##########
 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.
