Efficient AI Model Training with PyTorch

Course overview

Focus on efficient model training in PyTorch: data and model preparation with Accelerator, distributed training using Trainer, efficiency techniques such as gradient accumulation and checkpointing, and practical use of AdamW optimizers.

Covers preparing models and datasets using AutoModel and Accelerator, automatic device placement, and preprocessing pipelines for image and audio training.

Details distributed training workflows: fine‑tuning with Trainer, setting evaluation metrics, specifying TrainingArguments and configuring the Trainer for different setups.

Presents efficiency techniques—gradient accumulation (with Accelerator and Trainer), gradient checkpointing and local SGD—and practical guidance on using AdamW and computing optimizer memory requirements.