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chore(docs): prioritize use of entry points in docs + fix nightly badge (#1692)

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* chore(docs): fix typo in nightly badge

* chore(docs): prioritize the use of entrypoints for consistency
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Steven Palma
2025-08-07 14:25:44 +02:00
committed by GitHub
parent c66cd40176
commit ce3b9f627e
31 changed files with 105 additions and 107 deletions
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README.md
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@@ -6,7 +6,7 @@
<div align="center">
[![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nighty.yml?query=branch%3Amain)
[![Tests](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml/badge.svg?branch=main)](https://github.com/huggingface/lerobot/actions/workflows/nightly.yml?query=branch%3Amain)
[![Python versions](https://img.shields.io/pypi/pyversions/lerobot)](https://www.python.org/downloads/)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/huggingface/lerobot/blob/main/LICENSE)
[![Status](https://img.shields.io/pypi/status/lerobot)](https://pypi.org/project/lerobot/)
@@ -276,7 +276,7 @@ Check out [example 2](https://github.com/huggingface/lerobot/blob/main/examples/
We also provide a more capable script to parallelize the evaluation over multiple environments during the same rollout. Here is an example with a pretrained model hosted on [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht):
```bash
python -m lerobot.scripts.eval \
lerobot-eval \
--policy.path=lerobot/diffusion_pusht \
--env.type=pusht \
--eval.batch_size=10 \
@@ -288,10 +288,10 @@ python -m lerobot.scripts.eval \
Note: After training your own policy, you can re-evaluate the checkpoints with:
```bash
python -m lerobot.scripts.eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
lerobot-eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
```
See `python -m lerobot.scripts.eval --help` for more instructions.
See `lerobot-eval --help` for more instructions.
### Train your own policy
@@ -303,7 +303,7 @@ A link to the wandb logs for the run will also show up in yellow in your termina
\<img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/wandb.png" alt="WandB logs example"\>
Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python -m lerobot.scripts.eval --help` for more instructions.
Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `lerobot-eval --help` for more instructions.
#### Reproduce state-of-the-art (SOTA)
@@ -311,7 +311,7 @@ We provide some pretrained policies on our [hub page](https://huggingface.co/ler
You can reproduce their training by loading the config from their run. Simply running:
```bash
python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
lerobot-train --config_path=lerobot/diffusion_pusht
```
reproduces SOTA results for Diffusion Policy on the PushT task.