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Tarek ZiadeandClaude Opus 5.5
34f416369d
* Move the GPU CI images to the Python in `.python-version` The NVIDIA images (all-latest, quantization, deepspeed-latest) and the AMD DeepSpeed image used Ubuntu 22.04's system Python 3.10. They now get Python from uv at `PYTHON_VERSION`, in a venv on PATH (same setup as transformers-intel-cpu), so `python`, `python3` and `pip` resolve to it. `python-is-python3` is no longer needed for DeepSpeed's `#!/usr/bin/env python` scripts. transformers-pytorch-amd-gpu ships torch in its base image's Python, so its base tag is picked from `PYTHON_VERSION` (rocm7.2_ubuntu22.04_py3.11_pytorch_release_2.10.0). AMD only publishes pre-built flash-attn wheels for Python 3.10, so flash-attn is now built from source, from the commit AMD's wheel was built from (0e60e39) and for the same gfx942/gfx950 targets. The build workflows pass `PYTHON_VERSION` from `.python-version`. Unused images (pytorch-gpu, transformers-gpu, pytorch-tpu, deepspeed-nightly) are left as they are. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> * Allow building test GPU images with a postfix from workflow_dispatch Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> * Remove the GitHub API cache-bust from the AMD docker images * Bust the AMD images' clone cache with the commit being built Replaces the removed GitHub API `ADD`: the build workflow now passes `REF=${{ github.sha }}` to the AMD images, so the `git checkout $REF` step (and every layer after it) changes, and is rebuilt, for each new commit. No network call, and the image is pinned to the commit it was built from. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> * Share the common GPU image steps through a `gpu-common` build context The GPU dockerfiles start from different CUDA/ROCm base images, so they can't share a base image, but they repeated the same steps on top of them. These now live in `docker/gpu-common/`, passed to the builds as the `gpu-common` named build context: - `install-python.sh`: Python from `PYTHON_VERSION` with uv, in a venv (all-latest, quantization, deepspeed-latest, deepspeed-amd) - `finish-transformers-install.sh`: uninstall `kernels` + `setup.py develop` (all five images) - `check-torch.sh`: import torch and check it still matches the `PYTORCH` pin (all-latest, quantization, and now deepspeed-latest) The torch installs stay per image, their differences are intended. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> * Install a pre-built flash-attention wheel in the AMD image AMD's mirror has no Python 3.11 flash-attention wheel, and building the CK backend from source on the image builder takes hours. Use the torch 2.10 / cp311 ROCm wheel from mjun0812/flash-attention-prebuild-wheels instead, pinned by checksum, and import it at build time to catch an ABI mismatch with the base image's torch. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> * Build flash-attention from source in the AMD image, tuned like the NVIDIA one Drop the third-party pre-built wheel and build the CK backend from the upstream commit AMD's wheel was built from, with the same tweaks as the NVIDIA FA2 build: MAX_JOBS derived from the runner's RAM (setup.py's default leaves most of the runner idle) and a filtered build log, since hipify's output fills BuildKit's per-step log limit before compilation starts. Give the AMD image job an explicit timeout below GitHub's 6h default. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> * Leave the AMD PyTorch image out of the Python move #49460 moves `transformers-pytorch-amd-gpu` to a ROCm 10 / Python 3.14 base image that ships flash-attention, while AMD only publishes Python 3.10 flash-attention wheels for the current base and building them from source takes hours. Keep that image (Dockerfile and build job) as on main. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> * Drop the image postfix input of workflow_dispatch It was only needed to build test images for this PR. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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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). They all build on top ofci-base.dockerfile, using the Python version from the repo's.python-version. Build them withdocker buildx bakefrom the repository root, e.g.PYTHON_VERSION=$(cat .python-version) docker buildx bake -f docker/docker-bake.hcl torch-light(a plaindocker buildwon't resolve theci-basestage). - 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) The GPU ones share the steps ingpu-common/(Python from the repo's.python-version, finishing thetransformersinstall, checking the pinned torch) through a named build context, e.g.docker build --build-context gpu-common=docker/gpu-common --build-arg PYTHON_VERSION=$(cat .python-version) docker/transformers-all-latest-gpu.
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! 🤗