diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 379fbd7ad..9c90c77ac 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -26,8 +26,15 @@ jobs: strategy: matrix: config: - - { tag: "light", dockerfile: ".devops/main.Dockerfile" } - - { tag: "full", dockerfile: ".devops/full.Dockerfile" } + - { tag: "light", dockerfile: ".devops/main.Dockerfile", platforms: "linux/amd64,linux/arm64" } + - { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" } + # NOTE(canardletter): The CUDA builds on arm64 are very slow, so I + # have disabled them for now until the reason why + # is understood. + - { tag: "light-cuda", dockerfile: ".devops/main-cuda.Dockerfile", platforms: "linux/amd64" } + - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" } + - { tag: "light-rocm", dockerfile: ".devops/main-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } + - { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } steps: - name: Check out the repo uses: actions/checkout@v3 @@ -51,7 +58,7 @@ jobs: with: context: . push: true - platforms: linux/amd64,linux/arm64 + platforms: ${{ matrix.config.platforms }} tags: "ghcr.io/ggerganov/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}" file: ${{ matrix.config.dockerfile }} @@ -60,6 +67,6 @@ jobs: with: context: . push: ${{ github.event_name == 'push' }} - platforms: linux/amd64,linux/arm64 + platforms: ${{ matrix.config.platforms }} tags: "ghcr.io/ggerganov/llama.cpp:${{ matrix.config.tag }}" file: ${{ matrix.config.dockerfile }} diff --git a/README.md b/README.md index fe7391e01..b3845afd7 100644 --- a/README.md +++ b/README.md @@ -844,8 +844,17 @@ Place your desired model into the `~/llama.cpp/models/` directory and execute th #### Images We have two Docker images available for this project: -1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. -2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. +1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`) +2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`) + +Additionally, there the following images, similar to the above: + +- `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) + +The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the Gitlab Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now). #### Usage