v0.10.8
·
572 commits
to main
since this release
Added
- Docker Containerization: Complete Docker infrastructure for production deployments
- Full ML stack with PyTorch 2.5.1, MinkowskiEngine v0.5.4, torch-geometric, ROOT, and LArCV2
- Ubuntu 22.04 base with CUDA 12.1 toolkit (perfect version match with PyTorch)
- XRootD client with SciTokens support for dCache streaming with token authentication
- Multi-GPU architecture support: V100, A100, H100/H200, RTX 20xx/30xx/40xx (compute 7.0-9.0)
- Automated GitHub Actions workflow for container builds and publishing to GHCR
- Comprehensive documentation with Apptainer/Singularity usage examples
- Build script for local development and testing
Changed
- Dependencies: Removed torch-sparse dependency (no longer required)
- Documentation: Updated all Singularity references to Apptainer (current standard)
- Sphinx: Removed torch-sparse from autodoc mock imports
- Docker: Local Docker builds now force-refresh the base image with
--pull - Feature/shower energy by @francois-drielsma in #120
Fixed
- NumPy 2: Avoid coercing
EventSparseTensor3Dlists into NumPy arrays inSparse3DParser
Full Changelog: v0.10.6...v0.10.8
Contributors
francois-drielsma