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Update Dockerfile and requirements.txt
Browse files- Dockerfile +15 -36
- Dockerfile_old +48 -0
- requirements.txt +1 -1
Dockerfile
CHANGED
@@ -1,48 +1,27 @@
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# Use an official Python runtime as a parent image
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FROM python:3.11.1
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# Set up user and paths
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RUN useradd -m -u 1000 user
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USER user
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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# WORKDIR $HOME/app
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# COPY --chown=user . $HOME/app
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# Set the working directory in the container
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WORKDIR $HOME/app
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# Copy the current directory contents into the container at /usr/src/app
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# COPY . .
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COPY --chown=user . $HOME/app
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#
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USER root
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# Install
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RUN curl
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pip install --upgrade pip && \
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pip install -r requirements.txt
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# Make a port available to the world outside this container
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# The EXPOSE instruction informs Docker that the container listens on the specified network ports at runtime. Your container needs to listen to Streamlit’s (default) port 8501.
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EXPOSE 8501
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#
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#
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#
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#
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#
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# docker run -p 8501:8501 <image_name>
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# Use an official Python runtime as a parent image
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FROM python:3.11.1
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# Set the working directory in the container
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WORKDIR /usr/src/app
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# Set user to root
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USER root
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# Install Cargo (Rust's package manager)
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RUN curl https://sh.rustup.rs -sSf | sh -s -- -y
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ENV PATH="/root/.cargo/bin:${PATH}"
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# Copy the current directory contents into the container at /usr/src/app
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COPY . /usr/src/app
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# Install any needed packages specified in requirements.txt
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RUN pip install --no-cache-dir -r requirements.txt
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# Make port 80 available to the world outside this container
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EXPOSE 80
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# Define environment variable
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ENV NAME World
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# Run train_llm.py when the container launches
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CMD ["python", "./train_llm.py"]
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Dockerfile_old
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# Use an official Python runtime as a parent image
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FROM python:3.11.1
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# Set up user and paths
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RUN useradd -m -u 1000 user
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USER user
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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# WORKDIR $HOME/app
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# COPY --chown=user . $HOME/app
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# Set the working directory in the container
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# WORKDIR /app
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WORKDIR $HOME/app
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# Copy the current directory contents into the container at /usr/src/app
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# COPY . .
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COPY --chown=user . $HOME/app
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# Switch to root user
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USER root
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# Install Rust and Cargo, Reqirements, and Python dependencies
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RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y && \
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export PATH="/root/.cargo/bin:${PATH}" && \
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pip install --upgrade pip && \
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pip install -r requirements.txt
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# Make a port available to the world outside this container
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# The EXPOSE instruction informs Docker that the container listens on the specified network ports at runtime. Your container needs to listen to Streamlit’s (default) port 8501.
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EXPOSE 8501
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# The HEALTHCHECK instruction tells Docker how to test a container to check that it is still working. Your container needs to listen to Streamlit’s (default) port 8501:
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HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
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# Set the permissions for the script file
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# RUN chmod +x python 3 train_llm.py
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# Change permissions of the working directory
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# RUN chmod 777 /app # Gives all users read, write, and exec permissions in the app directory.
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# Run the command inside your image filesystem.
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CMD ["python", "train_llm.py"]
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# Execute with:
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# docker build -t <image_name> .
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# docker run -p 8501:8501 <image_name>
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requirements.txt
CHANGED
@@ -74,7 +74,7 @@ networkx==3.2.1
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nltk==3.8.1
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numpy==1.26.2
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oauthlib==3.2.2
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opencv-python==4.8.1.78
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opencv-python-headless==4.8.1.78
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optuna==3.3.0
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orjson==3.9.10
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nltk==3.8.1
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numpy==1.26.2
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oauthlib==3.2.2
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# opencv-python==4.8.1.78
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opencv-python-headless==4.8.1.78
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optuna==3.3.0
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orjson==3.9.10
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