- Introduced a new memory management policy for ONNX Runtime to release temporary GPU memory after inference completion.
- Established a shared inference entry point for all ONNX models, differentiating between long-lived sessions and temporary memory arenas.
- Updated CUDA sessions to utilize a shrinkable memory arena, reclaiming GPU memory post-inference.
- Ensured consistent memory release semantics across BEN2, DA3, MoGe-2, and Upscale models while reusing existing sessions.
- Added lifecycle tests to validate temporary memory release and session reuse.
- Modified model adapters to integrate the new memory management features, ensuring backward compatibility with existing inference behavior.
- Enhanced unit tests to cover new memory release scenarios and verify correct behavior across different execution providers.
- Implement sync_models.py for preparing and uploading ONNX artifacts.
- Create build_node.py for building and validating Blender scripts.
- Add docs.py for building and previewing documentation with Mike or MkDocs.
- Introduce model_exports.py for exporting PyTorch models to ONNX format.
- Develop pack.py for packaging the project with dependencies for different platforms.
- Add test_sync_models.py to validate sync_models functionality.
- Add test_tool_commands.py to ensure repository tools have correct command scripts.
- Initialize tools package with __init__.py.
