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Allowed for app running on AWS to use smaller embedding model and not to load representation LLM (due to size restrictions).
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# First stage: build dependencies | |
FROM public.ecr.aws/docker/library/python:3.11.9-slim-bookworm | |
# Install Lambda web adapter in case you want to run with with an AWS Lamba function URL (not essential if not using Lambda) | |
#COPY --from=public.ecr.aws/awsguru/aws-lambda-adapter:0.8.3 /lambda-adapter /opt/extensions/lambda-adapter | |
# Install wget, curl, and build-essential | |
RUN apt-get update && apt-get install -y \ | |
wget \ | |
curl \ | |
build-essential | |
# Create a directory for the model | |
RUN mkdir /model && mkdir /model/rep && mkdir /model/embed | |
WORKDIR /src | |
COPY requirements.txt . | |
RUN pip install --no-cache-dir -r requirements.txt | |
# Gradio needs to be installed after due to conflict with spacy in requirements | |
RUN pip install --no-cache-dir gradio==4.41.0 | |
# Set up a new user named "user" with user ID 1000 | |
RUN useradd -m -u 1000 user | |
# Change ownership of /home/user directory | |
RUN chown -R user:user /home/user | |
# Make output folder | |
RUN mkdir -p /home/user/app/output && chown -R user:user /home/user/app/output | |
RUN mkdir -p /home/user/.cache/huggingface/hub && chown -R user:user /home/user/.cache/huggingface/hub | |
RUN mkdir -p /home/user/.cache/matplotlib && chown -R user:user /home/user/.cache/matplotlib | |
RUN mkdir -p /home/user/app/model/rep && chown -R user:user /home/user/app/model/rep | |
RUN mkdir -p /home/user/app/model/embed && chown -R user:user /home/user/app/model/embed | |
# Download the quantised phi model directly with curl. Changed at it is so big - not loaded | |
#RUN curl -L -o /home/user/app/model/rep/Phi-3.1-mini-128k-instruct-Q4_K_M.gguf https://huggingface.co/bartowski/Phi-3.1-mini-128k-instruct-GGUF/tree/main/Phi-3.1-mini-128k-instruct-Q4_K_M.gguf | |
# Download the Mixed bread embedding model during the build process - changed as it is too big for AWS. Not loaded. | |
#RUN curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | bash | |
#RUN apt-get install git-lfs -y | |
#RUN git lfs install | |
#RUN git clone https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1 /home/user/app/model/embed | |
#RUN rm -rf /home/user/app/model/embed/.git | |
# Download the BGE embedding model during the build process. Create a directory for the model and download specific files using huggingface_hub | |
COPY download_model.py /src/download_model.py | |
RUN python /src/download_model.py | |
# Switch to the "user" user | |
USER user | |
# Set home to the user's home directory | |
ENV HOME=/home/user \ | |
PATH=/home/user/.local/bin:$PATH \ | |
PYTHONPATH=$HOME/app \ | |
PYTHONUNBUFFERED=1 \ | |
PYTHONDONTWRITEBYTECODE=1 \ | |
GRADIO_ALLOW_FLAGGING=never \ | |
GRADIO_NUM_PORTS=1 \ | |
GRADIO_SERVER_NAME=0.0.0.0 \ | |
GRADIO_SERVER_PORT=7860 \ | |
GRADIO_THEME=huggingface \ | |
AWS_STS_REGIONAL_ENDPOINT=regional \ | |
GRADIO_OUTPUT_FOLDER='output/' \ | |
#GRADIO_ROOT_PATH=/data-text-search \ | |
SYSTEM=spaces | |
# Set the working directory to the user's home directory | |
WORKDIR $HOME/app | |
# Copy the current directory contents into the container at $HOME/app setting the owner to the user | |
COPY --chown=user . $HOME/app | |
CMD ["python", "app.py"] |