forked from open-webui/open-webui
75a40dead6
+Dockerfile-cuda I created this file to help add CUDA support to open-webui for access to a GPU during embedding operations.
81 lines
2.9 KiB
Text
81 lines
2.9 KiB
Text
# syntax=docker/dockerfile:1
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######## WebUI frontend ########
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FROM node:21-alpine3.19 as build
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WORKDIR /app
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COPY package.json package-lock.json ./
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RUN npm ci
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COPY . .
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RUN npm run build
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######## WebUI backend ########
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ARG CUDA_VERSION=12.3.2
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#FROM nvidia/cuda:$CUDA_VERSION-devel-ubuntu22.04 as base
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FROM --platform=linux/amd64 nvidia/cuda:$CUDA_VERSION-devel-ubuntu22.04 AS cuda-build-amd64
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# Set environment variables for NVIDIA Container Toolkit
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ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
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ENV NVIDIA_DRIVER_CAPABILITIES=all
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ENV NVIDIA_VISIBLE_DEVICES=all
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# Install NVIDIA CUDA toolkit and libraries in the container
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#RUN apt-get update && \
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# apt-get install -y --no-install-recommends nvidia-cuda-toolkit nvidia-cuda-dev nvidia-cudnn-dev
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ENV ENV=prod
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ENV PORT ""
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ENV OLLAMA_BASE_URL "/ollama"
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ENV OPENAI_API_BASE_URL ""
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ENV OPENAI_API_KEY ""
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ENV WEBUI_SECRET_KEY ""
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ENV SCARF_NO_ANALYTICS true
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ENV DO_NOT_TRACK true
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######## Preloaded models ########
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# whisper TTS Settings
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ENV WHISPER_MODEL="base"
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ENV WHISPER_MODEL_DIR="/app/backend/data/cache/whisper/models"
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# RAG Embedding Model Settings
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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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# Leaderboard: https://huggingface.co/spaces/mteb/leaderboard
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# for better performance and multilangauge support use "intfloat/multilingual-e5-large" (~2.5GB) or "intfloat/multilingual-e5-base" (~1.5GB)
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# IMPORTANT: If you change the default model (all-MiniLM-L6-v2) and vice versa, you aren't able to use RAG Chat with your previous documents loaded in the WebUI! You need to re-embed them.
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ENV RAG_EMBEDDING_MODEL="all-MiniLM-L6-v2"
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# device type for whisper tts and embedding models - "cpu" (default), "cuda" (NVIDIA GPU and CUDA required), or "mps" (apple silicon) - choosing this right can lead to better performance
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ENV RAG_EMBEDDING_MODEL_DEVICE_TYPE="cuda"
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ENV RAG_EMBEDDING_MODEL_DIR="/app/backend/data/cache/embedding/models"
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ENV SENTENCE_TRANSFORMERS_HOME $RAG_EMBEDDING_MODEL_DIR
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######## Preloaded models ########
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WORKDIR /app/backend
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# Install Python & dependencies in the container
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RUN apt-get update && \
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apt-get install -y --no-install-recommends python3.11 python3-pip ffmpeg libsm6 libxext6 pandoc netcat-openbsd && \
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rm -rf /var/lib/apt/lists/*
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COPY ./backend/requirements.txt ./requirements.txt
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RUN pip3 install torch torchvision torchaudio --no-cache-dir
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RUN pip3 install -r requirements.txt --no-cache-dir
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# copy embedding weight from build
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RUN mkdir -p /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2
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COPY --from=build /app/onnx /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2/onnx
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# copy built frontend files
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COPY --from=build /app/build /app/build
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COPY --from=build /app/CHANGELOG.md /app/CHANGELOG.md
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COPY --from=build /app/package.json /app/package.json
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# copy backend files
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COPY ./backend .
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CMD [ "bash", "start.sh"]
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