Ray

Ray

Anyscale
+
+

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About

Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.

About

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

ML and AI Engineers, Software Developers

Audience

Engineers and data scientists requiring a solution to manage and improve their machine learning research

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Anyscale
Founded: 2019
United States
ray.io

Company Information

scikit-learn
United States
scikit-learn.org/stable/

Alternatives

Alternatives

Gensim

Gensim

Radim Řehůřek
ML.NET

ML.NET

Microsoft
MLlib

MLlib

Apache Software Foundation
Keepsake

Keepsake

Replicate

Categories

Categories

Integrations

Databricks
Python
Amazon EC2 Trn2 Instances
Amazon EKS
Apache Airflow
Dask
GLM-5.1
GLM-5.2
Google Cloud Platform
Keepsake
Kubernetes
LanceDB
MLJAR Studio
MLflow
Matplotlib
NumPy
TensorFlow
Thunder Compute
Train in Data
io.net

Integrations

Databricks
Python
Amazon EC2 Trn2 Instances
Amazon EKS
Apache Airflow
Dask
GLM-5.1
GLM-5.2
Google Cloud Platform
Keepsake
Kubernetes
LanceDB
MLJAR Studio
MLflow
Matplotlib
NumPy
TensorFlow
Thunder Compute
Train in Data
io.net
Claim Ray and update features and information
Claim Ray and update features and information
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