Course Code: horovod
Duration: 7 hours
Prerequisites:
  • 理解 Machine Learning,特别是深度学习
  • 熟悉机器学习库(TensorFlow、Keras、PyTorch、Apache MXNet)
  • Python 程式设计经验

观众

  • 开发人员
  • 数据科学家
Overview:

Horovod 是一个开源软体框架,旨在使用 TensorFlow、Keras、PyTorch 和 Apache MXNet 处理快速高效的分散式深度学习模型。它可以扩展单个 GPU 训练脚本,以便在多个 GPU 或主机上运行,只需进行最少的代码更改。

这种由讲师指导的即时培训(在线或现场)面向希望使用 Horovod 运行分散式深度学习训练并将其扩展为在多个 GPU 之间并行运行的开发人员或数据科学家。

在本次培训结束时,参与者将能够:

  • 设置必要的开发环境以开始运行深度学习训练。
  • 安装并配置 Horovod 以使用 TensorFlow、Keras、PyTorch 和 Apache MXNet 训练模型。
  • 使用 Horovod 扩展深度学习训练以在多个 GPU 上运行。

课程形式

  • 互动讲座和讨论。
  • 大量的练习和练习。
  • 在即时实验室环境中动手实施。

课程自定义选项

  • 本课程侧重于 Horovod,但可能需要其他软体工具和框架,如 TensorFlow、Keras、PyTorch 和 Apache MXNet。如果您有具体要求或偏好,请告诉我们。
  • 要申请本课程的定制培训,请联系我们进行安排。
Course Outline:

介绍

  • Horovod 功能和概念概述
  • 了解支援的框架

安装和设定 Horovod

  • 准备托管环境    
  • 为 TensorFlow、Keras、PyTorch 和 Apache MXNet 构建 Horovod
  • 正在执行 Horovod

运行分散式训练

  • 使用 TensorFlow 修改和运行训练范例
  • 使用 Keras 修改和运行训练范例
  • 使用 Py 修改和运行训练范例Torch
  • 使用 Apache MXNet 修改和运行训练范例

优化分散式训练过程

  • 在多个GPU    上运行并发操作
  • 优化超参数
  • 启用性能自动调整

故障排除

总结和结论

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