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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
.PHONY: tests
PYTHON_PATH := $(shell which python)
# If uv is installed and a virtual environment exists, use it
UV_CHECK := $(shell command -v uv)
ifneq ($(UV_CHECK),)
PYTHON_PATH := $(shell .venv/bin/python)
endif
export PATH := $(dir $(PYTHON_PATH)):$(PATH)
DEVICE ?= cpu
build-user:
docker build -f docker/Dockerfile.user -t lerobot-user .
build-internal:
docker build -f docker/Dockerfile.internal -t lerobot-internal .
test-end-to-end:
${MAKE} DEVICE=$(DEVICE) test-act-ete-train
${MAKE} DEVICE=$(DEVICE) test-act-ete-train-resume
${MAKE} DEVICE=$(DEVICE) test-act-ete-eval
${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-train
${MAKE} DEVICE=$(DEVICE) test-diffusion-ete-eval
${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-train
${MAKE} DEVICE=$(DEVICE) test-tdmpc-ete-eval
${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-train
${MAKE} DEVICE=$(DEVICE) test-smolvla-ete-eval
test-act-ete-train:
lerobot-train \
--policy.type=act \
--policy.dim_model=64 \
--policy.n_action_steps=20 \
--policy.chunk_size=20 \
--policy.device=$(DEVICE) \
--policy.push_to_hub=false \
--env.type=aloha \
--env.episode_length=5 \
--dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
--dataset.image_transforms.enable=true \
--dataset.episodes="[0]" \
--batch_size=2 \
--steps=4 \
--env_eval_freq=2 \
--eval.n_episodes=1 \
--eval.batch_size=1 \
--save_freq=2 \
--save_checkpoint=true \
--log_freq=1 \
--wandb.enable=false \
--output_dir=tests/outputs/act/
test-act-ete-train-resume:
lerobot-train \
--config_path=tests/outputs/act/checkpoints/000002/pretrained_model/train_config.json \
--resume=true
test-act-ete-eval:
lerobot-eval \
--policy.path=tests/outputs/act/checkpoints/000004/pretrained_model \
--policy.device=$(DEVICE) \
--env.type=aloha \
--env.episode_length=5 \
--eval.n_episodes=1 \
--eval.batch_size=1
test-diffusion-ete-train:
lerobot-train \
--policy.type=diffusion \
--policy.down_dims='[64,128,256]' \
--policy.diffusion_step_embed_dim=32 \
--policy.num_inference_steps=10 \
--policy.device=$(DEVICE) \
--policy.push_to_hub=false \
--env.type=pusht \
--env.episode_length=5 \
--dataset.repo_id=lerobot/pusht \
--dataset.image_transforms.enable=true \
--dataset.episodes="[0]" \
--batch_size=2 \
--steps=2 \
--env_eval_freq=2 \
--eval.n_episodes=1 \
--eval.batch_size=1 \
--save_checkpoint=true \
--save_freq=2 \
--log_freq=1 \
--wandb.enable=false \
--output_dir=tests/outputs/diffusion/
test-diffusion-ete-eval:
lerobot-eval \
--policy.path=tests/outputs/diffusion/checkpoints/000002/pretrained_model \
--policy.device=$(DEVICE) \
--env.type=pusht \
--env.episode_length=5 \
--eval.n_episodes=1 \
--eval.batch_size=1
test-tdmpc-ete-train:
lerobot-train \
--policy.type=tdmpc \
--policy.device=$(DEVICE) \
--policy.push_to_hub=false \
--env.type=pusht \
--env.episode_length=5 \
--dataset.repo_id=lerobot/pusht_image \
--dataset.image_transforms.enable=true \
--dataset.episodes="[0]" \
--batch_size=2 \
--steps=2 \
--env_eval_freq=2 \
--eval.n_episodes=1 \
--eval.batch_size=1 \
--save_checkpoint=true \
--save_freq=2 \
--log_freq=1 \
--wandb.enable=false \
--output_dir=tests/outputs/tdmpc/
test-tdmpc-ete-eval:
lerobot-eval \
--policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
--policy.device=$(DEVICE) \
--env.type=pusht \
--env.episode_length=5 \
--env.observation_height=96 \
--env.observation_width=96 \
--eval.n_episodes=1 \
--eval.batch_size=1
test-smolvla-ete-train:
lerobot-train \
--policy.type=smolvla \
--policy.n_action_steps=20 \
--policy.chunk_size=20 \
--policy.device=$(DEVICE) \
--policy.push_to_hub=false \
--env.type=aloha \
--env.episode_length=5 \
--dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
--dataset.image_transforms.enable=true \
--dataset.episodes="[0]" \
--batch_size=2 \
--steps=4 \
--env_eval_freq=2 \
--eval.n_episodes=1 \
--eval.batch_size=1 \
--save_freq=2 \
--save_checkpoint=true \
--log_freq=1 \
--wandb.enable=false \
--output_dir=tests/outputs/smolvla/
test-smolvla-ete-eval:
lerobot-eval \
--policy.path=tests/outputs/smolvla/checkpoints/000004/pretrained_model \
--policy.device=$(DEVICE) \
--env.type=aloha \
--env.episode_length=5 \
--eval.n_episodes=1 \
--eval.batch_size=1
# E2E annotation pipeline smoke test against a tiny in-memory fixture
# dataset. Opt-in (not part of `make test-end-to-end`) and uses a stub VLM
# backend, so it does not require a real model checkpoint or GPU.
annotation-e2e:
uv run python -m tests.annotations.run_e2e_smoke