maxpumperla/learning_ray
Notebooks for the O'Reilly book "Learning Ray"
This resource provides comprehensive guides and code examples to help you understand and apply Ray, a flexible distributed Python framework. It takes you from core concepts of distributed computing to building and deploying complex machine learning applications. If you're a machine learning engineer, data scientist, or researcher, you'll learn how to leverage Ray to handle large-scale data processing, model training, hyperparameter optimization, and model serving.
345 stars. No commits in the last 6 months.
Use this if you need to scale your Python-based machine learning workflows beyond a single machine and want to learn how to build distributed applications efficiently.
Not ideal if you are looking for a plug-and-play solution for a specific machine learning task without needing to understand the underlying distributed system.
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345
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89
Language
Jupyter Notebook
License
MIT
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Last pushed
Apr 25, 2024
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