Month: 2025-01 — Focus on enabling scalable, high-performance inference for the Anemoi model via distributed multi-process execution. Delivered a dedicated ParallelRunner with dynamic backend selection (nccl for CUDA, gloo otherwise), robust initialization of communication primitives, and environment-variable support for MASTER_ADDR/MASTER_PORT. Achievements include reproducibility via consistent seeding, compatibility with older models, and comprehensive documentation. This work lays the foundation for multi-GPU/nodes inference with improved throughput and reliability.

Concise monthly summary for 2024-11 focusing on key features delivered, major bugs fixed, overall impact, and technologies demonstrated. Highlights across ecmwf/anemoi-core and ecmwf/anemoi-inference: memory optimization, correctness fixes, and multi-GPU inference enabling scalable performance with robustness improvements. Business value emphasized: reduced memory footprint, higher throughput, and reliable distributed inference with simpler operation in production.