Skip to content

End-to-End CPU Training

Last Updated: 2026-06-22

Train, evaluate, and inspect a full Codex symbolic pipeline on CPU — no GPU or cloud required.

Prerequisites

pip install -e ".[dev]"

1 — Prepare data

# Small sample datasets are included in data/
ls data/
# corpus.jsonl   demos.jsonl   prefs.jsonl

2 — Run the pipeline

python deploy/deploy_codex_pipeline.py \
  --corpus  data/corpus.jsonl \
  --demos   data/demos.jsonl \
  --prefs   data/prefs.jsonl \
  --output-dir runs/cpu_test

This executes three stages in sequence:

Stage Script section Output
Pretraining pretrain() runs/cpu_test/M0/
SFT sft() runs/cpu_test/M1/
RLHF rlhf() runs/cpu_test/M2/

3 — Evaluate

pytest tests/test_deploy_codex_pipeline.py -v

Expected: all assertions pass, reproducible outputs (seed=0).

4 — Inspect checkpoints

ls runs/cpu_test/
# M0/  M1/  M2/  metrics.json

python -c "import json; print(json.load(open('runs/cpu_test/metrics.json')))"

5 — Containerised run

docker compose run --rm trainer \
  python deploy/deploy_codex_pipeline.py \
    --corpus /data/corpus.jsonl \
    --output-dir /runs/exp1

Artifacts are written to the mounted volume at /runs/.