# Training on the GPU cluster (template, not yet filled in)

`train.sbatch` runs one ledger run on the `mx` partition. It needs these facts,
which replace the `__PLACEHOLDERS__` in the script:

| Placeholder | What | Value |
| --- | --- | --- |
| `__PARTITION__` | SLURM partition | `mx` |
| `__ACCOUNT__` | SLURM account to charge | |
| `__WALLTIME__` | wall-time limit for 400 epochs of yolo26n at 640 px | |
| `__SHARED_FS__` | shared filesystem visible from login and compute nodes, holds the repo checkout | |
| `__MODULES__` / `__VENV__` / `__CONTAINER__` | how Python, CUDA and ultralytics are provided on compute nodes (one of the three) | |
| `__DEPLOY_KEY__` | SSH key on the cluster allowed to push to this repo, if the job reports back itself | |

Still to confirm before the first job: the cluster and login node names; whether
compute nodes reach gitea.psi.ch (git + LFS) and PyPI, or whether a container has to
carry everything; the GPU type on the partition; who submits (a service account or
a person). Until then the reference runs happen on the DGX Spark with the same
`tools/train_run.py`.

Workflow once filled in:

```bash
python tools/ledger.py new --dataset dataset/<name> --tag <tag> --base yolo26n-seg.pt
git commit -am "run <id>: exported" && git push
sbatch slurm/train.sbatch <id>
python tools/ledger.py show
```