slurmgrid 0.1.0

Manage large Slurm job arrays that exceed cluster submission limits

slurmgrid

Manage large Slurm job arrays that exceed your cluster's submission limit.

If you need to run 50,000 small jobs but your cluster caps MaxArraySize at 10,000 (or limits total queued jobs), slurmgrid handles the tedious cycle of "submit a batch, wait, submit the next batch" automatically. It chunks your parameter manifest, submits array jobs via sbatch, monitors completion via sacct, retries failures, and persists state so you can resume if interrupted.

Installation

pip install slurmgrid

Or just clone the repo and run directly (no dependencies beyond Python 3.8+):

git clone https://github.com/jgaeb/slurmgrid.git
cd slurmgrid
python -m slurmgrid --help

Quick start

1. Create a manifest file (CSV or TSV) with one row per job:

alpha,beta,seed
0.1,1,42
0.1,2,42
0.5,1,42
0.5,2,42
...

2. Run slurmgrid submit with your command template:

python -m slurmgrid submit \
  --manifest params.csv \
  --command "python train.py --alpha {alpha} --beta {beta} --seed {seed}" \
  --partition gpu \
  --time 01:00:00 \
  --mem 4G \
  --max-concurrent 5000

That's it. slurmgrid will:

• Shuffle and split the manifest into chunks (default: 1/3 of MaxArraySize)
• Submit each chunk as a fast array job via sbatch, using Slurm's %throttle to limit concurrency to --max-concurrent
• Poll sacct every 30 seconds to track completion
• Submit the next chunk when the current one finishes
• Batch failed jobs into retry chunks (up to --max-retries, default 3)
• Save state to disk after every poll so you can resume if interrupted

Usage

Submit a new run

python -m slurmgrid submit \
  --manifest params.csv \
  --command "python train.py --alpha {alpha} --beta {beta}" \
  --state-dir ./my_run \
  --partition gpu \
  --time 02:00:00 \
  --mem 8G \
  --cpus-per-task 4 \
  --max-concurrent 5000 \
  --max-retries 3 \
  --poll-interval 30 \
  --preamble "module load python/3.10 && conda activate myenv"

The --command template uses {column_name} placeholders that are resolved from the manifest columns. Any column in the manifest can be referenced.

Resume an interrupted run

If you lose your SSH session or Ctrl-C out, running Slurm jobs continue independently. Resume monitoring with:

python -m slurmgrid resume --state-dir ./my_run

Check status

python -m slurmgrid status --state-dir ./my_run

==================================================
  Total jobs:            50000
  Completed:             35420  (70.8%)
  Active:                 4580
  Pending:               10000
  Failed (retrying):         0
  Failed (final):            0
  Chunks: 35/50 completed, 5 active, 10 pending
==================================================

Cancel all jobs

python -m slurmgrid cancel --state-dir ./my_run

Dry run

Generate all chunk files and sbatch scripts without actually submitting:

python -m slurmgrid submit --manifest params.csv --command "echo {x}" --dry-run

Inspect the generated scripts in ./sc_state/scripts/ to verify correctness.

How it works

1. Chunking: The manifest is split into sub-manifests. Each chunk gets its own sbatch script that uses SLURM_ARRAY_TASK_ID to index into the sub-manifest and extract the parameters for that task.

2. Shuffling: Manifest rows are shuffled before chunking (disable with --no-shuffle) so each chunk gets a representative mix of the parameter space and chunks take roughly the same wall time.

3. Batch submission: Each chunk is submitted as a single sbatch --array call with a %throttle suffix to limit concurrency, which is orders of magnitude faster than submitting jobs individually.

4. Monitoring: The tool polls sacct to track job status. One chunk runs at a time; when it finishes, the next is submitted.

5. Retries: When all regular chunks are done, failed tasks are batched into a single retry chunk and resubmitted, up to --max-retries per task.

6. State persistence: All state is saved as JSON after every poll. Atomic writes (via temp file + rename) prevent corruption. You can resume at any time.

State directory layout

sc_state/
  config.json          # Frozen copy of the submission configuration
  state.json           # Chunk-level status and failure tracking
  slurmgrid.log         # Tool's own log file
  chunks/
    chunk_000.chunk    # Sub-manifests (internal format)
    chunk_001.chunk
  scripts/
    chunk_000.sh       # Generated sbatch scripts
    chunk_001.sh
  logs/
    chunk_000/         # Slurm stdout/stderr per chunk
      slurm-12345_0.out
      slurm-12345_0.err

All options

Flag	Default	Description
--manifest	(required)	CSV/TSV manifest file
--command	(required)	Command template with {column} placeholders
--state-dir	./sc_state	Directory for state, chunks, scripts, logs
--delimiter	auto-detect	Manifest delimiter (, for .csv, \t for .tsv)
--chunk-size	auto-detect	Jobs per array chunk (default: MaxArraySize / 3)
--max-concurrent	10000	Max simultaneously running tasks (Slurm %throttle)
--max-retries	3	Max retries per failed job
--poll-interval	30	Seconds between status checks
--max-runtime	unlimited	Max seconds to run before saving state and exiting
--dry-run	false	Generate scripts without submitting
--no-shuffle	false	Don't shuffle manifest rows before chunking
--partition		Slurm partition
--time		Wall time limit (e.g., 01:00:00)
--mem		Memory per node (e.g., 4G)
--mem-per-cpu		Memory per CPU
--cpus-per-task	1	CPUs per task
--gpus		GPU specification
--gres		Generic resource specification
--account		Slurm account
--qos		Quality of service
--constraint		Node constraint
--exclude		Nodes to exclude
--job-name-prefix	sc	Prefix for Slurm job names
--preamble		Shell commands before the main command
--preamble-file		File containing preamble commands
--extra-sbatch		Extra #SBATCH flags (repeatable)

Requirements

• Python 3.8+ (stdlib only, no external dependencies)
• Slurm with sbatch, sacct, squeue, scancel, scontrol available
• Slurm accounting enabled (sacct must work)

License

MIT