What is Ray?
You can start developing on your laptop and then effortlessly scale your Python code across numerous GPUs in the cloud. Ray transforms conventional Python concepts into a distributed framework, allowing for the straightforward parallelization of serial applications with minimal code modifications. With a robust ecosystem of distributed libraries, you can efficiently manage compute-intensive machine learning tasks, including model serving, deep learning, and hyperparameter optimization. Scaling existing workloads is straightforward, as demonstrated by how Pytorch can be easily integrated with Ray. Utilizing Ray Tune and Ray Serve, which are built-in Ray libraries, simplifies the process of scaling even the most intricate machine learning tasks, such as hyperparameter tuning, training deep learning models, and implementing reinforcement learning. You can initiate distributed hyperparameter tuning with just ten lines of code, making it accessible even for newcomers. While creating distributed applications can be challenging, Ray excels in the realm of distributed execution, providing the tools and support necessary to streamline this complex process. Thus, developers can focus more on innovation and less on infrastructure.
Pricing
Price Starts At:
Free
Price Overview:
Open source. Consumption-based.
Free Version:
Free Version available.
Free Trial Offered?:
Yes
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Screenshots and Video
Company Facts
Company Name:
Anyscale
Date Founded:
2019
Company Location:
United States
Company Website:
ray.io
Product Details
Deployment
SaaS
Windows
Mac
Linux
On-Prem
Training Options
Documentation Hub
Online Training
Webinars
On-Site Training
Video Library
Support
Web-Based Support
Product Details
Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Startup
Supported Languages
English