add support for base image with uv (#2691)
This commit is contained in:
76
.github/workflows/base.yml
vendored
76
.github/workflows/base.yml
vendored
@@ -17,7 +17,7 @@ jobs:
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build-base:
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if: github.repository_owner == 'axolotl-ai-cloud'
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# this job needs to be run on self-hosted GPU runners...
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runs-on: axolotl-gpu-runner
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runs-on: ubuntu-latest-m
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strategy:
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fail-fast: false
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matrix:
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@@ -28,42 +28,50 @@ jobs:
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python_version: "3.11"
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pytorch: 2.5.1
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torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
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dockerfile: "Dockerfile-base"
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- cuda: "124"
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cuda_version: 12.4.1
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cudnn_version: ""
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python_version: "3.11"
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pytorch: 2.6.0
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torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
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dockerfile: "Dockerfile-base"
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- cuda: "126"
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cuda_version: 12.6.3
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cudnn_version: ""
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python_version: "3.11"
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pytorch: 2.6.0
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torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
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dockerfile: "Dockerfile-base"
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- cuda: "126"
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cuda_version: 12.6.3
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cudnn_version: ""
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python_version: "3.11"
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pytorch: 2.7.0
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torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
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dockerfile: "Dockerfile-base"
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- cuda: "128"
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cuda_version: 12.6.3
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cudnn_version: ""
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python_version: "3.11"
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pytorch: 2.7.0
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torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
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dockerfile: "Dockerfile-base"
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- cuda: "128"
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cuda_version: 12.8.1
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cudnn_version: ""
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python_version: "3.11"
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pytorch: nightly
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torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
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- cuda: "128"
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cuda_version: 12.8.1
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cudnn_version: ""
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python_version: "3.11"
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pytorch: next
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torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
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dockerfile: "Dockerfile-base-nightly"
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# # "next" is for release candidates of pytorch
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# - cuda: "128"
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# cuda_version: 12.8.1
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# cudnn_version: ""
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# python_version: "3.11"
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# pytorch: next
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# torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
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# dockerfile: "Dockerfile-base-next"
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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@@ -85,7 +93,59 @@ jobs:
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uses: docker/build-push-action@v4
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with:
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context: .
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file: ${{ matrix.pytorch == 'nightly' && './docker/Dockerfile-base-nightly' || matrix.pytorch == 'next' && './docker/Dockerfile-base-next' || './docker/Dockerfile-base' }}
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file: ./docker/${{ matrix.dockerfile }}
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push: ${{ github.event_name != 'pull_request' }}
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tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
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labels: ${{ steps.metadata.outputs.labels }}
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build-args: |
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CUDA_VERSION=${{ matrix.cuda_version }}
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CUDNN_VERSION=${{ matrix.cudnn_version }}
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CUDA=${{ matrix.cuda }}
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PYTHON_VERSION=${{ matrix.python_version }}
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PYTORCH_VERSION=${{ matrix.pytorch }}
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TORCH_CUDA_ARCH_LIST=${{ matrix.torch_cuda_arch_list }}
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build-base-uv:
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if: github.repository_owner == 'axolotl-ai-cloud'
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runs-on: ubuntu-latest-m
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strategy:
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fail-fast: false
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matrix:
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include:
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- cuda: "126"
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cuda_version: 12.6.3
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cudnn_version: ""
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python_version: "3.11"
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pytorch: 2.6.0
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torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
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dockerfile: "Dockerfile-uv-base"
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- cuda: "128"
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cuda_version: 12.8.1
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cudnn_version: ""
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python_version: "3.11"
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pytorch: 2.7.0
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torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
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dockerfile: "Dockerfile-uv-base"
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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- name: Docker metadata
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id: metadata
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uses: docker/metadata-action@v5
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with:
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images: |
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axolotlai/axolotl-base-uv
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- name: Login to Docker Hub
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uses: docker/login-action@v2
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with:
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username: ${{ secrets.DOCKERHUB_USERNAME }}
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password: ${{ secrets.DOCKERHUB_TOKEN }}
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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- name: Build
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uses: docker/build-push-action@v4
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with:
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context: .
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file: ./docker/${{ matrix.dockerfile }}
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push: ${{ github.event_name != 'pull_request' }}
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tags: ${{ steps.metadata.outputs.tags }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
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labels: ${{ steps.metadata.outputs.labels }}
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@@ -51,7 +51,7 @@ Features:
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- NVIDIA GPU (Ampere or newer for `bf16` and Flash Attention) or AMD GPU
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- Python 3.11
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- PyTorch ≥2.4.1
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- PyTorch ≥2.5.1
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### Installation
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@@ -24,9 +24,9 @@ df_template = template_env.get_template("Dockerfile.jinja")
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df_args = {
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"AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
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"AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
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"PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.4.1"),
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"BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu121-2.4.1"),
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"CUDA": os.environ.get("CUDA", "121"),
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"PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.5.1"),
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"BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu124-2.5.1"),
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"CUDA": os.environ.get("CUDA", "124"),
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"GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
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"GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
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"CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
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@@ -22,9 +22,9 @@ df_template = template_env.get_template("Dockerfile.jinja")
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df_args = {
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"AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
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"AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
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"PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.4.1"),
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"BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu121-2.4.1"),
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"CUDA": os.environ.get("CUDA", "121"),
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"PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.5.1"),
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"BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu124-2.5.1"),
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"CUDA": os.environ.get("CUDA", "124"),
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"GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
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"GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
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"NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
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36
docker/Dockerfile-uv-base
Normal file
36
docker/Dockerfile-uv-base
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@@ -0,0 +1,36 @@
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ARG CUDA_VERSION="12.6.3"
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ARG CUDNN_VERSION=""
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ARG UBUNTU_VERSION="22.04"
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ARG MAX_JOBS=4
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FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION AS base-builder
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ARG PYTHON_VERSION="3.11"
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ARG PYTORCH_VERSION="2.6.0"
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ARG CUDA="126"
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ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
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ENV PYTHON_VERSION=$PYTHON_VERSION
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ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST
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ENV UV_TORCH_BACKEND="cu${CUDA}"
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RUN apt-get update \
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&& apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev pkg-config curl && rm -rf /var/lib/apt/lists/* \
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&& git lfs install --skip-repo \
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&& curl -LsSf https://astral.sh/uv/install.sh | sh
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ENV PATH="/root/.local/bin:${PATH}"
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RUN uv python install ${PYTHON_VERSION}
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WORKDIR /workspace
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RUN uv venv --no-project --relocatable axolotl-venv
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ENV PATH="/workspace/axolotl-venv/bin:${PATH}"
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RUN uv pip install packaging setuptools wheel \
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&& uv pip install torch==${PYTORCH_VERSION} \
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&& uv pip install --no-build-isolation "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" \
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&& uv pip install "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main" \
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&& uv pip install awscli pydantic
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@@ -36,7 +36,6 @@ Tags examples:
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- `main-base-py3.11-cu126-2.7.0`
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- `main-base-py3.11-cu124-2.6.0`
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- `main-base-py3.11-cu124-2.5.1`
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- `main-base-py3.11-cu124-2.4.1`
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## Main
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@@ -77,12 +76,10 @@ Tags examples:
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- `main-py3.11-cu126-2.7.0`
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- `main-py3.11-cu124-2.6.0`
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- `main-py3.11-cu124-2.5.1`
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- `main-py3.11-cu124-2.4.1`
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- `main-latest`
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- `main-20250303-py3.11-cu124-2.6.0`
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- `main-20250303-py3.11-cu124-2.5.1`
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- `main-20250303-py3.11-cu124-2.4.1`
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- `0.7.1`
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- `0.9.2`
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## Cloud
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@@ -15,7 +15,7 @@ This guide covers all the ways you can install and set up Axolotl for your envir
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- NVIDIA GPU (Ampere architecture or newer for `bf16` and Flash Attention) or AMD GPU
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- Python ≥3.10
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- PyTorch ≥2.4.1
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- PyTorch ≥2.5.1
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## Installation Methods {#sec-installation-methods}
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@@ -41,6 +41,40 @@ installed) in order not to clobber it, and so that we set the correct version of
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dependencies that are specific to the PyTorch version or other installed
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co-dependencies.
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### uv Installation {#sec-uv}
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uv is a fast, reliable Python package installer and resolver built in Rust. It offers significant performance improvements over pip and provides better dependency resolution, making it an excellent choice for complex environments.
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Install uv if not already installed
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```{.bash}
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curl -LsSf https://astral.sh/uv/install.sh | sh
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source $HOME/.local/bin/env
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```
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Choose your CUDA version to use with PyTorch; e.g. `cu124`, `cu126`, `cu128`,
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then create the venv and activate
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```{.bash}
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export UV_TORCH_BACKEND=cu126
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uv venv --no-project --relocatable
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source .venv/bin/activate
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```
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Install PyTorch
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- PyTorch 2.6.0 recommended
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```{.bash}
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uv pip install packaging setuptools wheel
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uv pip install torch==2.6.0
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uv pip install awscli pydantic
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```
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Install axolotl from PyPi
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```{.bash}
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uv pip install --no-build-isolation axolotl[deepspeed,flash-attn]
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# optionally install with vLLM if you're using torch==2.6.0 and want to train w/ GRPO
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uv pip install --no-build-isolation axolotl[deepspeed,flash-attn,vllm]
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```
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### Edge/Development Build {#sec-edge-build}
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For the latest features between releases:
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@@ -11,7 +11,7 @@
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=@# @# #@= #@ =#@@@@#= +#@@= +#@@@@#= .##@@+ @@
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@@@@ @@@@@@@@@@@@@@@@
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Welcome to the axolotl cloud image! If the you've mounted a disk to /workspace and the axolotl directory ie empty, run the following commands:
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Welcome to the axolotl cloud image! If the you've mounted a disk to /workspace and the axolotl directory is empty, run the following commands:
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```
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cd /workspace
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