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Efficient AI Model Training with PyTorch

AdvancedSkill Level
4.9+
37 reviews
Updated 06/2025
Learn how to reduce training times for large language models with Accelerator and Trainer for distributed training
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PythonArtificial Intelligence4 hours13 videos45 Exercises3,850 XPStatement of Accomplishment

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Course Description

Distributed training is an essential skill in large-scale machine learning, helping you to reduce the time required to train large language models with trillions of parameters. In this course, you will explore the tools, techniques, and strategies essential for efficient distributed training using PyTorch, Accelerator, and Trainer.

Preparing Data for Distributed Training

You'll begin by preparing data for distributed training by splitting datasets across multiple devices and deploying model copies to each device. You'll gain hands-on experience in preprocessing data for distributed environments, including images, audio, and text.

Exploring Efficiency Techniques

Once your data is ready, you'll explore ways to improve efficiency in training and optimizer use across multiple interfaces. You'll see how to address these challenges by improving memory usage, device communication, and computational efficiency with techniques like gradient accumulation, gradient checkpointing, local stochastic gradient descent, and mixed precision training. You'll understand the tradeoffs between different optimizers to help you decrease your model's memory footprint. By the end of this course, you'll be equipped with the knowledge and tools to build distributed AI-powered services.

Prerequisites

Intermediate Deep Learning with PyTorchWorking with Hugging Face
1

Data Preparation with Accelerator

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2

Distributed Training with Accelerator and Trainer

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3

Improving Training Efficiency

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4

Training with Efficient Optimizers

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Efficient AI Model Training with PyTorch
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*4.9
from 37 reviews
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  • Wendy
    6 days

  • Jinfeng
    16 days

  • Toby
    19 days

  • Nicholas
    21 days

  • Bach
    2 months

  • Marko
    2 months

Wendy

Jinfeng

Toby

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