mirror of
https://github.com/google-deepmind/alphafold3.git
synced 2026-06-02 11:54:36 +08:00
f236bc4e8c
Suggested in https://github.com/google-deepmind/alphafold3/pull/639/files PiperOrigin-RevId: 892848172 Change-Id: I483403805e4252d150b19b482917736bddca47fa
89 lines
4.2 KiB
Docker
89 lines
4.2 KiB
Docker
# Copyright 2024 DeepMind Technologies Limited
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#
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# AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
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# this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/
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#
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# To request access to the AlphaFold 3 model parameters, follow the process set
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# out at https://github.com/google-deepmind/alphafold3. You may only use these
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# if received directly from Google. Use is subject to terms of use available at
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# https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md
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FROM nvidia/cuda:12.6.3-base-ubuntu24.04
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# Some RUN statements are combined together to make Docker build run faster.
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# Get latest package listing, install python, git, wget, compilers and libs.
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# * git is required for pyproject.toml toolchain's use of CMakeLists.txt.
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# * gcc, g++, make are required for compiling HMMER and AlphaFold 3 libaries.
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# * zlib is a required dependency of AlphaFold 3.
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RUN DEBIAN_FRONTEND=noninteractive \
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apt-get update --quiet \
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&& apt-get install --yes --quiet python3.12 python3.12-dev \
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&& apt-get install --yes --quiet git wget gcc g++ make zlib1g-dev zstd
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# Install uv from the official repository. The version is pinned for
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# reproducibility.
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COPY --from=ghcr.io/astral-sh/uv:0.9.24 /uv /uvx /bin/
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# UV_COMPILE_BYTECODE=1 speeds up future container starts.
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# UV_PROJECT_ENVIRONMENT explicitly sets the virtual environment location.
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ENV UV_COMPILE_BYTECODE=1
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ENV UV_PROJECT_ENVIRONMENT=/alphafold3_venv
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RUN uv venv $UV_PROJECT_ENVIRONMENT
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ENV PATH="/hmmer/bin:/alphafold3_venv/bin:$PATH"
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# Install HMMER. Do so before copying the source code, so that docker can cache
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# the image layer containing HMMER. Alternatively, you could also install it
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# using `apt-get install hmmer` instead of bulding it from source, but we want
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# to have control over the exact version of HMMER and also apply the sequence
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# limit patch. Also note that eddylab.org unfortunately doesn't support HTTPS
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# and the tar file published on GitHub is explicitly not recommended to be used
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# for building from source.
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# Download, check hash, and extract the HMMER source code.
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RUN mkdir /hmmer_build /hmmer ; \
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wget http://eddylab.org/software/hmmer/hmmer-3.4.tar.gz --directory-prefix /hmmer_build ; \
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(cd /hmmer_build && echo "ca70d94fd0cf271bd7063423aabb116d42de533117343a9b27a65c17ff06fbf3 hmmer-3.4.tar.gz" | sha256sum --check) && \
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(cd /hmmer_build && tar zxf hmmer-3.4.tar.gz && rm hmmer-3.4.tar.gz)
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# Apply the --seq_limit patch to HMMER.
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COPY docker/jackhmmer_seq_limit.patch /hmmer_build/
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RUN (cd /hmmer_build && patch -p0 < jackhmmer_seq_limit.patch)
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# Build HMMER.
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RUN (cd /hmmer_build/hmmer-3.4 && ./configure --prefix /hmmer) && \
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(cd /hmmer_build/hmmer-3.4 && make -j) && \
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(cd /hmmer_build/hmmer-3.4 && make install) && \
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(cd /hmmer_build/hmmer-3.4/easel && make install) && \
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rm -R /hmmer_build
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# Copy the AlphaFold 3 source code from the local machine to the container and
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# set the working directory to there.
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COPY . /app/alphafold
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WORKDIR /app/alphafold
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# Install the exact dependency tree using uv and cache the build artifacts.
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# --frozen: do not update the lockfile during build.
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# --all-groups: install development/test dependencies defined in pyproject.toml.
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# --no-editable: install as a static package.
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# If using this as a recipe for local installation, we recommend removing the
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# --frozen and --no-editable flags.
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RUN --mount=type=cache,target=/root/.cache/uv \
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UV_LINK_MODE=copy uv sync --frozen --all-groups --no-editable
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# Build chemical components database (this binary was installed by uv sync).
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RUN uv run build_data
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# To work around a known XLA issue causing the compilation time to greatly
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# increase, the following environment variable setting XLA flags must be enabled
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# when running AlphaFold 3. Note that if using CUDA capability 7 GPUs, it is
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# necessary to set the following XLA_FLAGS value instead:
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# ENV XLA_FLAGS="--xla_disable_hlo_passes=custom-kernel-fusion-rewriter"
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# (no need to disable gemm in that case as it is not supported for such GPU).
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ENV XLA_FLAGS="--xla_gpu_enable_triton_gemm=false"
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# Memory settings used for folding up to 5,120 tokens on A100 80 GB.
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ENV XLA_PYTHON_CLIENT_PREALLOCATE=true
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ENV XLA_CLIENT_MEM_FRACTION=0.95
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CMD ["uv", "run", "python3", "run_alphafold.py"]
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