launch
Pre-flight checklist for long-running ML training jobs covering config diff, run naming, path verification, monitoring setup, and restart-cleanup. Use when the user asks to launch, kick off, start, restart, or kill a training run, or mentions launching a multi-hour or multi-day GPU job (python train, accelerate launch, torchrun, deepspeed, sbatch, tmux training).
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/fcakyon/phd-skills --skill launch- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads launch automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Launch: pre-flight checklist for long ML training jobs
Long training jobs are expensive to fail. A 12-hour run that crashes on epoch 3 from a missing dataset path or a default workers=8 against an NFS mount is a full day lost. This skill walks five quick checks before you commit the GPUs.
The agentic Stop hook in this plugin will route here from reason when an assistant tries to launch a run without going through the checklist.
When to run
The user just asked to:
- launch / kick off / start / fire up a training run
- restart a run that died
- kill a current run (also runs the cleanup half of the checklist)
- review a launch command before submitting
Or the user is about to run any of: python train.py, accelerate launch, torchrun, deepspeed, sbatch train.sh, tmux new-session ... python ... train, wandb sweep.
The checklist
1. Config diff against a reference run
The most expensive failure is launching with the wrong knobs. Before starting:
find configs/ recipes/ experiments/ -maxdepth 3 \( -name '*.yaml' -o -name '*.yml' -o -name '*.json' -o -name '*.toml' \) -mtime -30 2> /dev/null | head
Pick the most-recently-modified config that resembles the intended run (same model family, same task). Diff against the intended config:
diff -u configs/baseline_v1.yaml configs/intended.yaml
Walk every diff line. For each, ask: is this difference intentional and motivated, or is it a stale default I forgot to set? Common silent regressors:
num_workers/ dataloader workers (default in many repos is 8: wrong on NFS)batch_size(per-device vs global mismatch under DDP)learning_rate(linearly scaled with batch size; if batch changed, lr should too)optimizerbetas / weight decay (paper-default vs framework-default)mixed_precision(fp16vsbf16matters for some models)gradient_accumulation_stepsseed(still set if you care about reproducibility)
If no reference exists in this project, ask the user to point at one. Do not launch with framework defaults alone.
2. Run name discipline
The run name will live in wandb / neptune / checkpoint dirs / status reports for the rest of its life. It must describe the experiment in plain English without internal codes:
- bad:
run-1,wave-2,cs-ad,phase2-internal - good:
7src-fastvit-s-featmap-mlp-dinov3,coco-baseline-bs256-lr3e-4,swin-t-imagenet-distill-from-vit-l
The pattern: <dataset/task>-<model>-<key-config>-<distinctive-recipe-piece>. If you can't describe the experiment from the name in one sentence, the name is wrong. The Stop hook flags any run reference that uses session-local labels.
3. Path verification
Before launching, every path the run depends on must be confirmed to exist:
# Dataset path
ls -la /path/to/dataset | head
# Pretrained checkpoint (if loading)
ls -la /path/to/checkpoint.pt
# Output directory parent (must exist; the run dir will be created)
ls -la /path/to/runs/
# Config file
cat configs/intended.yaml | head
Never trust a path that was recalled from memory. The destructive_path_guard.sh hook will already block obvious cases for rm/mv, but the launch path needs the same scrutiny, a run started with a nonexistent dataset path crashes 30 minutes in instead of immediately.
4. Monitoring setup
Auto-detect the experiment tracker:
WANDB_API_KEYset orwandbimport in the launcher → wandbNEPTUNE_API_TOKENset → neptuneMLFLOW_TRACKING_URIset ormlflowin launcher → mlflow- presence of
runs/orlightning_logs/→ tensorboard - none of the above → ask the user; "no monitoring" is rarely the right answer for a multi-hour run
Confirm the run will appear under the right project / entity / experiment-name. Confirm any tags / groups for cohort comparison are set.
5. ETA in your local timezone
Estimate wall-clock duration: epochs × seconds-per-epoch / 3600 = hours. State the ETA in your local TZ (the system's TZ, which the timezone_scrub.sh hook validates against). If the run will straddle a meeting / sleep / OOO window, decide whether to defer or split.
Restart and kill cleanup
If this is a restart of a previously-failed run, or a kill before launching a replacement, purge stale artifacts in this exact order:
- Local checkpoint dir on the launching machine:
rm -rf /local/runs/<run-name>(verify path first; thedestructive_path_guard.shwill warn). - Remote artifact dir on the cluster / NFS / object store:
rm -rf /remote/runs/<run-name>(or equivalent). - Experiment tracker run: delete via the tracker's API (
wandb api.run(...).delete(), neptunerun.stop() + delete via UI, etc.). Stale tracker runs corrupt later comparisons. - Scheduler reservation: cancel the SLURM job (
scancel <jobid>), the lambda labs reservation, the cron entry, etc. Runs that "killed but the GPUs are still allocated" are a recurring waste.
Skipping any of these creates ghost state that will confuse the next launch or the next comparison.
Output
When the user invokes this skill, walk the five checks (or three checks + cleanup, if killing) and report which passed and which failed. Block the launch on any failure unless the user explicitly waives the check.
For a clean launch, end with the launch command itself in a fenced block, ready to copy.
FAQ
What does the launch skill do?
Pre-flight checklist for long-running ML training jobs covering config diff, run naming, path verification, monitoring setup, and restart-cleanup. Use when the user asks to launch, kick off, start, restart, or kill a training run, or mentions launching a multi-hour or multi-day GPU job (python train, accelerate launch, torchrun, deepspeed, sbatch, tmux training).
How do I install launch?
Run npx skills add https://github.com/fcakyon/phd-skills --skill launch in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/launch/ (personal) or .claude/skills/launch/ (project).
Which AI agents can use launch?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is launch safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains launch?
It is published by fcakyon in fcakyon/phd-skills. It has 63 recorded installs.
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