Minimal Dockerfile for pre-built image, removed redundant build steps

Dockerfile

@@ -17,95 +17,4 @@
 # FROM nvcr.io/nvidia/pytorch:24.10-py3
 
 # This Dockerfile will simply pull your pre-built image and run it.
-FROM elungky/gen3c:latest
-
-# Install basic tools and clean apt cache
-RUN apt-get update && apt-get install -y git tree ffmpeg wget \
-    && apt-get clean \
-    && rm -rf /var/lib/apt/lists/*
-
-# Create symlinks for bash and libcuda
-RUN rm /bin/sh && ln -s /bin/bash /bin/sh && ln -s /lib64/libcuda.so.1 /lib64/libcuda.so
-
-# Set the working directory inside the container
-WORKDIR /app
-
-# Copy the cosmos-predict1.yaml and requirements.txt files to the container root
-COPY ./cosmos-predict1.yaml /cosmos-predict1.yaml
-COPY ./requirements.txt /requirements.txt
-
-# Copy all your project files (including gui/, cosmos_predict1/, etc.) into the /app directory.
-COPY . /app
-
-# Step 1: Install Miniconda
-# Install Miniconda to /opt/miniconda3 for consistency
-RUN echo "Installing Miniconda..." && \
-    mkdir -p /opt/miniconda3 && \
-    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /opt/miniconda3/miniconda.sh && \
-    bash /opt/miniconda3/miniconda.sh -b -u -p /opt/miniconda3 && \
-    rm /opt/miniconda3/miniconda.sh
-
-# Step 2: Configure Conda and set PATH
-# Add Miniconda to PATH for current and subsequent RUN commands
-ENV PATH="/opt/miniconda3/bin:$PATH"
-RUN echo "Configuring Conda..." && \
-    conda config --set auto_activate_base false && \
-    conda config --set auto_update_conda false && \
-    conda config --set show_channel_urls true && \
-    conda config --set channel_priority strict && \
-    conda config --set safety_checks disabled && \
-    conda config --add channels conda-forge && \
-    conda config --set always_yes true && \
-    conda config --set restore_free_channel true && \
-    conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main && \
-    conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
-
-# Step 3: Create the conda environment from the YAML file
-RUN echo "Creating conda environment from YAML..." && \
-    conda env create --file /cosmos-predict1.yaml
-
-# Step 4: Activate environment and install core pip requirements
-# Add the new environment's bin directory to PATH for subsequent `pip` calls
-ENV PATH="/opt/miniconda3/envs/cosmos-predict1/bin:$PATH"
-RUN echo "Installing core pip requirements from requirements.txt..." && \
-    pip install --no-cache-dir -r /requirements.txt
-
-# Step 5: Create necessary symlinks
-# Use CONDA_PREFIX if set, otherwise fallback to expected path
-RUN echo "Creating symlinks..." && \
-    ln -sf ${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1}/lib/python3.10/site-packages/nvidia/*/include/* ${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1}/include/ && \
-    ln -sf ${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1}/lib/python3.10/site-packages/nvidia/*/include/* ${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1}/include/python3.10 && \
-    ln -sf ${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1}/lib/python3.10/site-packages/triton/backends/nvidia/include/* ${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1}/include/
-
-# Step 6: Install specific pip packages
-RUN echo "Installing specific pip packages..." && \
-    pip install "fastapi[standard]" && \
-    pip install pyexr && \
-    pip install transformer-engine[pytorch]==1.12.0
-
-# Step 7: Clone and install Apex
-RUN echo "Cloning and installing Apex..." && \
-    git clone https://github.com/NVIDIA/apex /app/apex_temp && \
-    cd /app/apex_temp && \
-    CUDA_HOME=${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1} pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" . && \
-    rm -rf /app/apex_temp # Clean up cloned repo after install
-
-# Step 8: Install MoGe
-RUN echo "Installing MoGe..." && \
-    pip install git+https://github.com/microsoft/MoGe.git
-
-# Set environment variables for your application at runtime
-# Assumes your checkpoints are in /app/checkpoints after COPY . /app
-ENV GEN3C_CKPT_PATH="/app/checkpoints"
-# Specify GPU count if your app needs it (e.g., "ALL" for all available)
-ENV GEN3C_GPU_COUNT="ALL"
-# Standard path for CUDA_HOME in NVIDIA PyTorch Docker images
-ENV CUDA_HOME="/usr/local/cuda"
-
-# Expose the port your FastAPI server will listen on
-EXPOSE 7860
-
-# Default command: Activate the conda environment and then run the FastAPI server.
-# This ensures all your dependencies are available and the server starts correctly.
-# 'gui.api.server:app' is the correct path for your FastAPI application.
-CMD ["bash", "-c", "source /opt/miniconda3/bin/activate cosmos-predict1 && uvicorn gui.api.server:app --host 0.0.0.0 --port 7860"]
+FROM elungky/gen3c:latest