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Course Outline
Introduction
- Overview of Horovod features and concepts
- Understanding the supported frameworks
Installing and Configuring Horovod
- Preparing the hosting environment
- Building Horovod for TensorFlow, Keras, PyTorch, and Apache MXNet
- Running Horovod
Running Distributed Training
- Modifying and running training examples with TensorFlow
- Modifying and running training examples with Keras
- Modifying and running training examples with PyTorch
- Modifying and running training examples with Apache MXNet
Optimizing Distributed Training Processes
- Running concurrent operations on multiple GPUs
- Tuning hyperparameters
- Enabling performance autotuning
Troubleshooting
Summary and Conclusion
Requirements
- An understanding of Machine Learning, specifically deep learning
- Familiarity with machine learning libraries (TensorFlow, Keras, PyTorch, Apache MXNet)
- Python programming experience
Audience
- Developers
- Data scientists
7 Hours