mirror of
https://github.com/dogkeeper886/ollama37.git
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1e99c1bb50
- Add OLLAMA_VERSION build arg to Dockerfiles - Update Makefile to pass version via --build-arg - Add .env.example as local development reference - Update build.yml to use cicd-1 environment for vars.OLLAMA_VERSION Fixes #8 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
73 lines
3.1 KiB
Docker
73 lines
3.1 KiB
Docker
# Ollama37 Runtime Image
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# Single-stage build: compiles and packages the binary in one image
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# The runtime needs access to the build directory for GGML CUDA libraries
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# This ensures the compiled binary can find all required runtime libraries at:
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# /usr/local/src/ollama37/build/lib/ollama
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# Base image: ollama37-builder contains GCC 10, CUDA 11.4, and build tools
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FROM ollama37-builder
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# Clone ollama37 source code from GitHub
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RUN cd /usr/local/src\
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&& git clone https://github.com/dogkeeper886/ollama37.git
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# Set working directory for build
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WORKDIR /usr/local/src/ollama37
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# Configure build with CMake
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# Use "CUDA 11" preset for Tesla K80 compute capability 3.7 support
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# Set LD_LIBRARY_PATH during build so CMake can locate GCC 10 runtime libraries
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# and properly link against them (required for C++ standard library and atomics)
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RUN bash -c 'LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64:/usr/lib64:$LD_LIBRARY_PATH \
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CC=/usr/local/bin/gcc CXX=/usr/local/bin/g++ \
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cmake --preset "CUDA 11"'
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# Build C/C++/CUDA libraries with CMake
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# Compile all GGML CUDA kernels and Ollama native libraries
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# Use all available CPU cores (-j) for parallel compilation to speed up build
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RUN bash -c 'LD_LIBRARY_PATH=/usr/local/lib:/usr/local/lib64:/usr/lib64:$LD_LIBRARY_PATH \
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CC=/usr/local/bin/gcc CXX=/usr/local/bin/g++ \
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cmake --build build -j$(nproc)'
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# Build Go binary
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# VCS info is embedded automatically since we cloned from git
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# Build to source directory so binary can find libraries via relative path
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# Set version via -ldflags - pass OLLAMA_VERSION as build arg
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ARG OLLAMA_VERSION
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RUN go build -ldflags "-X github.com/ollama/ollama/version.Version=${OLLAMA_VERSION}" -o ./ollama .
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# Create symlink to standard binary location
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# The code in ml/path.go uses filepath.EvalSymlinks() which resolves this symlink
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# to /usr/local/src/ollama37/ollama, allowing it to find libraries at build/lib/ollama
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RUN ln -s /usr/local/src/ollama37/ollama /usr/local/bin/ollama
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# Setup library paths for runtime
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# The binary expects libraries in these exact paths:
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# /usr/local/src/ollama37/build/lib/ollama - Ollama CUDA/GGML libraries
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# /usr/local/lib64 - GCC 10 runtime libraries (libstdc++, libgcc_s)
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# /usr/local/cuda-11.4/lib64 - CUDA 11.4 runtime libraries
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# /usr/lib64 - System libraries
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ENV LD_LIBRARY_PATH=/usr/local/src/ollama37/build/lib/ollama:/usr/local/lib64:/usr/local/cuda-11.4/lib64:/usr/lib64
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# Configure Ollama server to listen on all interfaces
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ENV OLLAMA_HOST=0.0.0.0:11434
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# Expose Ollama API port
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EXPOSE 11434
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# Create persistent volume for model storage
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# Models downloaded by Ollama will be stored here
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RUN mkdir -p /root/.ollama
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VOLUME ["/root/.ollama"]
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# Configure health check to verify Ollama is running
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# Uses 'ollama list' command to check if the service is responsive
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# This validates both API availability and model registry access
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HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
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CMD /usr/local/bin/ollama list || exit 1
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# Set entrypoint and default command
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# Container runs 'ollama serve' by default to start the API server
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ENTRYPOINT ["/usr/local/bin/ollama"]
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CMD ["serve"]
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