slurm

Stars11

Forks1

UpdatedMarch 14, 2026 at 19:39

Help write, debug, and manage SLURM jobs. Use when the user asks about sbatch, salloc, squeue, job scripts, or cluster resource allocation.

Installation

Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

Source

michaelrizvi

michaelrizvi/claude-config

View GitHub Repository View Creator Repositories

Download

Related occupationsSOC

Based on SOC occupation classification

Network and Computer Systems AdministratorsComputer and Mathematical Occupations·SOC 15-1244

SKILL.md

readonly

name	slurm
description	Help write, debug, and manage SLURM jobs. Use when the user asks about sbatch, salloc, squeue, job scripts, or cluster resource allocation.

SLURM Assistant

Help the user write job scripts, debug failed jobs, and manage cluster resources.

Job Script Guidelines

Always include: --job-name, --output, --error, --time, --mem, --gres (for GPUs), --cpus-per-task
Place scripts in a dedicated folder (e.g. scripts/)
Use set -euo pipefail in the bash portion
Log key info at the start: hostname, GPU info (nvidia-smi), date, git commit hash
Activate the correct virtual environment before running Python

Resource Allocation Rules

Small experiments (<1M params): 1 GPU, 4-8 CPUs, 16-32GB RAM
Medium experiments (1M-1B params): 1-2 GPUs, 8-16 CPUs, 32-64GB RAM
Large models (7B+): multiple GPUs, 64-128GB+ RAM
32B+ inference: 4+ GPUs, match tensor parallelism to GPU count
Rule of thumb: ~4-8 CPUs per GPU, ~2x model size in FP16 for VRAM

Known GPU Types & Selection

GPU types (use with `--gres=gpu:<type>:N`)

a100: A100 40GB HBM2e
a100l: A100 80GB HBM2e
a6000: RTX A6000 48GB GDDR6
h100: H100 80GB HBM3
l40s: L40S ~45GB GDDR6
rtx8000: Quadro RTX 8000 48GB GDDR6
v100: V100 32GB HBM2

GPU selection by attribute

You can also request GPUs by memory, architecture, or feature:

By memory: --gres=gpu:48gb:1 (any 48GB GPU: RTX8000, A6000, L40S)
By arch: --gres=gpu:ampere:1 (A100, A6000, L40S)
By interconnect: --gres=gpu:nvlink:1
By system: --gres=gpu:dgx:1
Memory tags: 12gb, 32gb, 40gb, 48gb, 80gb
Arch tags: volta, turing, ampere

Node Inventory

Nodes	Count	GPUs	CPUs	RAM
cn-l[001-091]	91	4x L40S (48GB)	48	1024GB
cn-c[001-040]	40	8x RTX8000 (48GB)	64	384GB
cn-g[001-029]	29	4x A100 (80GB)	64	1024GB
cn-a[001-011]	11	8x RTX8000 (48GB)	40	384GB
cn-b[001-005]	5	8x V100 (32GB)	40	384GB
cn-k[001-004]	4	4x A100 (40GB)	48	512GB
cn-n[001-002]	2	8x H100 (80GB)	192	2048GB
cn-d[001-004] (DGX)	4	8x A100 (40/80GB)	128	1024-2048GB
cn-j001	1	8x A6000 (48GB)	64	1024GB

GPUs per node is either 4 or 8 — don't request more than the node type has.

Partitions & Preemption

Partition	Time Limit	Per-User Limits
`long` (default)	7 days	No per-user GPU cap
`main`	5 days	2 GPUs, 8 CPUs, 48GB
`short`	3 hours	4 GPUs, 1TB mem
`unkillable`	2 days	1 GPU, 6 CPUs, 32GB

Preemption hierarchy: unkillable > main > long. Once preempted, jobs are killed and auto-requeued. main jobs do NOT preempt other main jobs. -grace variants give a SIGTERM grace period before kill. Checkpoint frequently on long partition.

Storage

Path	Quota	Key Policy
`$HOME`	100GB / 1M files	Daily backup, low I/O — don't write logs here
`$SCRATCH`	5TB / unlimited	Files unused >90 days deleted
`$SLURM_TMPDIR`	No quota	Fastest I/O, cleared after job
`/network/projects/<group>/`	1TB / 1M files	Shared project storage
`$ARCHIVE`	5TB	No backup, not on GPU nodes

Always copy data to $SLURM_TMPDIR at job start for performance. Write logs/outputs to $SCRATCH, not $HOME. Check usage with disk-quota.

Module System

module load python/3.10 — required before creating venvs on cluster
module load miniconda/3 — for conda environments
module avail / module spider <term> — search available modules
Pre-built PyTorch/TF modules exist for Mila GPUs
On login/CPU nodes without GPUs: CONDA_OVERRIDE_CUDA=11.8 before conda commands

Debugging Failed Jobs

Check .err files first — experiment logs go to stderr
sacct -j <jobid> --format=JobID,State,ExitCode,MaxRSS,Elapsed,NodeList for completed jobs
Common issues: OOM (check MaxRSS), time limit, bad path, missing module/env
For OOM: check batch size, model size, gradient accumulation, and whether --mem was sufficient
torch.autograd.set_detect_anomaly(True) causes extreme filesystem IOPS — never leave on in batch jobs, admins will flag it

Monitoring

disk-quota — check storage usage
squeue -u $USER — your active jobs
echo $SLURM_JOB_GPUS — which GPU(s) your job got
Netdata per-node: <node>.server.mila.quebec:19999 (requires Mila wifi or SSH tunnel)
Grafana dashboard: dashboard.server.mila.quebec

Limits

Max 1000 jobs per user in the system at any time

Safety

Never submit jobs (sbatch) without explicit user confirmation
Verify paths and configs before submission
Test on small instances first when possible

Scope

$ARGUMENTS

SLURM Assistant

Help the user write job scripts, debug failed jobs, and manage cluster resources.

Job Script Guidelines

Always include: --job-name, --output, --error, --time, --mem, --gres (for GPUs), --cpus-per-task
Place scripts in a dedicated folder (e.g. scripts/)
Use set -euo pipefail in the bash portion
Log key info at the start: hostname, GPU info (nvidia-smi), date, git commit hash
Activate the correct virtual environment before running Python

Resource Allocation Rules

Small experiments (<1M params): 1 GPU, 4-8 CPUs, 16-32GB RAM
Medium experiments (1M-1B params): 1-2 GPUs, 8-16 CPUs, 32-64GB RAM
Large models (7B+): multiple GPUs, 64-128GB+ RAM
32B+ inference: 4+ GPUs, match tensor parallelism to GPU count
Rule of thumb: ~4-8 CPUs per GPU, ~2x model size in FP16 for VRAM

Known GPU Types & Selection

GPU types (use with `--gres=gpu:<type>:N`)

a100: A100 40GB HBM2e
a100l: A100 80GB HBM2e
a6000: RTX A6000 48GB GDDR6
h100: H100 80GB HBM3
l40s: L40S ~45GB GDDR6
rtx8000: Quadro RTX 8000 48GB GDDR6
v100: V100 32GB HBM2

GPU selection by attribute

You can also request GPUs by memory, architecture, or feature:

By memory: --gres=gpu:48gb:1 (any 48GB GPU: RTX8000, A6000, L40S)
By arch: --gres=gpu:ampere:1 (A100, A6000, L40S)
By interconnect: --gres=gpu:nvlink:1
By system: --gres=gpu:dgx:1
Memory tags: 12gb, 32gb, 40gb, 48gb, 80gb
Arch tags: volta, turing, ampere

Node Inventory

Nodes	Count	GPUs	CPUs	RAM
cn-l[001-091]	91	4x L40S (48GB)	48	1024GB
cn-c[001-040]	40	8x RTX8000 (48GB)	64	384GB
cn-g[001-029]	29	4x A100 (80GB)	64	1024GB
cn-a[001-011]	11	8x RTX8000 (48GB)	40	384GB
cn-b[001-005]	5	8x V100 (32GB)	40	384GB
cn-k[001-004]	4	4x A100 (40GB)	48	512GB
cn-n[001-002]	2	8x H100 (80GB)	192	2048GB
cn-d[001-004] (DGX)	4	8x A100 (40/80GB)	128	1024-2048GB
cn-j001	1	8x A6000 (48GB)	64	1024GB

GPUs per node is either 4 or 8 — don't request more than the node type has.

Partitions & Preemption

Partition	Time Limit	Per-User Limits
`long` (default)	7 days	No per-user GPU cap
`main`	5 days	2 GPUs, 8 CPUs, 48GB
`short`	3 hours	4 GPUs, 1TB mem
`unkillable`	2 days	1 GPU, 6 CPUs, 32GB

Storage

Path	Quota	Key Policy
`$HOME`	100GB / 1M files	Daily backup, low I/O — don't write logs here
`$SCRATCH`	5TB / unlimited	Files unused >90 days deleted
`$SLURM_TMPDIR`	No quota	Fastest I/O, cleared after job
`/network/projects/<group>/`	1TB / 1M files	Shared project storage
`$ARCHIVE`	5TB	No backup, not on GPU nodes

Always copy data to $SLURM_TMPDIR at job start for performance. Write logs/outputs to $SCRATCH, not $HOME. Check usage with disk-quota.

Module System

module load python/3.10 — required before creating venvs on cluster
module load miniconda/3 — for conda environments
module avail / module spider <term> — search available modules
Pre-built PyTorch/TF modules exist for Mila GPUs
On login/CPU nodes without GPUs: CONDA_OVERRIDE_CUDA=11.8 before conda commands

Debugging Failed Jobs

Check .err files first — experiment logs go to stderr
sacct -j <jobid> --format=JobID,State,ExitCode,MaxRSS,Elapsed,NodeList for completed jobs
Common issues: OOM (check MaxRSS), time limit, bad path, missing module/env
For OOM: check batch size, model size, gradient accumulation, and whether --mem was sufficient
torch.autograd.set_detect_anomaly(True) causes extreme filesystem IOPS — never leave on in batch jobs, admins will flag it

Monitoring

disk-quota — check storage usage
squeue -u $USER — your active jobs
echo $SLURM_JOB_GPUS — which GPU(s) your job got
Netdata per-node: <node>.server.mila.quebec:19999 (requires Mila wifi or SSH tunnel)
Grafana dashboard: dashboard.server.mila.quebec

Limits

Max 1000 jobs per user in the system at any time

Safety

Never submit jobs (sbatch) without explicit user confirmation
Verify paths and configs before submission
Test on small instances first when possible

Scope

$ARGUMENTS

slurm

SLURM Assistant

Job Script Guidelines

Resource Allocation Rules

Known GPU Types & Selection

GPU types (use with --gres=gpu:<type>:N)

GPU selection by attribute

Node Inventory

Partitions & Preemption

Storage

Module System

Debugging Failed Jobs

Monitoring

Limits

Safety

Scope

More from this repository

More from this repository

SLURM Assistant

Job Script Guidelines

Resource Allocation Rules

Known GPU Types & Selection

GPU types (use with --gres=gpu:<type>:N)

GPU selection by attribute

Node Inventory

Partitions & Preemption

Storage

Module System

Debugging Failed Jobs

Monitoring

Limits

Safety

Scope

GPU types (use with `--gres=gpu:<type>:N`)

GPU types (use with `--gres=gpu:<type>:N`)