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Train (UI & CLI)

You can run every stage two ways: from the command line (full control, best for long jobs) or from the Streamlit control panel (forms, one-click launch, live logs, charts — see The UI).

Install

pip install -e ".[train]"     # editable install — no more PYTHONPATH=.
export HF_HOME=/ephemeral/hf_cache

The pipeline, end to end (CLI)

Each command reads its stage JSON from configs/ (override anything with --field, or point at another file with --config). Use python for one GPU and torchrun for many.

python scripts/prepare_pretrain_data.py --split val   --out /ephemeral/data/pile_dev.h5
python scripts/prepare_pretrain_data.py --split train --num_shards 1 --out /ephemeral/data/pile_train.h5
python scripts/prepare_sft_data.py
python scripts/prepare_preference_data.py --source both
python scripts/prepare_rl_prompts.py

# single GPU
python scripts/pretrain_base.py --config configs/pretrain.json
# both GPUs (effective batch = batch_size * grad_accum * num_gpus)
torchrun --standalone --nproc_per_node=2 scripts/pretrain_base.py --config configs/pretrain.json

torchrun --standalone --nproc_per_node=2 scripts/train_sft.py
torchrun --standalone --nproc_per_node=2 scripts/train_reward.py
torchrun --standalone --nproc_per_node=2 scripts/train_dpo.py --loss_type dpo
torchrun --standalone --nproc_per_node=2 scripts/train_ppo.py --reward_source verifier
torchrun --standalone --nproc_per_node=2 scripts/train_grpo.py

The whole alignment chain in one shot:

bash scripts/run_posttraining.sh        # SFT → RM → DPO → PPO → GRPO → eval table

Multi-GPU notes

  • torchrun --standalone --nproc_per_node=N launches N data-parallel ranks (DDP + bf16). Only rank 0 logs and checkpoints.
  • On the dev box (2× H100, no NVLink) the educational attention materializes a (B, n_head, T, T) tensor per block, so memory scales with sequence length — at context 1024 use --batch_size 8 --grad_accum 12 and recover the effective batch via accumulation. Set PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True.

Where outputs go

  • Checkpoints → /ephemeral/ckpts/<stage>.pt (each carries its own resolved cfg).
  • Metrics → /ephemeral/logs/<stage>_<timestamp>.jsonl (one JSON per logged step). The UI plots these live; you can also --use_wandb true to mirror to Weights & Biases.

Then evaluate on GSM8K and chat with any checkpoint.