Overview

Anemoi Training is a comprehensive framework designed for developing and training machine learning models for weather forecasting. It is part of the larger Anemoi ecosystem, which aims to provide a complete toolkit for data-driven weather prediction. This overview will introduce you to the key features and components of Anemoi Training, helping both users and developers understand its capabilities and structure.

Key Features

1. Flexible Model Architectures

Anemoi Training supports multiple model architectures, including:

  • Graph Neural Networks (GNNs)

  • Graph Transformers

  • Transformers with Flash Attention

This flexibility allows researchers and practitioners to experiment with different approaches and select the most suitable architecture for their specific forecasting tasks.

2. Configurable Training Pipeline

The framework uses a YAML-based configuration system, enabling users to adjust various aspects of the training process without modifying the underlying code. This includes:

  • Data preprocessing and normalization

  • Model hyperparameters

  • Training settings (e.g., learning rate, batch size)

  • Hardware utilization

3. Data Handling and Routing

Anemoi Training integrates seamlessly with the Anemoi Datasets module, providing efficient data loading and preprocessing capabilities. It offers:

  • Support for various meteorological variables

  • Customizable data routing for input/output variables

  • Multiple normalization strategies

4. Experiment Tracking

The framework includes built-in support for experiment tracking in existing tools like MlFlow, allowing users to:

  • Monitor training progress in real-time

  • Compare different runs and model configurations

  • Log metrics, hyperparameters, and model artifacts

Anemoi Training is compatible with popular tracking tools like MLflow, making it easier to manage and analyze your experiments.

5. Distributed Training

To accelerate model development and handle large-scale datasets, Anemoi Training supports distributed training across multiple GPUs and nodes. This feature enables:

  • Data parallelism for improved training speed

  • Efficient resource utilization on high-performance computing systems

6. Advanced Training Techniques

The framework incorporates several advanced training techniques to enhance model performance:

  • Rollout training for improved long-term forecasting

  • Customizable loss function scaling

  • Flexible learning rate scheduling

7. Debugging and Troubleshooting

Anemoi Training provides tools and configurations to help users identify and resolve issues during the training process, including:

  • Debug configurations for quick error identification

  • Guidance on isolating and addressing common problems

8. Benchmarking and HPC Profiling

Anemoi Training offers tools and configurations to support benchmarking and High-Performance Computing (HPC) profiling, allowing users to optimize training performance. This includes:

  • Benchmarking configurations for evaluating training efficiency across different hardware setups.

  • Profiling tools for monitoring resource utilization (CPU, GPU, memory) and identifying performance bottlenecks.

Components and Structure

Anemoi Training is organized into several key modules:

1. Data Module

Handles data loading, preprocessing, and routing. It interfaces with Anemoi Datasets to ensure efficient data management.

2. Training Module

Orchestrates the training process, including loss calculation, optimization, and learning rate scheduling.

3. Loss Module

Implements various loss functions and manages the model’s optimisation.

4. Diagnostics Module

Manages experiment tracking, metric logging, and visualization of training progress.

5. Strategy Module

Implements training strategies, including distributed training and advanced techniques.

Integration with Anemoi Ecosystem

Anemoi Training is designed to work seamlessly with other components of the Anemoi ecosystem:

  • Anemoi Datasets: Provides preprocessed data for training

  • Anemoi Graphs: Defines the structure for graph-based models

  • Anemoi Models: Offers pre-defined model architectures

  • Anemoi Registry: Stores and manages trained models

  • Anemoi Inference: Enables operational use of trained models

This integration ensures a smooth workflow from data preparation to model deployment in operational settings.

Getting Started

To begin using Anemoi Training, we recommend following the “Getting Started” guide, which will walk you through the installation process, basic configuration, and training your first model. As you become more familiar with the framework, you can explore the detailed user guide and module documentation to leverage its full capabilities.

Whether you’re a researcher exploring new machine learning approaches for weather forecasting or a practitioner looking to implement data-driven models in operational settings, Anemoi Training provides the tools and flexibility to support your work in advancing the field of meteorological prediction.