md for the full API reference and error handling guide.

Development

See docs/Development.md for build instructions, testing, and release process.