菜单
Use when running workloads on LUMI supercomputer, including GPU job submission, PyTorch with ROCm/AMD MI250X, container workflows, and LUMI-specific Slurm configuration. Triggers: "LUMI", "MI250X", "ROCm", "AMD GPU", "CSC", "LUMI-G"
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when turning recent agent-session evidence into durable skill updates. Triggers: "consolidate lessons", "update skills from sessions", "prevent repeated fixes", "recurring mistakes", "capture workflow learnings"
Use when integrating W&B experiment tracking into ML training pipelines, including logging strategy, run configuration, and online/offline mode management. Triggers: "W&B", "wandb", "weights and biases", "experiment logging", "wandb.log", "wandb.init", "training dashboard"
"Use when extracting Overleaf login cookies from Chrome on macOS for the VSCode Overleaf Workshop extension. Decrypts Chrome's cookie store and outputs the session cookie in the exact format the extension expects. Triggers: "overleaf cookie", "overleaf login", "overleaf workshop cookie", "get overleaf cookie", "vscode overleaf login"
Use when preparing to launch any remote or multi-GPU ML experiment. Triggers include "preflight", "sanity check", "launch validation", "before training", "GPU check", "dataset check", "environment check"
Use when pushing changes in a repository that contains git submodules, especially when the submodule has its own commits that must not be left stranded. Triggers include "push", "git push", "submodule", "commit submodule", "workspace push"
Use when evaluating academic papers or surveying a research topic. Gathers venue, citations, GitHub stats, social buzz, reproducibility, and author signals to produce a scored markdown report. Triggers: "evaluate paper", "paper review", "research survey", "literature review", "is this paper good", "find papers on", "compare papers", "paper impact"
| name | lumi-supercomputer |
| description | Use when running workloads on LUMI supercomputer, including GPU job submission, PyTorch with ROCm/AMD MI250X, container workflows, and LUMI-specific Slurm configuration. Triggers: "LUMI", "MI250X", "ROCm", "AMD GPU", "CSC", "LUMI-G" |
| Fact | Value |
|---|---|
| GPU | AMD Instinct MI250X (2 GCDs each = 8 logical GPUs/node) |
| GPU memory | 64 GB HBM2e per GCD, 512 GB total/node |
| CPU | AMD EPYC 7A53 "Trento", 64 cores (56 usable) |
| CPU memory | 512 GB DDR4 per node |
| Network | HPE Slingshot-11, 200 Gbps/NIC, 4 NICs/node |
| Software stack | ROCm (not CUDA), Singularity containers, module load PyTorch/... |
| ROCm default | 6.3.4 (since Jan 2026 maintenance). PE versions: 25.03, 25.09 |
| SSH | lumi.csc.fi |
| Outbound IP | 193.167.209.128/26 (for firewall allowlists) |
| Quota check | lumi-workspaces (no module needed) |
| Allocation check | lumi-allocations |
| Docs | https://docs.lumi-supercomputer.eu |
| Support | https://lumi-supercomputer.eu/user-support |
These are the critical gotchas when moving from NVIDIA (Snellius, etc.) to LUMI:
ROCm, not CUDA: All GPU code runs through ROCm/HIP. PyTorch works transparently but CUDA-specific extensions (cuDNN calls, custom CUDA kernels) won't compile. Use ROCR_VISIBLE_DEVICES instead of CUDA_VISIBLE_DEVICES.
GCDs, not GPUs: Each MI250X has 2 Graphics Compute Dies. Slurm sees 8 "GPUs" per node, but they're 4 physical modules with 2 GCDs each. In-package bandwidth (same MI250X) is 4x faster than cross-module.
56 cores, not 64: Low-noise mode disables 1 core per L3 region (8 regions) = 8 disabled. Only 56 are schedulable. Use --cpus-per-task=7 per GCD (not 8).
Container-first Python: Never pip install directly on Lustre. Use the provided Singularity containers via module load PyTorch/.... Extend with pip install inside the container shell, then make-squashfs to consolidate.
MIOpen cache on /tmp: MIOpen (AMD's cuDNN equivalent) uses a file-locked database. On Lustre this causes hangs. Always redirect: MIOPEN_USER_DB_PATH=/tmp/${USER}-miopen-cache.
Slingshot RCCL vars: Multi-GPU/multi-node communication requires explicit network config:
export NCCL_SOCKET_IFNAME=hsn0,hsn1,hsn2,hsn3
export NCCL_NET_GDR_LEVEL=3
No email notifications: Slurm on LUMI does not support --mail-type.
Auto-requeue: Enabled by default. Always use --no-requeue and --open-mode=append to avoid duplicated output.
Account is mandatory: Every job needs --account=project_<id>. Check allocation with lumi-allocations.
Jan 2026 maintenance broke old software: The system default ROCm is now 6.3.4 and PE versions are 25.03/25.09. Software installed before Jan 2026 may not run — recompilation or rebuilding with 25.03+ is often required. Older PE versions are unsupported.
Reload CrayEnv in job scripts: Login node environment is copied into jobs (with Rome/zen2 CPU targets). Reload CrayEnv in your sbatch script to pick up correct zen3 targets for LUMI-G compute nodes:
module load CrayEnv # resets CPU/network/accelerator targets for current node
zen3 CPU optimization: LUMI-G CPUs are AMD EPYC Trento (zen3 architecture). Compilers can optimize specifically for zen3 — the CrayEnv module auto-loads the correct target.
| Scenario | Partition | Walltime |
|---|---|---|
| Short single-GPU sanity, smoke test, latent extraction probe | dev-g | ≤3 hrs |
| Resumable training chunk (checkpoint every hour) | dev-g | ≤3 hrs |
| Longer production run, multi-GPU training | small-g | Up to 3 days |
Never silently fall back from dev-g to small-g. If dev-g is unavailable, report it and wait or requeue explicitly.
Before any small-g production job:
dev-g probe with reduced steps/eval--dependency=afterok:<dev-g-job-id>Concurrency limit: At most two dev-g probes active at once. When preparing more than two production jobs, chain probes with Slurm dependencies so the wave respects the limit.
For raw dataset download, unpacking, or non-GPU preprocessing:
debug or small)Reserve GPU jobs for GPU-bound preprocessing such as VAE latent extraction.
Before any remote check, sync, preflight, squeue, or sbatch:
ssh -G "$LUMI_SSH" resolves to a jump-routed targetproxyjump or proxycommandlumi.csc.fi resolves to multiple login nodes (uan01–uan04) via DNS round-robin. A later SSH to the same alias may land on a different node, so tmux ls/ps are unreliable for monitoring long login-node processes; use durable shared-scratch artifacts as the source of truth.scp/rsync for routine code syncAll LUMI launch scripts must follow the shared-project convention:
WANDB_EXPERIMENT_NAME<experiment-name>rerun-* or dev-* unless W&B uses them too#!/bin/bash
#SBATCH --account=project_<id>
#SBATCH --partition=small-g
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --gpus-per-node=8
#SBATCH --cpus-per-task=56
#SBATCH --mem=480G
#SBATCH --time=3-00:00:00
#SBATCH --no-requeue
#SBATCH --open-mode=append
#SBATCH --output=%j.log
#SBATCH --error=%j.log
module load LUMI PyTorch/2.7.0-rocm-6.2.4-python-3.12-singularity-20250527
export NCCL_SOCKET_IFNAME=hsn0,hsn1,hsn2,hsn3
export NCCL_NET_GDR_LEVEL=3
export MIOPEN_USER_DB_PATH=/tmp/${USER}-miopen-cache
export MIOPEN_CUSTOM_CACHE_DIR=$MIOPEN_USER_DB_PATH
export ROCR_VISIBLE_DEVICES=$SLURM_LOCALID
srun singularity exec $SIFPYTORCH conda-python-simple -u train.py
See references/pytorch-gpu-jobs.md for full multi-node templates with CPU binding masks and torch.distributed.run.
| Partition | Type | Max Walltime | Max Nodes | Billing |
|---|---|---|---|---|
standard-g | Full-node GPU | 2 days | 1,024 | nodes * 4 * hours GPU-h |
small-g | Shared GPU | 3 days | 4 | max(ceil(cores/8), ceil(mem/64GB), GCDs) * hours * 0.5 |
dev-g | Debug GPU | 30 min--2 hrs | 8--32 | Same as small-g |
standard | Full-node CPU | 2 days | 512 | nodes * 128 * hours core-h |
small | Shared CPU | 3 days | 4 | max(cores, ceil(mem/2GB)) * hours |
debug | Debug CPU | 30 min | 4 | Same as small |
| Area | Path | Default Quota | Purpose |
|---|---|---|---|
| Home | /users/<username> | 20 GB / 100k files | Config, scripts |
| Project | /project/<project> | 50 GB (expandable to 500 GB) | Shared code, small data |
| Scratch | /scratch/<project> | 50 TB (expandable to 500 TB) | Training data, checkpoints |
| Flash | /flash/<project> | 2 TB (expandable to 100 TB) | Hot data (3x billing) |
| Object | LUMI-O (S3) | 150 TB | Cold storage (0.25x billing) |
Warning: No backups on any storage. /tmp on compute nodes is memory-backed (no local disk). Never install Python packages directly on Lustre — use containers + SquashFS.
| Anti-Pattern | Fix |
|---|---|
Using CUDA_VISIBLE_DEVICES | Use ROCR_VISIBLE_DEVICES=$SLURM_LOCALID |
--cpus-per-task=8 per GCD | Use 7 (only 56 cores available, not 64) |
pip install on Lustre | Use container shell + make-squashfs |
| Missing MIOpen redirect | Set MIOPEN_USER_DB_PATH=/tmp/... |
| Missing NCCL vars | Set NCCL_SOCKET_IFNAME and NCCL_NET_GDR_LEVEL |
Expecting conda activate to work | Use module load PyTorch/... + Singularity |
No --no-requeue | Jobs auto-requeue on preemption, duplicating output |
| Using pre-Jan-2026 software | Recompile with PE 25.03+, ROCm 6.3.4 |
| Wrong CPU targets in job | Add module load CrayEnv in sbatch script |
| Using zsh | Cray PE init scripts are bash-only. Use bash on LUMI. |
Submitting small-g without dev-g probe | Always probe first, then chain with --dependency=afterok: |
| Using GPUs for raw dataset download/unpacking | Use CPU partitions (debug/small) for data prep |
| Direct SSH login without jump-node routing | Verify ssh -G shows proxyjump or proxycommand |
scp/rsync for routine code sync | Use GitHub: commit, push, then pull on LUMI |
Monitoring login-node tasks with tmux ls from a fresh SSH | lumi.csc.fi load-balances across uan01–uan04; use shared-scratch log/CSV/state files as the source of truth |
| Slurm job name differing from W&B experiment name | Use the same source variable for both |
More than two concurrent dev-g probes | Chain probes with Slurm dependencies |
Including --mail-type in sbatch | LUMI does not support Slurm email notifications |
tmux: missing or unsuitable terminal | export TERM=xterm-256color before tmux. LUMI lacks terminfo for modern terminals (Ghostty, Kitty, etc.). Fix permanently via SetEnv TERM=xterm-256color in local ~/.ssh/config. |
If a LUMI-specific question isn't covered above or in the reference files, scrape the official documentation:
WebFetch url="https://docs.lumi-supercomputer.eu/" prompt="<your question>"
Key documentation sections:
https://docs.lumi-supercomputer.eu/hardware/https://docs.lumi-supercomputer.eu/runjobs/https://docs.lumi-supercomputer.eu/software/https://docs.lumi-supercomputer.eu/software/packages/pytorch/https://docs.lumi-supercomputer.eu/storage/https://lumi-supercomputer.github.io/update-202601/https://lumi-supercomputer.eu/user-supportAfter finding useful new information, consider updating this skill's reference files so future sessions benefit.
slurm-gpu-training — General Slurm patterns (job submission, monitoring, preflight)gpu-training-acceleration — ROCm-compatible acceleration techniques