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Update Dockerfile
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‎Dockerfile‎

Lines changed: 69 additions & 43 deletions
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# ==============================================================================
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# Dockerfile for a LIGHTWEIGHT, professional, and optimized code-server environment
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# Dockerfile for a professionally architected, optimized code-server environment
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#
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# Architecture:
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# - Installation by 'root': The entire Python/Conda toolchain is installed
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# by the root user into a system-wide location (/opt/conda).
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# - Usage by 'coder': The non-root 'coder' user is configured to seamlessly
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# use this immutable, pre-built environment.
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#
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# Features:
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# - Base: code-server (latest)
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# - Python: Miniforge (Conda) with a pre-created Python 3.11 environment
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# - Package Manager: 'uv' (ultra-fast) and 'conda'
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# - Package Manager: 'uv' (installed via Conda for consistency)
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# - Root & Coder Access: 'python', 'conda', 'uv' are available for all users.
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# - Optimization (China):
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# - Timezone: Asia/Shanghai
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# - PyPI Mirror: Alibaba Cloud (configured via uv.toml - CORRECT FORMAT)
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# - PyPI Mirror: Alibaba Cloud (configured for the 'coder' user)
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# - Pre-installed Libraries: A minimal set (numpy, pandas, matplotlib)
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# - Convenience: Auto-activates conda environment in the terminal
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# - Convenience: Auto-activates conda environment for the 'coder' user.
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# ==============================================================================
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# Step 1: Start from the official code-server base image.
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FROM codercom/code-server:latest
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# --- Build Stage ---
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FROM codercom/code-server:latest as builder
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# Step 2: Set arguments for tool versions for easy updates.
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# Set arguments for tool versions.
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ARG MINIFORGE_VERSION=23.11.0-0
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ARG PYTHON_VERSION=3.11
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# Step 3: Define environment variables for paths and timezone.
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# Define global environment variables for paths and timezone.
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# This makes the toolchain available to ALL subsequent users (root and coder).
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ENV CONDA_DIR=/opt/conda
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ENV UV_DIR=/home/coder/.local
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ENV PATH=${CONDA_DIR}/bin:${UV_DIR}/bin:${PATH}
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ENV PATH=${CONDA_DIR}/bin:${PATH}
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ENV TZ=Asia/Shanghai
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# Step 4: Switch to the ROOT user for system-level installations.
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# --- Installation Phase (as root) ---
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USER root
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# Step 5: Install system dependencies, set timezone, and install Miniforge.
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RUN \
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# Update package lists and install necessary tools.
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# 1. Install system dependencies.
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apt-get update && apt-get install -y --no-install-recommends \
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wget \
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curl \
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git \
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build-essential \
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tzdata \
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# Set the timezone non-interactively.
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&& ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone \
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# Download and install Miniforge.
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\
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# 2. Install Miniforge (Conda).
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&& wget "https://github.com/conda-forge/miniforge/releases/download/${MINIFORGE_VERSION}/Miniforge3-${MINIFORGE_VERSION}-Linux-x86_64.sh" -O miniforge.sh \
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&& /bin/bash miniforge.sh -b -p ${CONDA_DIR} \
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&& rm miniforge.sh \
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# Give the 'coder' user ownership of the conda directory.
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&& chown -R coder:coder ${CONDA_DIR} \
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# Create symbolic links for 'conda' and 'python3' for easier root access (debugging).
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&& ln -s ${CONDA_DIR}/bin/conda /usr/local/bin/conda \
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&& ln -s ${CONDA_DIR}/bin/python /usr/local/bin/python3 \
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# Clean up apt cache to reduce image size.
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\
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# 3. Initialize Conda for the root's shell (useful for subsequent RUN commands).
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&& conda init bash && . /root/.bashrc \
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\
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# 4. Install 'uv' into the base conda environment for system-wide access.
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&& conda install -n base uv -c conda-forge -y \
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\
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# 5. Create the target Python environment.
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&& conda create -n py${PYTHON_VERSION} python=${PYTHON_VERSION} -y \
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\
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# 6. Install core libraries into the new environment using 'uv'.
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&& uv pip install --python=${CONDA_DIR}/envs/py${PYTHON_VERSION}/bin/python \
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numpy \
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pandas \
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matplotlib \
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\
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# 7. Ensure the 'coder' user home directory exists and has correct ownership.
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&& mkdir -p /home/coder && chown -R coder:coder /home/coder \
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\
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# 8. Clean up to reduce image size.
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&& apt-get clean \
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&& rm -rf /var/lib/apt/lists/*
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# Step 6: Switch back to the standard, non-root 'coder' user.
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# --- User Configuration Phase (as coder) ---
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USER coder
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# Step 7: As the 'coder' user, install 'uv', configure it, and create the environment.
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RUN \
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# Install 'uv' (the fast Python package manager).
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curl -LsSf https://astral.sh/uv/install.sh | sh && \
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\
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# Create the configuration directory for uv.
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# 1. Create the user-specific mirror configuration for 'uv'.
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mkdir -p ~/.config/uv && \
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\
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### --- [ THE REAL, FINAL FIX IS HERE ] --- ###
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# Create the uv.toml with the CORRECT format for a global user config.
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# It does NOT use the [tool.uv] section header.
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printf 'index-url = "https://mirrors.aliyun.com/pypi/simple"\n' > ~/.config/uv/uv.toml && \
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\
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# Create the default conda environment.
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conda create -n py${PYTHON_VERSION} python=${PYTHON_VERSION} -y && \
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\
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# Pre-install a minimal set of core data science libraries.
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# This command will now succeed as uv can correctly parse its config.
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uv pip install --python=${CONDA_DIR}/envs/py${PYTHON_VERSION}/bin/python \
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numpy \
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pandas \
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matplotlib && \
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\
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# Configure the shell to automatically activate this environment on login.
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# 2. Configure the user's shell to auto-activate the system-wide environment.
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conda init bash && \
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echo "conda activate py${PYTHON_VERSION}" >> ~/.bashrc
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# Step 8: The base image's entrypoint will start code-server.
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# --- Verification and Final Stage ---
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# This stage ensures both root and coder environments are correctly configured.
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FROM builder
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# Final check as ROOT.
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RUN echo "Verifying root environment..." && \
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python --version && \
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conda --version && \
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uv --version && \
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echo "Root environment check PASSED!"
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# Final check as CODER.
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USER coder
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RUN echo "Verifying coder environment..." && \
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# We must source .bashrc to load the 'conda activate' alias in a non-interactive shell.
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source ~/.bashrc && \
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python --version && \
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uv --version && \
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echo "Coder environment check PASSED!"
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# The base image's CMD is inherited automatically.
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# No need to specify it again.

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