Скачать или смотреть Distributed gradient descent exercise using a Horovod algorithm and PyTorch

Check out Carl Osipov's book 📖 Cloud Native Machine Learning | http://mng.bz/YrEj 📖 To save 40% off this book ⭐ DISCOUNT CODE: watchosipov40 ⭐ In this video, Carl provides an explanation of distributed gradient descent with an algorithm Horovod using multivariate linear regression in PyTorch and Python.

This video is an excerpt from a live coding session by Carl Osipov, the author of MLOps Engineering at Scale. Visit http://mng.bz/vXBq to watch the full video.

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"MLOps Engineering at Scale" is a guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers. You’ll start with best practices for your datasets, learn to bring VACUUM data-quality principles to your projects, and ensure that your datasets can be reproducibly sampled. Next, you’ll learn to implement machine learning models with PyTorch, discovering how to scale up your models in the cloud and how to use PyTorch Lightning for distributed ML training. Finally, you’ll tune and engineer your serverless machine learning pipeline for scalability, elasticity, and ease of monitoring with the built-in notification tools of your cloud platform. When you’re done, you’ll have the tools to easily bridge the gap between ML models and a fully functioning production system.