PufferLib

Choose the version before choosing an API. Reviewed 2026-10-01:

For 5.0, read references/native-5.md. The reviewed revision is 6ffa5b10dbbbe4d1e8288367c7d9d3acd3bad4a2. Its CLI is ./puffer train after building an environment, not puffer train ENV_NAME. There is no 5.0 Python emulation/vector API or --slowly fallback.

The bundled plan schema deliberately supports only 3.0 and pinned 4.0; it does not launch training. All native/PufferLib training examples are source-reviewed, illustrative, and not executed in this review. Bundled synthetic checks are executed CPU tests, not evidence of PufferLib installation or learning quality. The PyPI sdist was hash-verified and its Python sources inspected; the moving 3.0 branch differs, including its load_policy and logger contracts.

Safe defaults

1. Start with bundled synthetic, CPU-only, network-free tools.
2. Do not import an arbitrary environment by dotted path. Bundled tools accept only allowlisted built-ins and slug identifiers.
3. Do not install or execute an unreviewed environment package, native extension, ROM, map, checkpoint, or pickle file.
4. Verify official source, immutable revision, licenses, checksums or attestations, and build hooks. Sandbox native builds and first execution.
5. Cap steps, environments, agents, workers, threads, buffers, memory, disk, render size, and wall time.
6. Keep training and evaluation environments/seeds separate.
7. Default logging to local/none. External logging requires explicit opt-in, disclosure acknowledgment, and separate artifact-upload approval.
8. Never pass W&B or Neptune credentials via CLI, INI, JSON, tags, run names, or logger configuration. Never print them.
9. Never dump all environment variables or recursively search for .env.
10. Hash checkpoint bytes before trusted, sandboxed loading; metadata inspection is not proof of safety.

First local checks

All bundled CLIs are dependency-free and emit strict JSON:

python3 scripts/env_template.py --help
python3 scripts/env_contract_validator.py
python3 scripts/benchmark_vectorization.py --backend serial
python3 scripts/train_template.py
python3 scripts/validate_plan.py
python3 scripts/repro_plan.py

Defaults are synthetic, deterministic, bounded, local, CPU-only, no-network, and dry-run where training would otherwise occur.

Installation and provenance

Published 3.0.0

PyPI supplies only pufferlib-3.0.0.tar.gz:

sha256: 7df3a3e3f5f894d78d2a1f5374097890aec01473183e748abefe4f3faa10eaa9
Requires-Python: >=3.9

After source/build review, create a pinned uv project:

uv venv --python 3.11
uv add --exact --no-sync "pufferlib==3.0.0"
uv lock
uv sync --frozen

These installation commands are illustrative and were not executed. Commit pyproject.toml and uv.lock; verify the archive digest and every resolved dependency. The source build can compile native code and fetch build assets, so resolve/build in a sandbox without credentials or sensitive mounts. The archive declares NumPy <2, Gymnasium <=0.29.1, and PettingZoo <=1.24.1; latest Gymnasium/NumPy are not valid substitutes for this profile. Its setup supports Linux/macOS and rejects other systems. Python classifiers alone do not establish a successful native build. The uploaded metadata does not pin Torch or CUDA; do not claim a supported CUDA matrix that PyPI does not declare.

Pinned 4.0 source

The reviewed branch head on 2026-07-23 was:

25647630e1b15330bb3153a5a0d3ff8d234c3acf

Pin the commit, not branch 4.0:

uv add --no-sync \
  "pufferlib @ git+https://github.com/PufferAI/PufferLib.git@25647630e1b15330bb3153a5a0d3ff8d234c3acf"
uv lock

The reviewed 4.0 package declares Python >=3.10 and Torch >=2.9. The reviewed PufferTank snapshot uses Ubuntu 24.04, Python 3.12, and an NVIDIA CUDA 13.0.2/cuDNN development image with the cu130 Torch index, but does not pin the exact Torch wheel or all system packages. Treat it as a reference, not a

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