nanoslurm 0.1.2

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nanoslurm

nanoslurm is a zero-dependency Python wrapper for SLURM job submission and monitoring.
It uses a tiny POSIX-compatible shell script to call sbatch and related commands, avoiding any heavy Python dependencies.

Features

• Submit jobs from Python without pyslurm or other packages
• Monitor status (PENDING, RUNNING, COMPLETED, etc.)
• Cancel jobs
• Tail job logs
• Get detailed info via scontrol
• Respects working directory at runtime (sbatch -D)

Requirements

• SLURM cluster with sbatch, squeue, and optionally sacct / scontrol
• Python ≥ 3.11
• Linux operating system

Quickstart

import nanoslurm

job = nanoslurm.submit(
    command=["python", "train.py", "--epochs", "10"],
    name="my_job",
    cluster="gpu22",
    time="01:00:00",
    cpus=4,
    memory=16,
    gpus=1,
    stdout_file="./slurm_logs/%j.txt",
    stderr_file="./slurm_logs/%j.err",
    signal="SIGUSR1@90",
    workdir="."
)

print(job)                      # Job(id=123456, name='my_job_2025-08-08_09-12-33.123', ...)
print(job.status)               # "PENDING", "RUNNING", ...
print(job.is_running())         # True / False
print(job.is_finished())        # True / False
print(job.info())               # Detailed dict from scontrol
job.tail(10)                    # Last 10 lines of stdout
job.wait(poll_interval=5)       # Wait until completion
job.cancel()                    # Cancel job

Command line interface

Install the CLI with uv:

uv tool install nanoslurm

Submit a job from the terminal:

nslurm run -c gpu22 -t 01:00:00 -p 4 -m 16 -g 1 -- python train.py --epochs 10

Launch an interactive prompt to build a command and adjust options:

nslurm run -i

Manage persistent defaults (stored as YAML via platformdirs):

nslurm defaults show            # list current defaults
nslurm defaults set cluster gpu22
nslurm defaults reset
nslurm defaults edit            # open the YAML config in $EDITOR

Most job parameters (such as cluster, time, or resource counts) have no built-in defaults. Set them explicitly on the command line or persist them via nslurm defaults set.

Releasing

Bump the version in pyproject.toml and merge the change into main. A workflow will tag the commit as vX.Y.Z and publish the package to PyPI.