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
Testimonials (8)
examples based on our data
Witold - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)
code examples:-)
Marcin - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)
The structure from first principles, to case studies, to application.
Margaret Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to Deep Learning
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
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course - Advanced Deep Learning
Very flexible.
Frank Ueltzhöffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
Doing exercises on real examples using Eras. Italy totally understood our expectations about this training.
Paul Kassis
Course - Advanced Deep Learning
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.