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Add USE_SLIM argument to Dockerfile
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1 changed files with 8 additions and 1 deletions
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@ -3,6 +3,7 @@
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# use build args in the docker build command with --build-arg="BUILDARG=true"
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ARG USE_CUDA=false
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ARG USE_OLLAMA=false
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ARG USE_SLIM=false
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# Tested with cu117 for CUDA 11 and cu121 for CUDA 12 (default)
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ARG USE_CUDA_VER=cu128
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# any sentence transformer model; models to use can be found at https://huggingface.co/models?library=sentence-transformers
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@ -43,6 +44,7 @@ FROM python:3.11-slim-bookworm AS base
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ARG USE_CUDA
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ARG USE_OLLAMA
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ARG USE_CUDA_VER
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ARG USE_SLIM
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ARG USE_EMBEDDING_MODEL
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ARG USE_RERANKING_MODEL
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ARG UID
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@ -54,6 +56,7 @@ ENV ENV=prod \
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# pass build args to the build
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USE_OLLAMA_DOCKER=${USE_OLLAMA} \
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USE_CUDA_DOCKER=${USE_CUDA} \
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USE_SLIM_DOCKER=${USE_SLIM} \
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USE_CUDA_DOCKER_VER=${USE_CUDA_VER} \
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USE_EMBEDDING_MODEL_DOCKER=${USE_EMBEDDING_MODEL} \
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USE_RERANKING_MODEL_DOCKER=${USE_RERANKING_MODEL}
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@ -120,6 +123,7 @@ RUN apt-get update && \
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COPY --chown=$UID:$GID ./backend/requirements.txt ./requirements.txt
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RUN pip3 install --no-cache-dir uv && \
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if [ "$USE_SLIM" != "true" ]; then \
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if [ "$USE_CUDA" = "true" ]; then \
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# If you use CUDA the whisper and embedding model will be downloaded on first use
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir && \
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@ -134,10 +138,13 @@ RUN pip3 install --no-cache-dir uv && \
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python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"; \
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python -c "import os; import tiktoken; tiktoken.get_encoding(os.environ['TIKTOKEN_ENCODING_NAME'])"; \
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fi; \
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else \
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uv pip install --system -r requirements.txt --no-cache-dir && \
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fi; \
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chown -R $UID:$GID /app/backend/data/
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# Install Ollama if requested
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RUN if [ "$USE_OLLAMA" = "true" ]; then \
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RUN if [ [ "$USE_OLLAMA" = "true" ] && [ "$USE_SLIM" != "true" ] ]; then \
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date +%s > /tmp/ollama_build_hash && \
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echo "Cache broken at timestamp: `cat /tmp/ollama_build_hash`" && \
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curl -fsSL https://ollama.com/install.sh | sh && \
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