Slurm GPU Training

When to Use

• Submitting training jobs to a Slurm cluster
• Setting up conda/venv environments for non-interactive Slurm shells
• Debugging failed Slurm jobs (OOM, timeout, module issues)
• Planning walltime and resource requests for GPU training

Workflow

• Preflight: Run preflight script to verify data, weights, env vars
• Config: Set Hydra config overrides, verify with --cfg job
• Submit: sbatch with correct account, partition, walltime
• Observe: Monitor first 5 min for crashes, NaN loss, wrong config
• Monitor: squeue, tail -f, W&B dashboard
• Post-mortem: sacct, check W&B summary, save best checkpoint

Core Principles

Offline-First

HPC nodes often lack internet access. Default to offline mode for all package managers and model hubs:

export HF_DATASETS_OFFLINE=1
export TRANSFORMERS_OFFLINE=1
export HF_HUB_OFFLINE=1

Pre-cache everything (models, datasets, tokenizers) on the login node before submitting jobs. W&B can run online if the cluster allows outbound HTTPS — but always have an offline fallback.

Preflight Before Submit

Run a preflight check script before sbatch to verify:

• All dataset shards/files exist in cache
• Model weights are downloaded
• Environment variables are set (API keys, paths)
• GPU is detectable (for interactive debug sessions)

# scripts/preflight_training_offline.py
def check_dataset_cache(data_dir):
    if not Path(data_dir).exists():
        raise FileNotFoundError(f"Dataset not cached: {data_dir}")
    shard_count = len(list(Path(data_dir).glob("*.tar")))
    if shard_count == 0:
        raise FileNotFoundError(f"No shards in {data_dir}")
    print(f"OK: {shard_count} shards in {data_dir}")

Conda Init for Non-Interactive Shells

Slurm jobs run in non-interactive shells where conda activate doesn't work by default. Always source conda's init script first:

source "$(conda info --base)/etc/profile.d/conda.sh"
conda activate myenv
export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:${LD_LIBRARY_PATH:-}"

The LD_LIBRARY_PATH export is critical — without it, CUDA libraries from conda may not be found.

Patterns

Sbatch Template

#!/bin/bash
#SBATCH --job-name=train-experiment
#SBATCH --account=$ACCOUNT
#SBATCH --partition=gpu
#SBATCH --time=5-00:00:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=18
#SBATCH --gpus=1
#SBATCH --output=%j.log
#SBATCH --error=%j.log

set -euo pipefail

# Environment
source "$(conda info --base)/etc/profile.d/conda.sh"
conda activate myenv
export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:${LD_LIBRARY_PATH:-}"

# Offline defaults
export HF_DATASETS_OFFLINE=1
export TRANSFORMERS_OFFLINE=1

# Secrets from .env
set -a; source .env; set +a

# Run
python train.py mode=train training=fullrun

Log Naming

Use Slurm job ID only — no date stamps, no experiment names in the filename:

#SBATCH --output=%j.log    # Good: 12345678.log
#SBATCH --output=train_%j_%x.log  # Avoid: redundant, hard to parse

Run Naming with Job ID

Append Slurm job ID to W&B run names for traceability:

run_name = f"{experiment_name}_{os.environ.get('SLURM_JOB_ID', 'local')}"

Walltime Planning

GPU	Typical Training	Suggested Walltime
A100/H100	100k iters, bs=16	5 days (5-00:00:00)
A100/H100	1200 iters (fastrun)	1 hour (01:00:00)
Any	Smoke test / dryrun	30 min (00:30:00)

Log Git Commit at Startup

Always log the git commit hash at training start — without it, comparing two runs is guesswork:

# In sbatch script, before training:
echo "Git commit: $(git rev-parse HEAD)"
echo "Git dirty: $(git status --porcelain | head -5)"

Or in Python/W&B:

import subprocess
commit = subprocess.check_output(["git", "rev-parse", "HEAD"]).decode().strip()
wandb_run.config.update({"git_commit": commit})

When comparing jobs, cross-reference sacct -j JOBID --format=Submit with git log to determine which code each job ran.

Monitoring

squeue -u "$USER"                                    # Job status
sacct -j <jobid> --format=JobID,State,Elapsed,MaxRSS # Post-mortem
tail -f <jobid>.log                                  # Live output

Account Lookup

If you don't know the Slurm account name (needed for sbatch --account=), query it from past job history:

sacct --format=Account%30 -n | sort -u

This pulls the account field from all your historical jobs. Faster and more reliable than grepping log files.

Background Monitor Pattern

For long runs, launch a detached monitor that tails output:

nohup bash -c "while ! [ -f outputs/${SLURM_JOB_ID}.log ]; do sleep 5; done; tail -f outputs/${SLURM_JOB_ID}.log" > outputs/${SLURM_JOB_ID}_monitor.log 2>&1 &

Tiered Run Strategy

Structure experiments into run tiers with different purposes:

Tier	Purpose	Duration	Key Settings
dryrun	Syntax/config smoke test	5-10 min	Minimal iters, no eval
fastrun	Feature debugging	30-60 min	Short, frequent eval/callbacks
fullrun	Real training	Hours-days	Full iters, periodic eval
fullrun_noeval	Pure training speed	Hours-days	Full iters, no eval overhead

The fastrun is the key debugging tool: when testing a specific feature (evaluation, checkpointing, recognition callbacks), increase its frequency so it triggers within minutes. For example, to debug evaluation, set eval_every_steps: 30. The fastrun exists to catch issues cheaply before committing GPU hours to a fullrun.

Observe After Submit

After submitting any training job, always monitor for at least 5 minutes to confirm:

• No crashes or import errors
• Loss is reasonable (not NaN, not static)
• Correct config was picked up (batch size, learning rate, data source)
• Throughput matches expectations (s/step)

Don't submit and context-switch. The most common failure mode is a config mistake that burns GPU hours silently.

Job Name Override

When reusing an sbatch script for a different purpose, always override the job name to reflect actual usage:

# Testing Lhotse loader with a fastrun script
sbatch --job-name=lhotse-fastrun --account=$ACCOUNT slurm_scripts/train_fastrun.sbatch

# Not: sbatch slurm_scripts/train_fastrun.sbatch  (misleading job name)

This keeps squeue and sacct output meaningful when you have multiple variants running.

Anti-Patterns

• Hardcoding hyperparameters in sbatch scripts: Sbatch sets environment and calls python train.py with config overrides. Hyperparameters live in config files.
• Running GPU-heavy work on login nodes: Always use srun --pty bash for interactive GPU work, or submit via sbatch.
• Skipping LD_LIBRARY_PATH: Conda environments need this for CUDA/cuDNN to resolve correctly inside Slurm jobs.
• Date-stamped log files: Use %j.log (job ID only). Date stamps create clutter and the job ID is already unique and traceable via sacct.
• Assuming internet access: Never pip install or huggingface-cli download inside a Slurm job. Cache everything beforehand.
• Ignoring exit codes: Always use set -euo pipefail in sbatch scripts. Silent failures waste GPU hours.

See Also

• hydra-experiment-config — Config structure for experiments
• wandb-experiment-tracking — Run naming with job ID, monitoring dashboards
• gpu-training-acceleration — CUDA flags and acceleration settings in sbatch
• fail-fast-ml-engineering — Preflight validation before job submission