February 2026: Focused on improving backward-tensor gathering control in the Anemoi core. Implemented an Autograd Backward Gather Control Refactor to decouple gather_in_bwd from the gather_tensor primitive, increasing modularity, testability, and control over gradient paths. This work enhances backward operation safety and provides a solid foundation for future enhancements across multi-GPU setups.

January 2026 monthly summary focusing on key accomplishments in ecmwf/anemoi-core. Delivered a robust Balanced Data Partitioning for Batch Distribution, extracting logic into a new module to improve maintainability and testability. Implemented a fix for training dataloader worker ranges to ensure all batches are distributed and no batches are dropped due to uneven division. Together, these changes enhance multi-GPU scaling, batch utilization, and overall training throughput. Key collaborators include co-authors on the commit (e.g., Ana Prieto Nemesio). This work underpins more predictable performance and scalable data-parallel training.

December 2025 monthly summary for ecmwf/anemoi-core: Delivered RolloutEval sharding optimization to enable efficient evaluation without cross-rank allgathers. Refactored RolloutEval to run on all ranks while keeping batches sharded, significantly improving evaluation scalability on multi-GPU setups. This change addresses and closes issue #689, with the fix tracked as #714 (commit 0fbc071b2092eefdce6643083d60eb989e8040b2). Result: higher throughput, reduced inter-process communication, and better resource utilization. Emphasized unit tests, dependency updates, documentation, and parallel testing guidelines to ensure maintainability. Technologies involved include distributed computing, multi-GPU workflows, and standard ML infrastructure tooling.

November 2025 focused on stabilizing distributed inference, simplifying the processor architecture for checkpoint flexibility, and accelerating graph-based models. Key outcomes include robust distributed predict_step behavior, dynamic layer chunking for cross-model checkpoint compatibility, and a custom Triton kernel that delivers substantial speedups and memory efficiency for GraphTransformer. The work improves reliability, scalability, and training/inference efficiency, delivering clear business value for large-scale deployments.

October 2025 monthly summary for ecmwf/anemoi-core: Delivered targeted improvements in the training pipeline and plotting reliability that enhance repeatability and observability across model configurations. Key outcomes include standardizing the shard_strategy for encoder and decoder components, and fixing a plotting crash related to nan_mask_weight handling in PlotLoss. These changes reduce configuration risk, stabilize training runs, and improve confidence in training metrics for faster, data-driven decision making.

September 2025: Focus on stability and correctness of grid shard handling in ecmwf/anemoi-core. Fixed a critical bug affecting grid shard shapes alignment by correcting the dimension indexing in _get_shard_shapes (from 0 to -2) and ensuring truncation logic works across uneven shards. The change improves reliability of simulations relying on shard-based grids and reduces downstream errors.

August 2025 monthly summary for the ecmwf/anemoi-core repository focused on stability and reliability improvements in the training data pipeline. Delivered a critical bug fix for LAM sharding that ensures correct data partitioning when keep_batch_sharded is true by renaming a method and propagating grid_shard_slice to relevant functions. No new features introduced this month; the primary emphasis was on reliability, correctness, and maintainability of the training data workflow.

July 2025 monthly performance summary for ecmwf/anemoi-core focused on memory efficiency and large-scale training optimizations. Delivered two major feature improvements that reduce memory usage, lower peak memory, and improve scalability for high-resolution workflows, enabling more productive experimentation with fewer resource-related interruptions. Implemented a memory-conscious refactor of training loss scaling and introduced graph-transformer optimizations with edge sharding and checkpointed mapper chunking to reduce communication overhead and peak memory during both training and inference.

June 2025 (2025-06) monthly review focused on scalable training infrastructure through end-to-end model pipeline sharding. Delivered a feature that shards the entire training pipeline (data loading to loss computation), enabling larger input grids by keeping input/output grids off GPU memory. This work establishes a foundation for multi-GPU scalability and improves resource efficiency, aligning with our roadmap for larger-model experiments.

Month: 2025-05 — Delivered scalable inference enhancements for ecmwf/anemoi-core by introducing chunking for GraphTransformerProcessor and Mapper, enabling large computations to be partitioned and processed in chunks. This feature is controlled via environment variables for fine-grained resource management, improving throughput and memory utilization for large workloads. Documentation and tests were updated to reflect the new behavior. Core change is backed by commit 1daa9f22ab36426602ab644de6a29ef5e296a485 (feat: GraphtransformerProcessor chunking).

January 2025 monthly summary for ecmwf/anemoi-core focused on performance and scalability improvements in preprocessing and data loading. Implemented two key features with targeted memory and I/O optimizations, backed by precise fixes to memory handling and load strategy to ensure stability with large datasets.

November 2024 performance and reliability update across the Anemoi platform. Implemented sharded data loading via reader groups to reduce CPU memory usage and boost dataloader throughput, refactoring the distributed training workflow to assemble full batches from shard data and adjusting GraphForecaster accordingly. Fixed critical data handling issues: metadata serialization for numpy integers to ensure cross-platform compatibility, and grid slicing for cutout operations to preserve spatial integrity. Updated configuration, documentation, and callbacks to support and guide the new sharding capability. Overall impact: improved scalability, data integrity, and processing efficiency for large-scale datasets, enabling more robust pipelines and faster iteration cycles.