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.