* docs(benchmarks): add benchmark integration guide and standardize benchmark docs
Add a comprehensive guide for adding new benchmarks to LeRobot, and
refactor the existing LIBERO and Meta-World docs to follow the new
standardized template.
* refactor(envs): move dispatch logic from factory into EnvConfig subclasses
Replace hardcoded if/elif chains in factory.py with create_envs() and
get_env_processors() methods on EnvConfig. New benchmarks now only need
to register a config subclass — no factory.py edits required.
Net -23 lines: factory.py shrinks from ~200 to ~70 lines of logic.
* docs(benchmarks): clean up adding-benchmarks guide for clarity
Rewrite for simpler language, better structure, and easier navigation.
Move quick-reference table to the top, fold eval explanation into
architecture section, condense the doc template to a bulleted outline.
* fix link
* fix task count
* fix: enable SmolVLA eval on LIBERO with custom camera mappings
- Thread camera_name_mapping from LiberoEnv config through to gym envs
- Sync features_map with camera_name_mapping in LiberoEnv.__post_init__
- Fix render() to use first available camera instead of hardcoded "image"
- Handle non-dict final_info in rollout by falling back to info["is_success"]
- Add use_peft legacy field to SmolVLAConfig for checkpoint compat
- Add defaults to GR00TN15Config init=False fields for transformers 5.3
* fix: use direct AutoresetMode import for gymnasium compat
* fix: handle gymnasium < 1.0 without AutoresetMode
* refactor: revert policy changes, keep env-only camera mapping fixes
- Revert GR00T N1.5 default_factory/default changes (transformers compat)
- Revert SmolVLA use_peft legacy field
- Apply ruff formatting fixes
- camera_name_mapping stays entirely in env/eval layer (no policy changes)
* Update docs/source/env_processor.mdx
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* feat(envs): lazy env init + AsyncVectorEnv as default for n_envs > 1
LiberoEnv and MetaworldEnv previously allocated GPU resources (EGL context,
OpenGL framebuffer) in __init__, before AsyncVectorEnv's fork(). Worker
processes inherited stale GPU handles, causing EGL_BAD_CONTEXT crashes on
first render.
Fix: defer OffScreenRenderEnv / MT1 construction to _ensure_env(), called on
first reset() or step() inside the worker subprocess. Each worker creates its
own clean context after fork().
Also fixes lerobot_eval.py:170 (add_envs_task TODO): replace with
env.call("task") which works with both SyncVectorEnv and AsyncVectorEnv.
AsyncVectorEnv is now the default for n_envs > 1; auto-downgraded to
SyncVectorEnv when n_envs=1 (no benefit, less overhead).
Expected speedup: ~15-20x for LIBERO Spatial with batch_size=50.
* fix: close envs between tasks to prevent worker process accumulation
eval_policy_all never closed environments after each task completed,
causing AsyncVectorEnv worker processes to accumulate (N_tasks × n_envs).
This led to OOM, BrokenPipeError and EOFError on multi-task benchmarks.
Also fixes:
- AsyncVectorEnv compat in envs/utils.py (use get_attr/call instead of .envs)
- Tuple task handling in tokenizer_processor and lerobot_eval
- _LazyAsyncVectorEnv for deferred worker spawning in LIBERO
* fix(eval): use task_description instead of task for language conditioning
env.call("task") returns the LIBERO task name with underscores
(e.g. "pick_up_the_black_bowl_...") instead of the natural language
description ("pick up the black bowl ..."). The VLM tokenizes these
completely differently, causing 0.0 reward across all episodes.
* docs: update adding_benchmarks for async env changes
- Replace add_envs_task reference with env.call("task_description")
- Update use_async_envs default to True
- Add note about lazy GPU init for AsyncVectorEnv compatibility
* feat(eval): batch_size=auto + faster env loading
- batch_size=0 (default) auto-tunes based on CPU cores, capped by
n_episodes and 64. Removes the need for users to guess the right
value. The old batch_size > n_episodes error is replaced by silently
clamping to n_episodes.
- _LazyAsyncVectorEnv accepts pre-computed spaces so only one temp env
is created per suite (not per task). For libero_spatial (10 tasks)
this avoids 9 redundant LiberoEnv instantiations during env setup.
* docs: add evaluation guide and update benchmarks doc
- New docs/source/evaluation.mdx covering lerobot-eval usage, batch_size
auto-tuning, AsyncVectorEnv performance, tuning tips, output format,
multi-task evaluation, and programmatic usage.
- Add evaluation page to _toctree.yml under Benchmarks section.
- Update adding_benchmarks.mdx to reference batch_size auto default and
link to the evaluation guide.
* docs(evaluation): remove benchmark table, rename section header
* perf(eval): shared memory, observation passthrough, task prefetch
- AsyncVectorEnv now uses shared_memory=True for zero-copy observation transfer
- LiberoEnvConfig.gym_kwargs passes observation_height/width to the env
- eval_policy_all prefetches next task's workers while current task runs
* style: ruff format
* chore: revert env_processor.mdx changes (not part of this PR)
* ci(benchmarks): add isolated integration tests for libero and metaworld
Each benchmark gets its own Docker image (lerobot[libero] / lerobot[metaworld]
only) so incompatible dep trees cannot collide. A 1-episode smoke eval runs
per benchmark on GPU runners.
* ci(benchmarks): pin action hashes and use uv sync --locked
* ci(benchmarks): trigger only on envs/ or lerobot_eval.py changes
* fix(ci): set LIBERO_DATA_FOLDER to bypass interactive stdin prompt
libero/__init__.py calls input() to ask about a custom dataset path,
which raises EOFError when stdin is closed inside Docker. Setting
LIBERO_DATA_FOLDER skips the prompt entirely.
* docs(benchmarks): add CI smoke test step to adding_benchmarks guide
* fix(ci): pre-create libero config in Dockerfile to bypass stdin prompt
libero/__init__.py calls input() when ~/.libero/config.yaml is missing.
We write the config at image build time (without importing libero) so
the prompt never fires at runtime. Also trigger CI on pyproject.toml changes.
* fix(ci): use shell to create libero config instead of multiline python -c
The multiline RUN python -c "..." was being parsed as Dockerfile
instructions. Use printf to write ~/.libero/config.yaml directly.
* fix(ci): point libero config to bundled package init_files
The config was pointing to /tmp/libero_init which doesn't exist.
Use importlib.util.find_spec to locate the hf-libero package directory
and write paths to the actual bundled bddl_files/init_files/assets.
* fix(ci): add smolvla extra to benchmark Dockerfiles
num2words (required by SmolVLM processor) is declared in lerobot[smolvla],
not lerobot[libero/metaworld]. Install both extras together.
* fix(eval): render_frame covers _LazyAsyncVectorEnv
isinstance(env, AsyncVectorEnv) silently skipped _LazyAsyncVectorEnv,
causing video rendering to produce no frames on the default async path.
Switch to hasattr(env, "call") so any async-compatible env (including
_LazyAsyncVectorEnv) hits the call("render") branch.
* refactor(envs): remove unused _get_sub_env_attr helper
_get_sub_env_attr was defined but never called anywhere in the codebase.
_sub_env_has_attr (its sibling) is kept — it is actively used in utils.py.
* chore: apply prettier formatting to docs
* docs(env_processor): remove deprecated add_envs_task from pipeline example
add_envs_task is replaced by env.call("task_description") in this PR.
Remove it from the pipeline walkthrough and renumber the steps (8→7).
* refactor(envs): remove __del__ from _LazyAsyncVectorEnv
__del__ is unreliable as a cleanup mechanism. close() is already called
explicitly in the eval loop's finally block, so the finalizer is redundant.
* fix(eval): prefetch next task's workers after close to avoid GPU memory overlap
Previously, next task's AsyncVectorEnv workers were spawned while the
current task was still running, causing both tasks' GPU contexts to coexist.
Moving the prefetch start into the finally block (after env.close()) ensures
workers for task N+1 only spin up once task N has released GPU memory.
* refactor(envs): move _LazyAsyncVectorEnv to utils and apply to metaworld
_LazyAsyncVectorEnv lived in libero.py but metaworld had the same OOM
problem: all tasks' AsyncVectorEnv workers were spawned eagerly, wasting
GPU memory for tasks not yet running.
Move the class to envs/utils.py so both environments share it, then apply
the same is_async + lazy wrapping pattern in create_metaworld_envs.
* chore: remove out-of-scope benchmark/CI/docs files from PR
Benchmark CI workflow, Dockerfiles, benchmark docs, evaluation smoke-test
doc, and dispatch tests belong in a separate PR. Scope this PR to the
async env init changes only.
* chore: restore adding_benchmarks + test_dispatch, drop env_processor changes
- Restore docs/source/adding_benchmarks.mdx (belongs in this PR)
- Restore tests/envs/test_dispatch.py (belongs in this PR)
- Revert docs/source/env_processor.mdx to main (out of scope for this PR)
* docs(adding_benchmarks): remove CI smoke test step (coming in separate PR)
Step 7 (Dockerfile + benchmark_tests.yml CI job) and its table rows are
out of scope for this PR. The CI infrastructure will be added on top in a
follow-up PR.
* refactor(envs): remove unused add_envs_task
Replaced by env.call("task_description") in lerobot_eval.py. No callers
remain in the codebase.
* style: fix prettier formatting in env_processor.mdx
* fix(ci): use root container chmod to fix PermissionError on artifact dirs
Running chmod on the host doesn't propagate into Docker due to UID/SELinux
mismatch. Instead, spin up the image as root to mkdir+chmod from inside
the container before the eval run mounts the same path.
* fix(ci): re-chmod artifacts after eval to fix unreadable files
Files created by user_lerobot inside the eval container inherit a
restrictive umask, making them unreadable by the runner after the
container exits. Add a post-eval 'docker run --user root' chmod step
so upload-artifact can find the video files.
* feat(ci): add monthly schedule trigger for benchmark tests
Runs on the 1st of every month at 02:00 UTC in addition to the
existing push/PR and manual dispatch triggers.
* fix(ci): change benchmark schedule from monthly to weekly (every Monday)
* fix(ci): use docker cp instead of bind mounts for artifacts
Bind mounts on these runners don't surface container-written files on
the host path (likely DinD/socket-mount setup). Switch to named
containers + docker cp, which copies directly through the daemon and
lands files in the runner's accessible filesystem.
* fix(ci): write eval output to /tmp inside container
user_lerobot cannot create /artifacts at the container root.
Use /tmp/eval-artifacts (always writable) then docker cp it out.
* feat(ci): add parse_eval_metrics step to benchmark workflow
Adds scripts/ci/parse_eval_metrics.py and wires it into both Libero and
MetaWorld jobs so the dashboard can read pc_success, avg_sum_reward and
eval_s from the metrics artifact instead of relying on GitHub step timing.
* feat(ci): add Libero train+eval smoke test (1 step, eval_freq=1)
Runs accelerate launch --num_processes=1 lerobot-train with:
- steps=1, batch_size=1, dataset.episodes=[0] (episode 0 only)
- eval_freq=1 so the training loop triggers eval after step 1
- eval.n_episodes=1, eval.use_async_envs=false
Tests the full train→eval-within-training pipeline in the existing
libero-benchmark-libero:ci image (no extra Docker build cost).
Uploads eval video from /tmp/train-smoke/eval/ as libero-train-smoke-video.
* feat(ci): extract task descriptions and embed in metrics artifact
- Add scripts/ci/extract_task_descriptions.py: runs inside the benchmark
Docker container (LIBERO/MetaWorld installed) after lerobot-eval and
writes task_descriptions.json mapping task keys to NL instructions.
LIBERO: uses libero.libero.benchmark to get suite.get_task(i).language.
MetaWorld: formats task name as human-readable label.
- Call extraction at the end of each eval bash-c (|| true so never fatal).
- parse_eval_metrics.py reads task_descriptions.json and includes it in
metrics.json so the health dashboard Space can label videos by task.
* fix(ci): call extract_task_descriptions.py after eval in benchmark jobs
The task descriptions were never populated in metrics.json because
extract_task_descriptions.py was never invoked. The script exists and
parse_eval_metrics.py already looks for its output — the call was
simply missing from the workflow.
Appends the extraction step to the existing bash -c block (runs inside
the container where libero/metaworld is installed) so task_descriptions.json
is written to the eval-artifacts dir before docker cp copies it out.
* fix(test): use SyncVectorEnv in test_base_create_envs
AsyncVectorEnv spawns new subprocesses that do not inherit the
in-process gym registration created by the test. Pass
use_async_envs=False since this test validates dispatch logic,
not async parallelism.
* perf(ci): split Dockerfile dep-install from source-copy for faster rebuilds
The dep-install layer (uv sync) now only depends on pyproject.toml,
uv.lock, and a minimal package stub — not the full src/ tree. Source
code changes only rebuild the final COPY layer (seconds, not minutes).
Also switch from type=local cache (lost on ephemeral runners) to
type=gha (persisted in GitHub Actions cache, shared across all runs).
Before: every src/ change → full uv sync rebuild (~8-10 min)
After: src/-only change → cached dep layer, ~30s source copy
* fix(ci): add Docker Hub login to avoid pull rate limits
Anonymous pulls from Docker Hub are rate-limited to 100/6h, which
fails when multiple benchmark jobs pull nvidia/cuda in parallel.
Add docker/login-action step (conditional on DOCKERHUB_USERNAME var)
to authenticate and get 200 pulls/6h.
Setup: add DOCKERHUB_USERNAME as a repository variable and
DOCKERHUB_TOKEN as a repository secret in GitHub Settings.
* fix(ci): use existing DOCKERHUB_LEROBOT_USERNAME/PASSWORD secrets
* fix(ci): use env context for secrets check in step if-condition
Step-level 'if' cannot reference 'secrets' directly. Expose the
secret via an env var and check that instead.
* fix(ci): simplify Docker Hub login to match existing workflows
Drop the conditional guard — other workflows (docker_publish,
full_tests) call docker/login-action unconditionally.
* fix(ci): switch Docker cache from type=gha to type=registry
GHA cache is capped at 10GB per repo — a single CUDA + PyTorch +
benchmark image is ~8GB so the cache evicts before it's reused.
Switch to type=registry which pushes cache layers to Docker Hub
(huggingface/lerobot-benchmark-cache:{libero,metaworld}). No size
limit, layers persist until explicitly deleted, and shared across
all runners and branches.
* fix(ci): use GHCR for Docker layer cache (Docker Hub push denied)
Docker Hub CI token can't push to new repos. GHCR works out of the
box — GITHUB_TOKEN has automatic packages:write for the repo owner.
- Add GHCR login step (github.actor + GITHUB_TOKEN)
- Switch cache refs to ghcr.io/huggingface/lerobot/cache-benchmark
- Add packages:write at job level (not workflow, per zizmor)
- Keep Docker Hub login for pulling nvidia/cuda base image
* fix(ci): remove GHCR cache (org blocks GITHUB_TOKEN package writes)
The huggingface org restricts GHCR package creation via GITHUB_TOKEN,
causing 403 on cache export. Remove all registry caching and GHCR
login. The Dockerfile layer split (deps vs source) still helps when
the runner has a warm Docker daemon.
Also fix the metaworld job which had a stale conditional Docker Hub
login and was missing the GHCR login entirely.
* fix(ci): address PR review feedback for benchmark smoke tests
Security:
- Remove "Login to Hugging Face" step — it was a no-op (ephemeral
--rm container) that exposed the HF token via CLI argument in
docker inspect / /proc/*/cmdline. The eval step already
re-authenticates via env var.
Functional:
- Remove feat/benchmark-ci from push trigger branches (won't exist
post-merge).
Dockerfiles:
- Pin uv to 0.8.0 (was unpinned, fetching whatever latest ships).
- Add comment explaining the chmod +x ptxas workaround (Triton
packaging bug — ships ptxas without execute bit).
Scripts:
- parse_eval_metrics.py: add note that it runs on bare host and must
stay stdlib-only.
- parse_eval_metrics.py: add NaN guard for avg_sum_reward and eval_s
(was only guarding pc_success).
* ci(benchmarks): trigger on PRs targeting feat/benchmark-ci
Benchmark PRs (robomme, libero-plus, robocerebra, robotwin) target
feat/benchmark-ci, not main. Without this, the workflow never runs
on those PRs.
* fix(docker): use uv pip install instead of uv sync (cross-extra conflict)
uv sync --locked validates the entire lockfile across all extras.
Since robomme depends on mani-skill which pins numpy<2.0, and the
base project requires numpy>=2.0, the full lockfile is unsatisfiable.
Switch to uv pip install -e ".[libero,smolvla]" which only resolves
the requested extras for the current Python version and platform,
avoiding the cross-extra numpy conflict entirely.
* chore: revert configs.py, factory.py, test_dispatch.py to main
These use_async_envs default changes belong to the async-vector-env
PR (#3274), not this CI PR. Restore to match origin/main.
* fix: address PR review feedback — broken link, NaN guard, zizmor tags, fork skip
- Remove broken Triton issue link from Dockerfile.benchmark.libero
- Add module-level _safe_int helper to guard n_episodes against NaN
- Move _safe_float to module level alongside _safe_int
- Add # zizmor: ignore[unpinned-uses] to all upload-artifact@v4 steps
- Add if: env.HF_USER_TOKEN != '' to Libero smoke eval for fork PRs
* fix(ci): add fork PR guard to train-smoke and MetaWorld eval steps
Add if: env.HF_USER_TOKEN != '' to the Libero train+eval smoke and
MetaWorld smoke eval steps so fork PRs without the secret skip gracefully.
* fix(ci): remove feat/benchmark-ci from PR trigger branches
* refactor(docker): rebase benchmark images on nightly lerobot-gpu
Use huggingface/lerobot-gpu:latest as base for both libero and metaworld
benchmark Dockerfiles instead of building from nvidia/cuda scratch. The
nightly image already has all extras installed via uv sync --extra all,
so we only need to overlay the PR source code (and libero asset setup).
This eliminates duplicated system dep installation, Python setup, uv
venv creation, and the Triton ptxas workaround from both files.
---------
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
* feat(ci): add uv.lock
* feat(ci): use uv.lock in CI PR testing
* chore(ci): rename nightly to docker publish and test
* feat(ci): automated update of uv.lock + remove unbound check + docker images now use uv.lock
* fix(ci): add --force-with-lease + set -e for silent erros
* fix(ci): skip HF log in (and tests) in forks and community PRs
* chore(test): remove comment about test meant to be only run locally
* fix(tests): no hf log in decorator for xvla
* fix(test): no decorator in yield
* fix(ci): prevent runner group error on fork pushes
Add repository check to unbound_deps_tests workflow to ensure
aws-general-8-plus runner group is only used on main repository,
preventing 'Required runner group not found' errors on forks.
* fix(ci): use gating job to prevent runner allocation on forks
The previous approach failed because GitHub evaluates runs-on before if conditions.
Now using a check-repo job that runs on ubuntu-latest first, and all jobs with
special runners depend on it and check its output before being scheduled.
* fix(ci): add gating job to full_tests to prevent runner allocation on forks
Apply the same gating pattern used in unbound_deps_tests to full_tests.yml
to prevent GitHub from trying to allocate custom runners when workflows
run on forks. The check-repo job runs first on ubuntu-latest and all jobs
with custom runners depend on it and check its output.
* fix(ci): add repository check to unbound_deps_tests workflow
Add 'if: github.repository == huggingface/lerobot' check to build-and-push-docker job to prevent runner group access errors on forks, matching the pattern used in nightly.yml
* fix(ci): add repository check to full_tests workflow
Add 'if: github.repository == huggingface/lerobot' check to build-and-push-docker and gpu-tests jobs to prevent runner group access errors on forks
* refactor(ci): remove redundant check from gpu-tests job
gpu-tests depends on build-and-push-docker via needs, so it will automatically skip when the parent job is skipped
* refactor(ci): remove unnecessary fork check from full-tests job
full-tests runs on ubuntu-latest which is available to all forks, no need to restrict it
---------
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
* pi fixes for dependencies
* add walls sarm conflict
* also add conflicts for pi
* fix(ci): use --extra all instead of --all-extras + --no-extra
---------
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
* support wallx
* fix bugs in flow
* incorporate wallx model into lerobot
* update the policy methods
* reduce to least config and params & pass lerobot basic test
* fixed dtype bugs
* add wallx dependencies
* update
* remove flash-attn requirement && fix bug in inference and fast mode
* fix bug for inference
* add some small modifications
* fix pre-commit errors
* remove lerobot[wallx]
* fix ci
* fix precommit issues
* fix: exclude wallx extra properly in CI workflows
* fix: add uv conflicts for wallx transformers version
* fix: peft test import
* pre-commit
* only export WallXConfig from wall_x package to avoid peft import in CI
* remove torch dep
* precommit
* add import
---------
Co-authored-by: vincentchen <chenlufang@x2robot.com>
Co-authored-by: Geoffrey19 <sympathischmann35@gmail.com>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Pepijn <pepijn@huggingface.co>
* Enhance training and logging functionality with accelerator support
- Added support for multi-GPU training by introducing an `accelerator` parameter in training functions.
- Updated `update_policy` to handle gradient updates based on the presence of an accelerator.
- Modified logging to prevent duplicate messages in non-main processes.
- Enhanced `set_seed` and `get_safe_torch_device` functions to accommodate accelerator usage.
- Updated `MetricsTracker` to account for the number of processes when calculating metrics.
- Introduced a new feature in `pyproject.toml` for the `accelerate` library dependency.
* Initialize logging in training script for both main and non-main processes
- Added `init_logging` calls to ensure proper logging setup when using the accelerator and in standard training mode.
- This change enhances the clarity and consistency of logging during training sessions.
* add docs and only push model once
* Place logging under accelerate and update docs
* fix pre commit
* only log in main process
* main logging
* try with local rank
* add tests
* change runner
* fix test
* dont push to hub in multi gpu tests
* pre download dataset in tests
* small fixes
* fix path optimizer state
* update docs, and small improvements in train
* simplify accelerate main process detection
* small improvements in train
* fix OOM bug
* change accelerate detection
* add some debugging
* always use accelerate
* cleanup update method
* cleanup
* fix bug
* scale lr decay if we reduce steps
* cleanup logging
* fix formatting
* encorperate feedback pr
* add min memory to cpu tests
* use accelerate to determin logging
* fix precommit and fix tests
* chore: minor details
---------
Co-authored-by: AdilZouitine <adilzouitinegm@gmail.com>
Co-authored-by: Steven Palma <steven.palma@huggingface.co>
* refactor(scripts): update system info script
* chore(scripts): rename info script
* feat(scripts): add entrypoint for info
* chore(ci): update issue report template
Steven Palma