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Optimize PyTorch: Build and Accelerate Layers

本课程是 Deep Learning Engineering 专项课程的一部分

位教师：ansrsource instructors

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1个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

1个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

您将获得的技能

您将学习的工具

PyTorch (Machine Learning Library)

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积累特定领域的专业知识

本课程是 Deep Learning Engineering 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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该课程共有1个模块

Learn to build custom neural-network layers and accelerate model training with performance-driven PyTorch techniques. This hands-on, engineer-focused course teaches you how to design differentiable modules, diagnose bottlenecks, and apply optimizations like mixed precision and gradient accumulation to significantly boost training throughput.

单元详情

涵盖的内容

6个视频2篇阅读材料5个作业

6个视频总计31分钟

Why Custom Layers Matter in PyTorch5分钟
Tensor Operations & Autograd: How PyTorch Tracks Your Computations4分钟
Coding a Squeeze-and-Excite Layer in Pytorch3分钟
Profiling Your Training Loop with PyTorch 7分钟
Accelerating Training with FP16 and Gradient Accumulation9分钟
Congratulations and Continuous Learning Journey3分钟

2篇阅读材料总计18分钟

How PyTorch Tracks Your Computations10分钟
Diagnosing GPU Bottlenecks: Improving PyTorch Training Throughput 8分钟

5个作业总计79分钟

Graded Quiz: PyTorch Autograd, Custom Layers, and Training Performance20分钟
HOL: Design a Custom PyTorch Layer with Autograd in Mind20分钟
Practice Quiz: Core PyTorch & Deep Learning Concepts Check7分钟
HOL: Boost Training Throughput with Profiling, FP16, and Gradient Accumulation25分钟
Practice Quiz: Training Performance and Optimization Fundamentals 7分钟