Trainer components#
Overview#
The Cerebras documentation on Trainer components provides in-depth coverage of various elements essential for model training.
The model directory section details the artifacts outputted by the Trainer and how to configure the directory for storing these artifacts.
The backend device section details how to configure hardware and other settings for running workflows.
The model section explains how to pass the main training and validation module to the Trainer.
The loop configuration guide covers the use of LoopCallback subclasses to manage training and validation cycles.
Numeric precision settings, including automatic mixed precision, are discussed to optimize performance.
The optimizer and scheduler sections guide users on implementing and configuring these components for effective model parameter updates.
Checkpointing explains how to save training progress, while the logging mechanism details logging metrics to various backends.
Reproducibility ensures consistent training results, and extending the Trainer with custom Callbacks provides flexibility.
The callback section explains how to extend the Cerebras Trainer class using Callback classes, allowing for customized behavior during model training.
Additionally, deferred weight initialization is covered to reduce time-to-first-loss, and performance flags offer options for setting debugging and performance parameters during training and validation.
These comprehensive components collectively enhance the robustness and efficiency of training workflows.