SLURM Scripts for ML

Estimated time: 20 minutes

Overview

Questions

• How do you write SLURM scripts for GPU training jobs?
• How do you request GPU resources correctly?
• How do you launch multi-GPU training with SLURM?

Objectives

• Write SLURM batch scripts for GPU training
• Request appropriate GPU, CPU, and memory resources
• Launch distributed training with torchrun and SLURM

Single-GPU SLURM Job

---

BASH

#!/bin/bash
#SBATCH --job-name=pytorch_training
#SBATCH --partition=gpu
#SBATCH --gres=gpu:1
#SBATCH --cpus-per-task=4
#SBATCH --mem=16GB
#SBATCH --time=02:00:00
#SBATCH --output=training_%j.log

module load miniconda3/py313_26.3.2-2   # then activate your conda env -- no pytorch module exists

python3 << 'EOF'
import torch
print(f"GPU available: {torch.cuda.is_available()}")
print(f"GPU device: {torch.cuda.get_device_name(0)}")

# Your training code here
EOF

Submit and monitor:

BASH

sbatch train_job.sh
squeue -u $USER -l

Submitting the training job and confirming with squeue -l that it’s RUNNING on a GPU node.

TensorFlow SLURM Job

---

BASH

#!/bin/bash
#SBATCH --job-name=tf_training
#SBATCH --partition=gpu
#SBATCH --gres=gpu:1
#SBATCH --cpus-per-task=4
#SBATCH --mem=32GB
#SBATCH --time=04:00:00
#SBATCH --output=tf_training_%j.log

module load miniconda3/py313_26.3.2-2   # then activate your conda env -- no tensorflow module exists

python3 << 'EOF'
import tensorflow as tf
gpus = tf.config.list_physical_devices('GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)

# Your TensorFlow training code here
EOF

Multi-GPU SLURM Job

---

BASH

#!/bin/bash
#SBATCH --job-name=multi_gpu_training
#SBATCH --partition=gpu
#SBATCH --gres=gpu:4
#SBATCH --cpus-per-task=16
#SBATCH --mem=64GB
#SBATCH --time=04:00:00
#SBATCH --output=training_%j.log

module load miniconda3/py313_26.3.2-2   # then activate your conda env -- no pytorch module exists

# Set master node details for distributed training
export MASTER_ADDR=$(scontrol show hostname ${SLURM_NODELIST} | head -n1)
export MASTER_PORT=29500
export WORLD_SIZE=$SLURM_GPUS_PER_NODE

# Launch with torchrun (automatic launcher)
torchrun --nproc_per_node=4 train_distributed.py

Interactive GPU Sessions

---

Command-Line Interactive Session

BASH

# Request interactive GPU session
srun -p gpu --gres=gpu:1 --pty bash

# Now you're on a GPU node
python3 test_pytorch.py

OnDemand Web Interface

Access via https://ondemand.hpc.pomona.edu:

1. Log in with Pomona credentials
2. Click “Jupyter Lab” or “Terminal”
3. Select GPU and duration
4. Upload your code

Resource Guidelines

---

Workload	GPUs	CPUs	Memory	Time
Quick test	1	4	16 GB	30 min
Single-GPU training	1	4	16-32 GB	2-8 hours
Multi-GPU training	4	16	64 GB	4-24 hours
Hyperparameter sweep	1 per job	4	16 GB	2 hours each

Challenge

Challenge

Challenge: Write a Training Job Script

Write a SLURM script that requests 1 GPU, loads your conda environment, copies data from /bigdata to /scratch, runs training, and saves the model back to /bigdata.

Show me the solution

BASH

#!/bin/bash
#SBATCH --job-name=my_training
#SBATCH --partition=gpu
#SBATCH --gres=gpu:1
#SBATCH --cpus-per-task=4
#SBATCH --mem=16GB
#SBATCH --time=04:00:00
#SBATCH --output=training_%j.log

module load miniconda3
conda activate pytorch_env

# Copy data to fast storage
cp -r /bigdata/lab/<labname>/dataset /scratch/$USER/dataset

# Run training
python3 train.py --data /scratch/$USER/dataset --output /scratch/$USER/model.pt

# Save results back to persistent storage
cp /scratch/$USER/model.pt /bigdata/lab/<labname>/models/

Callout

Important: Always Test First

Before submitting a large GPU job: 1. Run on CPU with small dataset (10 iterations) 2. Check GPU memory usage with an interactive session 3. Verify checkpointing works 4. Then scale up to full training

Key Points

• Use --partition=gpu and --gres=gpu:N to request GPU resources
• Request 4 CPUs per GPU as a rule of thumb
• Use torchrun for launching distributed PyTorch training
• Test interactively with srun before submitting batch jobs
• Copy data to /scratch for faster I/O during training