- 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)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
_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.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
__del__ is unreliable as a cleanup mechanism. close() is already called
explicitly in the eval loop's finally block, so the finalizer is redundant.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
_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.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
num2words (required by SmolVLM processor) is declared in lerobot[smolvla],
not lerobot[libero/metaworld]. Install both extras together.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The multiline RUN python -c "..." was being parsed as Dockerfile
instructions. Use printf to write ~/.libero/config.yaml directly.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 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
Made-with: Cursor
- 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.
Made-with: Cursor
- 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.
Made-with: Cursor
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.
Made-with: Cursor
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
Made-with: Cursor
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.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* add: a flexible transformation registry
* fix: image transforms can be set both at init and after
* add: tests
* fix: take in review
* feat(datasets): add image transform setters
* fix: pre-commit
* fix: CI
---------
Signed-off-by: Francesco Capuano <74058581+fracapuano@users.noreply.github.com>
- 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
Made-with: Cursor
* 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
Pepijn