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name: tacc description: Manages experiment execution on the TACC Vista HPC cluster. Use when the user says "run on TACC", "run the pipeline", "allocate a GPU node", "check SLURM queue", "sync code to TACC", "download results from TACC", "cancel that job", or when any task requires GPU/CPU compute that cannot run locally. Do NOT use for local-only tasks like editing code, running tests, or plotting. allowed-tools: Bash

TACC HPC Cluster (Vista)

This skill targets the Vista supercomputer within TACC (Texas Advanced Computing Center). Queue names, module versions, and filesystem layout are Vista-specific and may differ on other TACC systems.

First-Time Setup

Before any TACC operations, verify the user's environment is ready. Run these checks and guide the user through fixing any failures:

Step 1: Verify SSH access

ssh tacc "echo 'SSH OK'"

If this fails, the user needs to configure tacc as an SSH host in ~/.ssh/config.

Step 2: Install command wrapper

ssh tacc "test -x \$HOME/local/bin/genecad/cmd && echo 'cmd OK' || echo 'MISSING'"

If missing, install it from the local skill scripts:

ssh tacc "mkdir -p ~/local/bin/genecad"
rsync -Pz .claude/skills/tacc/scripts/cmd tacc:~/local/bin/genecad/cmd
ssh tacc "chmod +x ~/local/bin/genecad/cmd"

The cmd script sources .bashrc, sets PYTHONPATH, cds to the repo, and exports RANK/WORLD_SIZE from SLURM variables, then execs the given command. It is the single entry point for running commands on compute nodes.

Step 3: Verify Python environment

ssh tacc "bash -l -c 'which python && python --version'"

Expected: Python 3.11+ from a venv at $WORK/envs/ml-rel/bin/activate, sourced by .bashrc. Do NOT use conda, mamba, or micromamba — they are not installed on Vista.

Step 4: Verify repository exists on TACC

ssh tacc "bash -l -c 'cd \$WORK/repos/genecad && git status --short'"

If the repo doesn't exist, clone it: ssh tacc "bash -l -c 'git clone <REPO_URL> \$WORK/repos/genecad'".

Filesystem Model

• $WORK — Small, persistent filesystem. All code and repositories live here. Treat stored data as read-only unless explicitly instructed otherwise.
• $SCRATCH — Effectively unlimited capacity but subject to automatic garbage collection (data expires if not accessed frequently enough). $SCRATCH/tmp is the default output location for ALL experiment results unless instructed otherwise. Any other directories under $SCRATCH should be treated as read-only data sources, not output destinations.

Login Node vs Compute Node

The tacc SSH alias connects to a login node. Use it freely for:

• File exploration (ls, find, du)
• Environment checks (which python, env)
• Data inspection and analysis (Python + pandas, matplotlib, parsing results, generating tables/plots)
• Running analysis scripts that don't need GPUs or heavy compute
• Git operations (git status, git pull)
• Job management (squeue, scancel, idev)

Compute nodes cost SU credits. Only allocate a compute node when:

• The task requires a GPU (model inference, predictions)
• The task requires significant CPU or memory (large-scale post-processing)

Do NOT allocate a compute node just to run ls, check paths, inspect files, or run quick scripts. Use ssh tacc "bash -l -c 'COMMAND'" for that.

Experiment Workflow

A typical experiment follows this sequence. Each step depends on the prior step completing successfully.

Step 1: Sync code to TACC

bash .claude/skills/tacc/scripts/sync          # rsync (default, fast, no commit needed)
bash .claude/skills/tacc/scripts/sync --git    # git push + pull (requires clean commit)

rsync (default): Directly pushes local files without requiring a git commit. Use for in-progress work. git (--git): Use only when changes are committed and you want the remote to match a specific branch/commit.

Step 2: Check for existing compute nodes

ssh tacc "squeue -u \$USER -o '%.18i %.9P %.30j %.2t %.10M %.6D %.20R'"

Reuse a running node if one exists — each session has a minimum 15-minute charge.

Step 3: Allocate a compute node

Queues:

| Queue | Type | Time Limit | Use Case | |-------|------|------------|----------| | gg | CPU-only | — | CPU-only jobs (post-processing, evaluation) | | gh-dev | GPU dev | 2 hours | Try first for GPU work | | gh | GPU prod | — | Fallback if gh-dev has no nodes |

Do not allocate a CPU-only node (gg) for GPU work. The prediction step requires a GPU.

Single-node (default): Use idev to allocate an interactive node:

idev -p gh-dev -N 1 -n 1 -t 2:00:00

Then find the allocated node:

ssh tacc "squeue -u \$USER -h -t R -o '%N'"

Multi-node: Use srun directly — no idev needed (see Step 4).

Step 4: Run the experiment

Write all output to $SCRATCH/tmp unless instructed otherwise.

Single-node execution

After allocating a node with idev (Step 3), find the node name and run commands via cmd. Since cmd cds to the repo automatically, commands can use repo-relative paths directly:

NODE=$(ssh tacc "squeue -u \$USER -h -t R -o '%N'" | head -1)
ssh tacc "ssh $NODE ~/local/bin/genecad/cmd python scripts/predict.py ..."

For long-running single-node jobs, prefer sbatch over idev to survive SSH disconnects.

Multi-node execution with srun

Use srun to launch the same command across multiple nodes simultaneously. The cmd wrapper sets RANK=$PMIX_RANK and WORLD_SIZE=$SLURM_NNODES, so each node knows its rank and the total node count.

ssh tacc "bash -l -c '\
  srun -p gh-dev -N 8 -n 8 --tasks-per-node 1 -t 2:00:00 \
    --output \$SCRATCH/tmp/logs/<name>.log \
    --error  \$SCRATCH/tmp/logs/<name>.log \
    ~/local/bin/genecad/cmd python scripts/predict.py create_predictions \
      --input ... --output-dir ...'" 2>&1 &

Important notes for multi-node srun:

• Always use --tasks-per-node 1 — each node runs one instance of the command.
• Environment variables set before the srun call propagate to all nodes.
• All nodes write to the same --output/--error log file (interleaved). Use [rank=N] prefixes in log messages to distinguish nodes.
• srun blocks until all nodes finish. Run it in background (&) and monitor via tail on the log file.

Monitoring progress

ssh tacc "tail -20 \$SCRATCH/tmp/logs/<name>.log"

Step 5: Download results

rsync -Pz tacc:/remote/results/path local/results/path

Always use rsync instead of scp.

Step 6: Cancel the job

ssh tacc "scancel <jobid>"

Always cancel jobs when done. Idle jobs consume SU credits.

Environment

• Python env: standard venv at $WORK/envs/ml-rel/bin/activate, sourced automatically by .bashrc
• Do NOT use conda, mamba, or micromamba — they are not installed
• Modules loaded by .bashrc: gcc/13.2.0, cuda/12.4, python3/3.11.8
• .bashrc must be sourced for all remote commands — cmd handles this automatically

Troubleshooting

"No running compute node found"

Cause: No idev session is active, or the job expired. Fix: Allocate a new node per Step 3 of the Experiment Workflow.

Node allocated on wrong queue (e.g., gg for GPU work)

Cause: gg is CPU-only. GPU predictions will fail or silently use CPU (extremely slow). Fix: Cancel the job (scancel <jobid>) and allocate on gh-dev or gh.

File transfer fails with scp

Fix: Use rsync -Pz instead of scp.

Python environment not found / conda not found

Cause: .bashrc was not sourced, or agent tried to use conda. Fix: Ensure all compute node commands go through cmd. Never use conda/mamba/micromamba.

Pre-commit pyrefly hook fails locally

Cause: pyrefly is not installed in the local dev environment. Fix: uv pip install pyrefly

SSH timeout during long-running command

Cause: Command was run in foreground and SSH connection dropped. Fix: Always background long-running commands and redirect to a log file. Check progress by tailing the remote log.

Hallucinated or incorrect numbers in result summaries

Cause: Model generated numbers from memory instead of reading source data. Fix: ALWAYS read raw result files (.stats, .tsv) before quoting any numbers. Never guess or recall numbers from earlier in the conversation.