AareLC_train/slurm

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:

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