February 2026 monthly summary for ecmwf/anemoi-core. Primary focus was stabilizing the test suite and reducing flaky CI failures to accelerate development velocity and improve release confidence. Implemented a targeted bug fix to Triton test tolerance, increasing tolerance from 1e-5 to 1e-4 to align with FP16 precision and reduce nondeterministic test outcomes. This change improves developer productivity, CI reliability, and software quality without altering user-facing functionality. Commit linked: 81e913be973e9f84d787f1cbce6809898b848dbd (fix(training): raise triton tolerence), PR #870.

January 2026 monthly summary focusing on key business value and technical achievements across ecmwf/anemoi-core, ecmwf/anemoi-transform, and ecmwf/anemoi-datasets. Key features delivered, major bugs fixed, overall impact, and technologies demonstrated.

December 2025 highlights for ecmwf/anemoi-core: Key features delivered include Weights_only support in PyTorch Lightning 2.6+ and Triton integration enhancements (CPU fallback and non-power-of-2 support). Major bug fix: compiled Layer Norm pickling enabling ensemble tests to pass. Overall impact: improved training compatibility, reduced test failures, and broader hardware support across CPU/GPU; business value includes faster iteration and more reliable experiments. Technologies demonstrated: PyTorch Lightning 2.6+, Triton, padding/masking strategies, multi-GPU testing, and robust testing/documentation practices.

November 2025 monthly summary for ecmwf/anemoi-core: Delivered CPU-enabled transformer training path via Gloo all_to_all fallback, refactored to remove circular dependencies by switching to torch.nn.Module, expanded developer documentation on throughput/memory optimization, and stabilized training configurations with critical import-path fixes and PyTorch Lightning 2.6.0 compatibility updates. These changes enable CPU training scalability, improve CI reliability, and provide clear guidance on performance optimization while preserving GPU paths.

For 2025-09, CI reliability improvement for ecmwf/anemoi-core: extended GitHub Actions benchmark timeout to 360 minutes to prevent overnight test failures due to Slurm queue delays. No changes to Slurm timeout. Result: more stable nightly benchmarks and faster feedback.

March 2025 — Key outcomes focused on improving profiler reliability and usability for the ecmwf/anemoi-inference project. Implemented changes to prevent overwriting of previous profiling runs, enhanced user guidance via logs, and streamlined data handling by replacing the heavy memory timeline HTML with a lightweight memory pickle. Disabled saving PyTorch profiler stack traces to preserve trace file integrity. These changes reduce operational friction, improve data integrity, and accelerate performance troubleshooting across deployments. The work demonstrates strong observability, data governance, and tooling modernization, contributing to faster optimization cycles and more trustworthy performance measurements. Commit 6cfa021ec8cdfc9b18a5bc51a7937759e4c73e28 (fix: Update Profiler #160).

February 2025: Delivered cross-repo improvements across ecmwf/anemoi-core and ecmwf/anemoi-inference to enhance compatibility, reliability, and performance. Key outcomes include enabling Torch v2.6 graph loading, restoring PyTorch compatibility, and introducing parallel inference on a single node with multi-GPU. These changes reduce deployment risk, expand hardware utilization, and improve reliability in non-SLURM environments. Accompanying docs updates clarified usage for SLURM and non-SLURM modes.

January 2025 monthly summary for ecmwf/anemoi-core: Delivered distributed inference enhancements and improved observability for multi-GPU setups. Implemented optional model_comm_group parameter in AnemoiModelInterface.predict_step to enable distributed communication, updating the method signature, usage patterns, and changelog. Fixed the Model Summary profiler for models sharded across multiple GPUs, ensuring reliable profiler output and proper logging in distributed deployments. These changes advance scalable inference, reduce debugging effort, and support more predictable performance in production.

Month 2024-12 — ecmwf/anemoi-core: Focused on stability and reliability for the profiler. Delivered a robust fix for environment variable handling, ensuring safe operation when required vars are missing, which is common in HPC/batch environments.

November 2024 monthly summary: Implemented critical resource monitoring improvements, stabilized offline MLflow workflows, and achieved substantial memory efficiency in the prediction runner. Result: better observability, reliability, and capacity for larger workloads across core and inference components.