Run Experiment

Submit an ML experiment to a compute environment — local machine, SLURM HPC (Ibex, UW, etc.), or RunAI/Kubernetes (EPFL).

Generates a reproducible job script in jobs/ that is committed alongside the code, then provides the exact submit command to run.

Pair this skill with research-project-memory when a launched job should be linked to a planned experiment, evidence item, worktree, or project action.

Terminology:

• local: the machine where the agent is running, usually the user's Mac
• Git remote: GitHub/GitLab remote used for code sync, such as origin
• server: SSH/HPC/RunAI execution environment such as quest, ibex-vscode, or epfl-haas

When launching on SLURM or RunAI, call it a server run rather than a remote run unless referring to the Git remote.

Skill Directory Layout

<installed-skill-dir>/
├── SKILL.md
├── environments.yaml        # Environment profiles (extend for new clusters)
└── templates/
    ├── slurm_job.sh         # SLURM template (Ibex, UW, any SLURM cluster)
    ├── runai_job.sh         # RunAI/Kubernetes template (EPFL)
    └── local_run.sh         # Local tmux/nohup template

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Steps to Follow

1. Read the environment registry

Resolve <installed-skill-dir> as the directory containing this SKILL.md, then read <installed-skill-dir>/environments.yaml.

List the available environments to the user with a one-line description each.

2. Ask for experiment details

Ask the user in a single message:

1. Environment: Which compute env? (show available choices from environments.yaml + "other")
2. Script / command: What command to run? (e.g., uv run python train.py --lr 1e-3)
3. Job name: Short identifier (e.g., baseline-cifar10, ablation-no-attn). Default: script basename + date.
4. GPU count: How many GPUs? (default from env profile, 0 for CPU-only)
5. Walltime / time limit: (SLURM only) How long? (default from env profile)
6. Conda env or venv: Name of the conda environment, or .venv path (if applicable)
7. Output directory: Where to save checkpoints/results? (default: outputs/<job-name>/)
8. Anything special?: Extra env vars, array job, specific GPU type, PVC mounts (RunAI), etc.

If --env, --script, --name, or --gpus were passed as arguments, pre-fill those answers.

3. Locate the project root

git rev-parse --show-toplevel 2>/dev/null || pwd

Also capture the short git commit hash:

git rev-parse --short HEAD 2>/dev/null || echo "no-git"

4. Generate the job script

Based on the environment type:

type: slurm (ibex, uw, or any SLURM cluster)

Read the SLURM template from <installed-skill-dir>/templates/slurm_job.sh.

Fill in all {PLACEHOLDER} variables:

Placeholder	Value
{PROJECT}	project directory name
{ENV_NAME}	environment key (e.g., ibex)
{ENV_DISPLAY}	display name from profile
{DATE}	today's date YYYY-MM-DD
{COMMIT}	short git SHA
{JOB_NAME}	user-provided job name
{SCRIPT_NAME}	filename of the generated script
{PARTITION}	from env profile defaults (or user override)
{CPUS}	cpus_per_task from profile (or user override)
{GPUS}	user-provided GPU count
{MEM}	from profile defaults (or user override)
{WALLTIME}	user-provided or profile default
{LOG_DIR}	outputs/logs/<job-name>
{OUTPUT_DIR}	outputs/<job-name>
{PROJECT_ROOT}	absolute project root path
{CONDA_ENV}	user-provided env name
{RUN_COMMAND}	user-provided command
{SCRATCH}	scratch path from env profile

Uncomment the relevant module load lines based on the env profile's common_modules. Uncomment the conda activate or source .venv/activate line based on user's answer. If scratch path is in the env profile, uncomment the TMPDIR block.

Output path: jobs/<job-name>.sh

type: runai (runai profile)

Read the RunAI template from <installed-skill-dir>/templates/runai_job.sh.

Fill in placeholders:

Placeholder	Value
{PROJECT}	project directory name
{DATE}	today's date
{COMMIT}	short git SHA
{JOB_NAME}	user-provided job name
{SCRIPT_NAME}	filename of generated script
{IMAGE}	from env profile default_image (ask user to confirm)
{RUNAI_PROJECT}	from env profile project
{GPUS}	GPU count
{CPUS}	CPU count from profile defaults
{MEM}	memory from profile defaults
{PVC_FLAGS}	generated from pvc_mounts in profile: --pvc claim:path \ per mount
{RUN_COMMAND}	user-provided command

Output path: jobs/<job-name>-runai.sh

type: local

Read the local template from <installed-skill-dir>/templates/local_run.sh.

Fill in placeholders similarly. Uncomment conda/venv activation as appropriate.

Output path: jobs/<job-name>-local.sh

type: other / unknown

If the user specifies an environment not in environments.yaml:

1. Ask: "What scheduler does it use? (slurm / runai / other)"
2. If SLURM-compatible: use the SLURM template with the info the user provides.
3. If truly novel: generate a minimal generic wrapper and explain what to fill in.
4. Suggest: "Want me to add this environment to environments.yaml for future use?"

5. Write the job script and preview

Create the job script directory, log directory, and output directory before previewing or submitting:

mkdir -p <project-root>/jobs
mkdir -p <project-root>/outputs/logs/<job-name>
mkdir -p <output-dir>

Write the filled-in script to jobs/<job-name>.sh (or -runai.sh / -local.sh).

Show the user the full generated script for review.

6. Show the submit command and ask to launch

Print the exact command(s) to submit, tailored to the environment:

SLURM (Ibex / UW / etc.)

# If you're already on the login node:
sbatch jobs/<job-name>.sh

# If submitting from your local machine to a server (requires ssh access):
scp jobs/<job-name>.sh <ssh-alias>:<project-root>/jobs/
ssh <ssh-alias> "cd <project-root> && mkdir -p outputs/logs/<job-name> <output-dir> jobs && sbatch jobs/<job-name>.sh"

# Monitor:
squeue -u $USER
sacct -j <jobid> --format=JobID,State,Elapsed,AllocGRES
tail -f outputs/logs/<job-name>/slurm-<jobid>.out

RunAI

bash jobs/<job-name>-runai.sh

# Monitor:
runai list
runai logs <job-name> -f

Local

# Attached (output in terminal):
bash jobs/<job-name>-local.sh

# Detached in tmux:
tmux new-session -d -s <job-name> "bash jobs/<job-name>-local.sh"
tmux attach -t <job-name>

# Background with nohup:
nohup bash jobs/<job-name>-local.sh &

Ask: "Want me to run the submit command now?"

• If yes and local: run it directly.
• If yes and server: run the scp + ssh sbatch command (requires ssh key auth to be set up).
• If no: remind the user that the script is saved in jobs/ and ready to submit.

7. Offer to add to jobs index (optional)

If a jobs/README.md or jobs/index.md exists, offer to append a one-line entry:

| {DATE} | {JOB_NAME} | {ENV_NAME} | {COMMIT} | {RUN_COMMAND_BRIEF} |

If the repo follows the code evidence layout from init-python-project, also offer to create or update a short run pointer under:

docs/runs/<DATE>-<job-name>.md

This file should contain the command, config, commit, output path, expected metric, and monitor command. It should not contain raw logs.

8. Update project memory when present

If the repo has memory/ or a worktree .agent/worktree-status.md, update only verified run pointers:

• memory/evidence-board.md: add or update the linked EXP-### with job script path, commit, command, output directory, and status planned, submitted, or running only if verified
• docs/runs/: write a small run pointer when the code repo uses that convention
• memory/action-board.md: mark the launch action as doing or create a monitor action
• memory/current-status.md: record the latest known job and what must be checked next
• <worktree>/.agent/worktree-status.md: link the run to the worktree purpose and exit condition

Do not store queue state, job success, or final metric values as durable facts unless they were verified in this session. Use needs-verification for monitor tasks.

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Environment Reference

All environments are defined in environments.yaml. The current known environments:

Key	Type	Cluster	Notes
local	local	—	Current machine, tmux/nohup
ibex	slurm	KAUST Ibex	ilogin.ibex.kaust.edu.sa; gpu/batch/himem partitions
uw	slurm	UW HPC	Placeholder — update environments.yaml with actual details
runai	runai	EPFL RunAI	Kubernetes; update project/image in environments.yaml

Adding a New Environment

Edit <installed-skill-dir>/environments.yaml and add a block:

my-cluster:
  type: slurm                       # or runai / local
  display_name: "My University HPC"
  login_node: "login.cluster.edu"
  ssh_alias: mycluster
  scheduler: slurm
  partitions:
    gpu:
      name: gpu
      flag: "--partition=gpu"
      gpu_flag: "--gres=gpu:{count}"
      max_gpus_per_job: 4
  defaults:
    partition: gpu
    gpus: 1
    cpus_per_task: 4
    mem: "32G"
    walltime: "12:00:00"
    max_walltime: "48:00:00"
  storage:
    home: "/home/{user}"
    scratch: "/scratch/{user}"
  module_system: lmod
  common_modules:
    - "cuda/12.1"
    - "python/3.11"
  notes: "..."

---

Reproducibility Conventions

Every generated job script includes:

• Git commit hash in the header and as an env var (GIT_COMMIT)
• Structured output directory: outputs/<job-name>/ for checkpoints, outputs/logs/<job-name>/ for logs
• Timestamped log files so reruns don't overwrite
• Exit code propagation so job arrays and downstream scripts detect failures

The jobs/ directory should be committed to git (the scripts are small text files). Actual outputs go to outputs/ which is typically .gitignored.

---

Example Invocations

/run-experiment                                              # interactive wizard
/run-experiment --env ibex --script train.py --gpus 2
/run-experiment --env local --script eval.py --name eval-baseline
/run-experiment --env runai --gpus 4 --name big-run
/run-experiment --env ibex --script sweep.py --name sweep --gpus 1

---

Common Patterns

Job Array (SLURM) — hyperparameter sweep

When the user says "I want to sweep over N configs":

1. Ask for the sweep configs or config file (e.g., configs/sweep.yaml with N entries).
2. Add #SBATCH --array=0-{N-1}%{max_concurrent} to the script.
3. Add to the run command: --config configs/sweep.yaml --config-idx $SLURM_ARRAY_TASK_ID
4. Output dir: outputs/<job-name>/$SLURM_ARRAY_TASK_ID/

Multi-GPU (DDP)

When GPUs > 1 and the env is SLURM:

• Add --ntasks-per-node={GPUS} directive
• Wrap command with torchrun --nproc_per_node={GPUS} or srun python -m torch.distributed.launch
• Ask the user which distributed launcher they use

Interactive Session (Debugging)

When the user wants to debug interactively (not submit a batch job):

Ibex:

srun --partition=gpu --gres=gpu:1 --cpus-per-task=4 --mem=32G --time=2:00:00 --pty bash

RunAI:

runai submit <name> --image <image> --gpu 1 --interactive --stdin -- bash
runai bash <name>

Generate this command directly without creating a script file.