1ecd52e50a
* fix dataset run_object_detection * bump version * keep same dataset actually * torchcodec in docstrings and testing utils * torchcodec in dockerfiles and requirements * remove duplicate * add torchocodec to all the remaining docker files * fix tests * support torchcodec in audio classification and ASR * [commit to revert] build ci-dev images * [commit to revert] trigger circleci * [commit to revert] build ci-dev images * fix * fix modeling_hubert * backward compatible run_object_detection * revert ci trigger commits * fix mono conversion and support torch tensor as input * revert map_to_array docs + fix it * revert mono * nit in docstring * style * fix modular --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Dockers for transformers
In this folder you will find various docker files, and some subfolders.
- dockerfiles (ex:
consistency.dockerfile) present under~/dockerare used for our "fast" CIs. You should be able to use them for tasks that only need CPU. For exampletorch-lightis a very light weights container (703MiB). - subfolders contain dockerfiles used for our
slowCIs, which can be used for GPU tasks, but they are BIG as they were not specifically designed for a single model / single task. Thus the~/docker/transformers-pytorch-gpuincludes additional dependencies to allow us to run ALL model tests (saylibrosaortesseract, which you do not need to run LLMs)
Note that in both case, you need to run uv pip install -e ., which should take around 5 seconds. We do it outside the dockerfile for the need of our CI: we checkout a new branch each time, and the transformers code is thus updated.
We are open to contribution, and invite the community to create dockerfiles with potential arguments that properly choose extras depending on the model's dependencies! 🤗