SLURM — Training on the Artemis Cluster

The University of Sussex Artemis HPC cluster provides GPU nodes for large-scale training. Access requires VPN.

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Single job

Submit one training run to SLURM:

cellclass sbatch --args ~/data/mitosis-rpe/rois.npz \
    --model shufflenet2x1_0 --lr 1e-3 --lr-scheduler plateau \
    --flip 3 --batch-size 128 -d cuda --wandb

cellclass sbatch wraps sbatch and passes everything after --args to cellclass train. Use cellclass sbatch --help to see SLURM-specific options (partition, memory, walltime), or read them in the generated cellclass sbatch reference.

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Batch of SLURM jobs

Use the same train.txt batch file as for local training, but pass the SLURM submitter as the command:

cellclass batch train.txt --script batch.sh --cmd "cellclass sbatch --args"
bash batch.sh

This generates one sbatch submission per line in train.txt. Each job runs independently on the cluster; results are logged to W&B as they complete.

Important: Comment out completed lines in train.txt before regenerating batch.sh to avoid resubmitting finished runs.

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Weights & Biases on Artemis

Your W&B credentials stored in ~/.netrc are available on Artemis if your home directory is mounted (it is by default). Verify before submitting jobs:

wandb status

If credentials are missing, run wandb login from an Artemis login node.

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Monitoring jobs

squeue -u $USER          # List your queued/running jobs
scancel <job_id>         # Cancel a job

Training progress is also visible in real time on your W&B dashboard at https://wandb.ai.