.. _usage-getting-started:

Welcome to the Anemoi Training user guide. Anemoi Training is a
framework designed for training machine learning models for weather
forecasting. This guide will walk you through the key components and
processes involved in using Anemoi Training effectively.

Anemoi Training is built to be flexible and customizable, allowing you
to adjust various aspects of the model and training process without
modifying the underlying code. The framework supports different model
architectures, including Graph Neural Networks (GNNs), Graph
Transformers, and Transformers with Flash Attention.

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 User Journey
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As you progress through this guide, you'll learn how to:

#. **Configure Your Training**: Understand how to set up and customize
   your training pipeline using YAML-based configuration files.

#. **Configure Your Data Handling**: Learn about data routing,
   normalization strategies, and how to set up your dataset for
   training.

#. **Choose and Configure Models**: Explore the different model
   architectures available and how to select the best one for your
   needs.

#. **Execute and Monitor Experiments**: Get hands-on with running your
   training jobs and tracking your experiments using tools like MLflow.

#. **Optimize Performance**: Discover techniques for fine-tuning your
   model, including learning rate scheduling and loss function scaling.

#. **Debug and Troubleshoot**: Learn strategies for identifying and
   resolving issues that may arise during the training process.

#. **Scale Your Training**: Understand how to leverage distributed
   training to accelerate your model development.

Whether you're new to machine learning for weather forecasting or an
experienced practitioner, this guide will provide you with the knowledge
and tools to make the most of Anemoi Training.

Let's begin by training your first model in the next section.
