# training/

GPU-side code for the M1 CEFR classifier. Runs on the local box (WSL2, RTX 3070)
with the `train` dependency group and **never** enters the runtime Docker image
(ADR 0001). The record policies it applies — label mapping, chunking, document-level
split, aggregation, metrics — live in `src/tutor/ml/cefr/` so inference shares them
byte-for-byte (no train/serve skew) and CI tests them without torch.

## One-time setup

```bash
uv add --group train torch transformers accelerate datasets mlflow
uv run --group train python -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0))"
```

## Runs (ADR 0003 experiment arms)

```bash
# Validate a config without GPU (split summary + leakage check):
uv run --group train python training/train_cefr.py --config training/configs/en_only.toml --dry-run

# Arms 1-3:
uv run --group train python training/train_cefr.py --config training/configs/en_only.toml
uv run --group train python training/train_cefr.py --config training/configs/multilingual.toml
uv run --group train python training/train_cefr.py --config training/configs/en_truncated.toml

# Published baseline on the same test docs:
uv run --group train python training/eval_cefr.py \
  --model UniversalCEFR/xlm-roberta-base-cefr-all-classifier \
  --config training/configs/en_only.toml
```

Arm 5 (generalization audits) = copies of `en_only.toml` with
`exclude_corpora_from_train = ["cambridge_exams_en"]` (and the elg twin).

## Tracking

```bash
uv run --group train mlflow ui --backend-store-uri sqlite:///mlflow.db
```

Headline metrics: `test_en_document_*` (macro-F1, adjacent accuracy, QWK).

## Export & deploy (M1)

```bash
uv add --group train onnx   # quantization dependency (export uses torch.onnx directly)

# Export + int8 + equivalence check + CPU bench (threads=2 ~ Space cpu-basic)
uv run --group train python training/export_onnx.py \
  --run-dir models/cefr/en_chunked_weighted --threads 2

# Parity: the deployed int8 service re-scored on the canonical test docs
uv run --group train python training/eval_service.py \
  --artifact models/cefr/en_chunked_weighted/onnx-int8 \
  --config training/configs/en_only.toml

# Publish the artifact (model card with cc-by-nc-sa-4.0 included)
uv run --group train python training/export_onnx.py \
  --run-dir models/cefr/en_chunked_weighted \
  --push <hf-username>/polyglot-tutor-cefr-onnx
```

The Space loads the artifact via `CEFR_MODEL_ID` (or `CEFR_MODEL_PATH` locally);
the runtime depends only on onnxruntime + tokenizers — torch never ships.