Cleanup

Dockerfile

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+# Use the base image with CUDA and PyTorch
+FROM kom4cr0/cuda11.7-pytorch1.13-mamba1.1.1:1.1.1
+
+# Install NVIDIA Container Toolkit (necessary for GPU support)
+RUN apt-get update && apt-get install -y \
+    nvidia-container-runtime \
+    python3 \
+    python3-pip \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install necessary dependencies and configure NVIDIA repository
+RUN apt-get update && apt-get install -y \
+    curl \
+    gnupg \
+    lsb-release \
+    sudo \
+    && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
+    && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
+        sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
+        tee /etc/apt/sources.list.d/nvidia-container-toolkit.list \
+    && sed -i -e '/experimental/ s/^#//g' /etc/apt/sources.list.d/nvidia-container-toolkit.list \
+    && apt-get update
+
+# Install NVIDIA Container Toolkit
+RUN apt-get install -y nvidia-container-toolkit
+
+# Set the environment variables for CUDA
+ENV PATH=/usr/local/cuda-11.7/bin:$PATH
+ENV LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64:$LD_LIBRARY_PATH
+
+# Set the runtime for GPU (requires NVIDIA runtime to be installed on the host machine)
+ENV NVIDIA_VISIBLE_DEVICES=all
+ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
+
+# Set working directory to /projects
+WORKDIR /project
+
+# Install necessary Python dependencies
+# Uncomment and modify the next lines as per your project requirements
+COPY requirements.txt requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Run your Python script
+CMD ["python3", "main.py"]