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1. www.youtube.com › watchRay (2004) Official Trailer - Jamie Foxx, Kerry Washington Movie...

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   15 mag 2014 · Subscribe to CLASSIC TRAILERS: http://bit.ly/1u43jDeSubscribe to TRAILERS: http://bit.ly/sxaw6hSubscribe to COMING SOON: http://bit.ly/H2vZUnLike us on FACEB...

   • Durata video: 3 min
   • Visualizzazioni: 1,3M

   • Autore: Rotten Tomatoes Classic Trailers

2. www.youtube.com › watchRay - YouTube

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   Jamie Foxx stars as the one-of-a-kind innovator of soul, Ray Charles, who overcame impossible odds and humble beginnings to become an extraordinary music leg...

3. www.ray.ioProductionizing and scaling Python ML workloads simply | Ray

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   Powered by Ray. "One of the biggest problems that Ray helped us resolve is improving scalability, latency, and cost-efficiency of very large workloads. We were able to improve the scalability by an order of magnitude, reduce the latency by over 90%, and improve the cost efficiency by over 90%. It was financially infeasible for us to approach ...

4. docs.ray.io › en › latestInstalling Ray — Ray 2.31.0

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   Ray is a fast and scalable framework for distributed computing in Python. This webpage provides instructions on how to install Ray on different platforms and environments. You can also learn more about Ray's features and libraries, such as data processing, machine learning, and reinforcement learning, by exploring the related webpages.

5. docs.ray.io › en › latestStarting Ray — Ray 2.31.0

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   The Ray runtime can be started on a laptop, a single server, or multiple servers. There are three ways of starting the Ray runtime: Implicitly via ray.init() ( Starting Ray on a single machine) Explicitly via CLI ( Starting Ray via the CLI (ray start)) Explicitly via the cluster launcher ( Launching a Ray cluster (ray up)) In all cases, ray ...

6. docs.ray.io › en › latestRay Tune: Hyperparameter Tuning — Ray 2.31.0

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   Ray Tune: Hyperparameter Tuning. #. Tune is a Python library for experiment execution and hyperparameter tuning at any scale. You can tune your favorite machine learning framework ( PyTorch, XGBoost, TensorFlow and Keras, and more) by running state of the art algorithms such as Population Based Training (PBT) and HyperBand/ASHA .

7. docs.ray.io › en › latestGetting Started — Ray 2.31.0

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   Getting Started. #. Use Ray to scale applications on your laptop or the cloud. Choose the right guide for your task. Scale ML workloads: Ray Libraries Quickstart. Scale general Python applications: Ray Core Quickstart. Deploy to the cloud: Ray Clusters Quickstart. Debug and monitor applications: Debugging and Monitoring Quickstart.