🐳 Docker Configuration: Set up PyTorch GPU Environment #11
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- Exclude unnecessary files and directories from Docker context - Prevent copying of development artifacts and cache files - Optimize build process and reduce image size
- Add Gradio certificate and cache directory exclusions - Ensure sensitive and temporary files are not tracked
- Set up PyTorch 2.2.1 with CUDA 12.1 support - Configure system dependencies and Python packages - Establish working directory and application deployment structure
- Modify launch configuration to allow remote access - Enable sharing capability for wider accessibility
- Configure service with NVIDIA GPU integration - Set up volume mounts for application code and model caching - Define port mapping and resource allocations - Enable GPU capabilities for ML operations
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📋 Overview
This PR introduces Docker configuration for the PyTorch application with GPU support, enabling containerized deployment and easier environment management.
🔄 Key Changes
🔧 Technical Details