setup-libero-training

name

setup-libero-training

description

Use when the user wants to set up and run a minimal end-to-end LIBERO fine-tuning/training in this GalaxeaVLA (G0.5) repo from scratch — builds the venv, wires the checkpoints + LIBERO LeRobot data + stats into the exact layout the configs expect, then runs a single-GPU smoke to prove the data→model→loss→optimizer loop works. Triggers: "配一个最小 libero 训练", "set up libero training", "跑通 libero finetune", "minimal libero smoke".

Set up a minimal LIBERO training

Goal: get bash scripts/run/finetune.sh 1 libero --test to run end-to-end on one GPU. Follow the steps in order; each step has a verification gate — do not proceed past a failing gate. Fail loudly (report the exact error); never paper over a missing file with a fallback.

Create a TodoWrite list mirroring steps 1–5.

Step 1 — Environment (.venv + .env)

# China-network mirrors (harmless elsewhere)
export UV_DEFAULT_INDEX=${UV_DEFAULT_INDEX:-https://mirrors.aliyun.com/pypi/simple/}
export UV_PYTHON_INSTALL_MIRROR=${UV_PYTHON_INSTALL_MIRROR:-https://gh-proxy.com/https://github.com/astral-sh/python-build-standalone/releases/download}
export HF_ENDPOINT=${HF_ENDPOINT:-https://hf-mirror.com}

[ -d .venv ] || uv sync --index-strategy unsafe-best-match   # builds venv; flash-attn-4 is a prebuilt wheel, no nvcc needed
[ -f .env ] || cp .env.example .env                          # then fill PROJECT_ROOT / G05_OUTPUT_DIR / HF caches

• If .env was just created from the template, edit it: set PROJECT_ROOT to this repo's absolute path and G05_OUTPUT_DIR="$PROJECT_ROOT/runs". Append source "$PROJECT_ROOT/.venv/bin/activate" at the end so a single source .env is the one-step entry.
• Gate: source .env && python -c "import torch, g05; print(torch.__version__, torch.cuda.is_available())" must print a +cu128 torch and True.

Step 2 — Checkpoints into the layout the configs expect

The configs hardcode these relative paths (training path → config field):

Path	Config field
checkpoints/g05-base/checkpoints/model_state_dict.pt	configs/model/g05.yaml: pretrained_ckpt
checkpoints/qwen3_5_2b_base_processor/	configs/model/g05.yaml: hf_processor_path
checkpoints/action_tokenizer.pt	configs/tokenizer/actioncodec.yaml: ckpt_dir

Two ways to populate them:

• Public download: huggingface-cli download OpenGalaxea/G05 --repo-type model --local-dir checkpoints (≈44 GB; produces exactly this layout). The public g05-base matches the public source's self-attention action expert — use it, not any visual-memory variant.
• Reuse a prepared copy (clusters with a shared, already-built copy): if GALAXEA_LIBERO_SRC is set to such a directory, symlink instead of downloading:

  S="$GALAXEA_LIBERO_SRC"
  ln -sfn "$S/data" data
  mkdir -p checkpoints/g05-base/checkpoints
  ln -sfn "$S/checkpoints/action_tokenizer.pt"          checkpoints/action_tokenizer.pt
  ln -sfn "$S/checkpoints/qwen3_5_2b_base_processor"    checkpoints/qwen3_5_2b_base_processor
  ln -sfn "$S/checkpoints/g05-base/checkpoints/model_state_dict.pt" checkpoints/g05-base/checkpoints/model_state_dict.pt
  

  This assumes GALAXEA_LIBERO_SRC is already in the canonical layout above. For a source with different internal names, map each file explicitly (see ENV_SETUP_NOTES.md §2.3 if present). Verify a loaded checkpoint actually matches: the first-step log must read Loaded N / N with Missing (rand init) 0. A partial Loaded 838/946 means a wrong checkpoint variant (e.g. a linear-attention mem build vs the public self-attention model) — stop and fix the source, do not train on it.
• Gate: ls checkpoints/g05-base/checkpoints/model_state_dict.pt checkpoints/action_tokenizer.pt checkpoints/qwen3_5_2b_base_processor/tokenizer_config.json — all three exist.

Step 3 — LIBERO training data + stats

configs/data/libero.yaml expects four LeRobot v3.0 @ 512×512 suites and a stats file:

data/stats/libero_stats.json
data/libero/libero_{10,goal,object,spatial}_no_noops_lerobot/

• If you symlinked data/ in Step 2, this is already satisfied — skip to the gate.
• Otherwise: the IPEC-COMMUNITY/libero_*_no_noops_1.0.0_lerobot datasets are LeRobot v2.1 / 256×256 and do not drop in. Re-convert to v3.0 / 512 (e.g. openpi convert_libero_data_to_lerobot.py), place each suite under the exact directory name above, then generate stats (it is computed, not downloaded):

  python tests/test_dataloader_batch.py --mixture configs/data/libero.yaml \
      --stats data/stats/libero_stats.json --stats-downsample-rate 1
  

• Gate: ls data/stats/libero_stats.json data/libero/libero_10_no_noops_lerobot both resolve.

Step 4 — Smoke test (the real verification)

source .env
CUDA_VISIBLE_DEVICES=0 bash scripts/run/finetune.sh 1 libero --test --max_datasets 1 \
    model.max_steps=2 model.num_workers=2

(Add model.pretrained_ckpt=null to test the pipeline with random init before checkpoints are ready.)

• Gate (must all appear in the log / runs/libero/test/):
  • first-batch pixel_values shape log → data pipeline OK
  • ActionCodec round-trip LOSSLESS
  • decreasing per-step Loss:
  • Max step 2 reached, stop training and exit code 0
• The --test flag forces offline logging (logger.mode=offline); no SwanLab/W&B key needed.

Step 5 — Report

Summarize: env (torch+cuda), how checkpoints/data were obtained (download vs symlink), the checkpoint match ratio (Loaded N/N), and the smoke's loss trajectory. Then give the real training command: bash scripts/run/finetune.sh 1 libero (single GPU) or 8 libero (multi-GPU).

Notes / gotchas

• Training needs no LIBERO simulator. Only evaluation rollouts (scripts/run/eval_libero.sh) do — install it separately with uv pip install -e <LIBERO> --no-deps (never without --no-deps).
• --dry-run still runs 1 train + 1 eval step (it is not config-only), so it also needs data + checkpoints.
• flash_attn has no __version__; importing it successfully is the success signal.