The control-panel UI

A polished Streamlit app that wraps the whole pipeline — theory, one-click training, live logs, metric charts, evaluation, and chat — so you can drive everything without memorizing commands.

pip install -e ".[ui]"
streamlit run ui/app.py

What's inside

• Home — the pipeline diagram, live job status, and GPU status at a glance.
• Data — launch the prepare_* scripts and watch the files appear under /ephemeral/data.
• A page per stage (Pretrain, SFT, Reward, DPO, PPO, GRPO). Each page gives you:
  1. the theory + hand-drawn diagram for that stage (pulled straight from these docs),
  2. a config form that writes the stage's configs/*.json,
  3. a Launch button that runs the real training script as a background job, and
  4. a live log tail + metric charts (loss / reward / KL / accuracy) read from the JSONL.
• Evaluate — run GSM8K accuracy on any checkpoint and inspect sample generations.
• Chat — talk to any checkpoint in-process, with temperature / top-p / top-k controls.

How launching works

The app builds the same command you'd type (torchrun … scripts/train_sft.py --config …), runs it as a detached background process, and streams its log file into the page — so jobs keep running even if you navigate away or refresh. A Stop button cleanly terminates the job (and all torchrun workers).

One GPU job at a time

Two multi-GPU torchrun jobs would fight over the same cards and OOM. The UI's GPU-busy guard disables Launch for a GPU stage while another GPU job is running (e.g. while pretraining occupies both H100s). CPU/smoke runs and the in-process Chat page always stay available.

Smoke mode

Each stage page has a smoke toggle that uses the tiny configs/smoke/*.json (small model, few steps, CPU) so you can validate the whole flow in seconds before committing to a real run.