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.