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

Number of participants



Price per participant

Testimonials (5)

Related Courses

Artificial Neural Networks, Machine Learning, Deep Thinking

21 Hours

Introduction to Deep Learning

21 Hours

Advanced Deep Learning

28 Hours

Deep Learning for Vision with Caffe

21 Hours

Deep Learning for Vision

21 Hours

Artificial Intelligence (AI) in Automotive

14 Hours

Machine Learning and Deep Learning

21 Hours

OpenNN: Implementing Neural Networks

14 Hours

OpenNMT: Setting Up a Neural Machine Translation System

7 Hours

Introduction Deep Learning & Réseaux de neurones pour l’ingénieur

21 Hours

PaddlePaddle

21 Hours

OpenFace: Creating Facial Recognition Systems

14 Hours

Advanced Machine Learning with Python

21 Hours

Advanced Machine Learning with R

21 Hours

Matlab for Deep Learning

14 Hours

Related Categories