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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
Testimonials (5)
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
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
Very flexible.
Frank Ueltzhöffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
The structure from first principles, to case studies, to application.
Margaret Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to Deep Learning
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course - Advanced Deep Learning
examples based on our data