- 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
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。
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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