NEOPOLIS AKADEMY
Efficient AI Model Training with PyTorch
Master efficient model training in PyTorch: data handling, optimized training loops, hyperparameter tuning, parallelism and best practices to speed convergence and lower compute cost.
View this course on Akademy ↗Enrolment and practical details are available on Neopolis Akademy.
What you will explore
The course covers the PyTorch training pipeline: data pipelines, dataloaders, augmentation strategies and efficient batching.
It addresses optimizers, learning rate schedules, checkpointing and acceleration techniques (FP16, gradient accumulation, distributed training).
Methods for monitoring training, diagnosing overfitting and ensuring experiment reproducibility are also presented.
STEP BY STEP
Course programme
01Efficient AI Model Training with PyTorch
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.
Explore this module on Akademy ↗Programme source: Neopolis Akademy. Original course page