| # Train/distill one paper-locked recipe. Site resources are supplied to sbatch. | |
| #SBATCH --job-name=dlb-train | |
| #SBATCH --output=results/logs/slurm-train-%A.out | |
| #SBATCH --error=results/logs/slurm-train-%A.err | |
| set -euo pipefail | |
| ROOT="${DLB_ROOT:-${SLURM_SUBMIT_DIR}}" | |
| RECIPE="${DLB_RECIPE:?set DLB_RECIPE (for example flm or mdlm_sdtt)}" | |
| DATASET="${DLB_DATASET:?set DLB_DATASET (lm1b or owt)}" | |
| MODEL="${DLB_MODEL:-}" | |
| registry_model="$RECIPE" | |
| if [[ "$RECIPE" == "di4c" ]]; then | |
| registry_model="${MODEL:?DLB_MODEL is required for di4c}" | |
| fi | |
| environment="$( | |
| PYTHONPATH="$ROOT/src${PYTHONPATH:+:$PYTHONPATH}" "${DLB_PYTHON:-python}" - "$ROOT" "$registry_model" <<'PY' | |
| from pathlib import Path | |
| import sys | |
| from dlb.registry import load_registry | |
| root, model = Path(sys.argv[1]), sys.argv[2] | |
| registry = load_registry(root / "configs/experiments.yaml") | |
| print(registry.models[model].environment) | |
| PY | |
| )" | |
| mkdir -p "$ROOT/results/logs" | |
| args=(--root "$ROOT" --recipe "$RECIPE" --dataset "$DATASET") | |
| [[ -n "$MODEL" ]] && args+=(--model "$MODEL") | |
| exec "${DLB_CONDA:-conda}" run -n "$environment" python -m dlb.recipes "${args[@]}" | |