Ray Description

You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.

Pricing

Pricing Starts At:
Free
Pricing Information:
Open source. Consumption-based.
Free Version:
Yes
Free Trial:
Yes

Integrations

API:
Yes, Ray has an API

Reviews

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Company Details

Company:
Anyscale
Year Founded:
2019
Headquarters:
United States
Website:
ray.io
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Product Details

Platforms
SaaS
Windows
Mac
Linux
On-Premises
Type of Training
Documentation
Live Online
Webinars
In Person
Videos
Customer Support
Online

Ray Features and Options

Deep Learning Software

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Machine Learning Software

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Ray User Reviews

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