slurm-emission 0.2.0

Submit jobs to SLURM seamlessly

📤 Slurm Emission

One shared SBATCH wrapper for every parameter combination, straight from Python.

Python 3.9+  ·  Slurm  ·  License: MIT

Slurm Emission is a small utility for Slurm clusters. It writes a single driver script with your directives and environment setup, expands experiment grids into CLI flags, and submits each run—ideal when many jobs differ only by arguments.

Features

• Builds a single .sh driver with #SBATCH options and optional preamble (modules, conda activate, cd, etc.).
• Expands experiment definitions into a Cartesian product of parameter lists and calls sbatch for each combination.
• Optional deduplication, shuffling, subsetting, and “dry run” style workflows via mock_send.

Requirements

• Python 3.9 or newer
• A Slurm cluster with sbatch available on your PATH
• A worker script that accepts the generated arguments (e.g. argparse flags like --seed=0)

Installation

pip install slurm-emission

Usage

Import run_experiments and pass:

1. A list of experiment dicts whose values are lists (or scalars); keys become argument names.
2. sbatch_args: mapping of Slurm option names (without #SBATCH) to values, e.g. job-name, partition, gres.
3. bash_prelines: shell commands run before the per-job command (modules, environment, working directory).
4. init_command: prefix for each job’s command (typically python your_script.py).

Generated batch scripts are written under ~/.cache/slurm-emission/shs/ by default (configurable with sh_location).

Example

from slurm_emission import run_experiments

script_path = "path/to/your/script"
script_name = "script.py"

sbatch_args = {
    "job-name": "example_1",
    "partition": "gpu",
    "gres": "gpu:1",
    "cpus-per-task": 4,
    "mem": "40G",
    "account": "your-account",
    "time": "23:00:00",
}

datasets = ["cifar", "mnist"]
models = ["transformer", "lstm"]

experiments = [
    {
        "seed": list(range(4)),
        "epochs": [300],
        "model": models,
        "dataset": datasets,
    }
]

load_modules = "module load conda"
activate_env = "conda activate llms"
py_location = f"cd {script_path}"
bash_prelines = f"{load_modules}\n{activate_env}\n{py_location}"

run_experiments(
    experiments,
    init_command=f"python {script_name}",
    sbatch_args=sbatch_args,
    bash_prelines=bash_prelines,
    id="llms",
)

Ensure script.py parses the emitted flags (by default --key=value style for argparse).

The generated driver looks like this (paths and timestamps will differ on your machine):

#!/bin/bash
#SBATCH --job-name=example_1
#SBATCH --partition=gpu
#SBATCH --gres=gpu:1
#SBATCH --cpus-per-task=4
#SBATCH --mem=40G
#SBATCH --account=your-account
#SBATCH --time=23:00:00

module load conda
conda activate llms
cd path/to/your/script
$1

Slurm is invoked once per combination, passing the full command as the script’s first argument, for example:

Number jobs: 16/16
1/16 sbatch ~/.cache/slurm-emission/shs/llms--2024-06-07--11-49-47--OukHy.sh 'python script.py --seed=0 --epochs=300 --model=transformer --dataset=cifar '
...
16/16 sbatch ~/.cache/slurm-emission/shs/llms--2024-06-07--11-49-47--OukHy.sh 'python script.py --seed=3 --epochs=300 --model=transformer --dataset=cifar '
Number jobs: 16/16

Boolean flags (##true## / ##false##)

When clean_store_true_false=True (the default), emitted commands are post-processed so you can sweep “on/off” flags that map to argparse action="store_true" / store_false-style CLIs without emitting awkward =True / =False pairs:

• =##true## → the =##true## fragment is removed and replaced with a space, so --use_amp=##true## becomes --use_amp (flag present, no value).
• --flag=##false## → the whole --flag=##false## token is stripped so the flag is omitted from the command.

Use letters, digits, and underscores only in the parameter name (the implementation matches --\w+=##false##).

experiments = [
    {
        "epochs": [3],
        "use_amp": ["##true##", "##false##"],
        "log_every": [100],
    }
]

run_experiments(
    experiments,
    init_command="python train.py",
    sbatch_args=sbatch_args,
    bash_prelines=bash_prelines,
    id="bool-sweep",
)

Roughly equivalent printed commands (after cleanup and whitespace normalization):

… 'python train.py --epochs=3 --use_amp --log_every=100 '
… 'python train.py --epochs=3 --log_every=100 '

Set clean_store_true_false=False if you want the raw --use_amp=##true## strings left unchanged.

See run_experiments in slurm_emission.submit_jobs for additional options (mock_send, subset, prevent, remove_duplicates, etc.).

License

This project is released under the MIT License.

Links

• Repository
• Issue tracker