February 2026 monthly summary for openxla/xla: Delivered a composite GitHub Action to benchmark HLO workloads with local and GCS path support, including TensorBoard conversion. Implemented context-aware checkout to reuse existing user_repo for internal CI or clone XLA sources for external callers. Updated json_to_tensorboard.py to strip unit suffixes from metric names, ensuring clean TensorBoard events and better compatibility with TB dashboards. This work enhances CI/CD automation, reproducibility of benchmarks, and observability for performance analysis under BAP workflows.

Monthly summary for 2025-11: Delivered standardization of ML container prerequisites for ROCm/tensorflow-upstream by implementing a manifest that lists all required CUDA 13.0 and cuDNN 9.15 packages to support building the public ML container image. This work is anchored by commit 85f1c0501140ae4972bfda048004d7f559c4fd75 with message 'Create ML public container image for CUDA 13.0/cuDNN 9.15'. The changes establish a repeatable, compatible environment, enabling faster container builds and reducing deployment issues across downstream ML workloads. No major bugs were fixed this month; the focus was on establishing a robust baseline for future improvements. Technologies demonstrated include containerization best practices, manifest-based configuration, CUDA/cuDNN version compatibility, and collaboration on ROCm/tensorflow-upstream.