50 lines
1.5 KiB
Docker
50 lines
1.5 KiB
Docker
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# New LiteLLM version has a dependency on madoka which requires g++ to build the wheel
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FROM python:3.13
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ARG GUARDRAILS_TOKEN
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ARG GUARDRAILS_TEMPLATE="guard-template.json"
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# Set environment variables to avoid writing .pyc files and to unbuffer Python output
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ENV PYTHONDONTWRITEBYTECODE=1
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ENV PYTHONUNBUFFERED=1
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ENV LOGLEVEL="DEBUG"
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ENV GUARDRAILS_LOG_LEVEL="DEBUG"
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ENV APP_ENVIRONMENT="production"
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ENV GUARDRAILS_TEMPLATE=$GUARDRAILS_TEMPLATE
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WORKDIR /app
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# Install Git and necessary dependencies
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RUN apt-get update && \
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apt-get install -y make git curl gcc jq pipx && \
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apt-get clean && \
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rm -rf /var/lib/apt/lists/*
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ENV PATH="/root/.local/bin:$PATH"
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# Copy the entrypoint script
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COPY /server_ci/fastapi-entry.sh /app/fastapi-entry.sh
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COPY ../ /app/guardrails
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# Install guardrails, the guardrails API, and gunicorn
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# openai optional. only used for integration testing
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RUN pip install "uvicorn[standard]" --no-cache-dir
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RUN pip install "/app/guardrails[api]"
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RUN guardrails configure --enable-metrics --enable-remote-inferencing --token $GUARDRAILS_TOKEN
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# bring in base template
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COPY /server_ci/$GUARDRAILS_TEMPLATE /app/$GUARDRAILS_TEMPLATE
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# Install Hub Deps and create config.py
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RUN guardrails create --template /app/$GUARDRAILS_TEMPLATE
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# RUN cp -r /usr/local/lib/python3.11/site-packages/guardrails/hub/* /app/guardrails/guardrails/hub
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# Expose port 8000 for the application
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EXPOSE 8000
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# Command to start the Gunicorn server with specified settings
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CMD ["/bin/bash", "/app/fastapi-entry.sh"]
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