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课程大纲
介绍
- Horovod 功能和概念概述
- 了解支持的框架
安装和配置 Horovod
- 准备托管环境
- 为 TensorFlow、Keras、PyTorch 和 Apache MXNet 构建 Horovod
- 运行 Horovod
运行分布式训练
- 使用 TensorFlow 修改和运行训练示例
- 使用 Keras 修改和运行训练示例
- 使用 PyTorch 修改和运行训练示例
- 使用 Apache MXNet 修改和运行训练示例
优化分布式训练流程
- 在多个 GPU 上运行 并发操作
- 调整超参数
- 启用性能自动调整
故障 排除
总结和结论
要求
- 了解机器学习,特别是深度学习
- 熟悉机器学习库(TensorFlow、Keras、PyTorch、Apache MXNet)
- Python编程经验
观众
- 开发 人员
- 数据科学家
7 小时
客户评论 (4)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
课程 - Artificial Neural Networks, Machine Learning, Deep Thinking
The structure from first principles, to case studies, to application.
Margaret Webb - Department of Jobs, Regions, and Precincts
课程 - Introduction to Deep Learning
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
课程 - Advanced Deep Learning
examples based on our data