e82e7a02e9
* 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>
Generating the documentation
To generate the documentation, you first have to build it. Several packages are necessary to build the doc, you can install them with the following command, at the root of the code repository:
pip install -e . -r docs-requirements.txt
You will also need nodejs. Please refer to their installation page
NOTE
You only need to generate the documentation to inspect it locally (if you're planning changes and want to
check how they look before committing for instance). You don't have to git commit the built documentation.
Building the documentation
Once you have setup the doc-builder and additional packages, you can generate the documentation by
typing the following command:
doc-builder build lerobot docs/source/ --build_dir ~/tmp/test-build
You can adapt the --build_dir to set any temporary folder that you prefer. This command will create it and generate
the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite
Markdown editor.
Previewing the documentation
To preview the docs, first install the watchdog module with:
pip install watchdog
Then run the following command:
doc-builder preview lerobot docs/source/
The docs will be viewable at http://localhost:3000. You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
NOTE
The preview command only works with existing doc files. When you add a completely new file, you need to update _toctree.yml & restart preview command (ctrl-c to stop it & call doc-builder preview ... again).
Adding a new element to the navigation bar
Accepted files are Markdown (.md).
Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
the filename without the extension in the _toctree.yml file.
Renaming section headers and moving sections
It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
Sections that were moved:
[ <a href="#section-b">Section A</a><a id="section-a"></a> ]
and of course, if you moved it to another file, then:
Sections that were moved:
[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
Use the relative style to link to the new file so that the versioned docs continue to work.
For an example of a rich moved sections set please see the very end of the transformers Trainer doc.
Adding a new tutorial
Adding a new tutorial or section is done in two steps:
- Add a new file under
./source. This file can either be ReStructuredText (.rst) or Markdown (.md). - Link that file in
./source/_toctree.ymlon the correct toc-tree.
Make sure to put your new file under the proper section. If you have a doubt, feel free to ask in a Github Issue or PR.
Writing source documentation
Values that should be put in code should either be surrounded by backticks: `like so`. Note that argument names
and objects like True, None or any strings should usually be put in code.
Writing a multi-line code block
Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
```
# first line of code
# second line
# etc
```
Adding an image
Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted dataset like
the ones hosted on hf-internal-testing in which to place these files and reference
them by URL. We recommend putting them in the following dataset: huggingface/documentation-images.
If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
to this dataset.