Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • Gemini Enterprise Agent Platform Reviews & Ratings
    4.3 out of 5 stars 1,178 Ratings
    Company Website for Gemini Enterprise Agent Platform
  • Runpod Reviews & Ratings
    4.2 out of 5 stars 230 Ratings
    Company Website for Runpod
  • Anaconda Reviews & Ratings
    4.4 out of 5 stars 626 Ratings
    Company Website for Anaconda
  • IONOS Cloud GPU Servers Reviews & Ratings
    4.6 out of 5 stars 45,816 Ratings
    Company Website for IONOS Cloud GPU Servers
  • Fraud.net Reviews & Ratings
    4.7 out of 5 stars 57 Ratings
    Company Website for Fraud.net
  • Google Compute Engine Reviews & Ratings
    4.4 out of 5 stars 1,169 Ratings
    Company Website for Google Compute Engine
  • Compute with Hivenet Reviews & Ratings
    5.0 out of 5 stars 4 Ratings
    Company Website for Compute with Hivenet
  • Qloo Reviews & Ratings
    5.0 out of 5 stars 23 Ratings
    Company Website for Qloo
  • Overmonitor Reviews & Ratings
    4.5 out of 5 stars 8 Ratings
    Company Website for Overmonitor
  • Bacula Enterprise Reviews & Ratings
    4.5 out of 5 stars 125 Ratings
    Company Website for Bacula Enterprise

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.

What is Determined AI?

Determined allows you to participate in distributed training without altering your model code, as it effectively handles the setup of machines, networking, data loading, and fault tolerance. Our open-source deep learning platform dramatically cuts training durations down to hours or even minutes, in stark contrast to the previous days or weeks it typically took. The necessity for exhausting tasks, such as manual hyperparameter tuning, rerunning failed jobs, and stressing over hardware resources, is now a thing of the past. Our sophisticated distributed training solution not only exceeds industry standards but also necessitates no modifications to your existing code, integrating smoothly with our state-of-the-art training platform. Moreover, Determined incorporates built-in experiment tracking and visualization features that automatically record metrics, ensuring that your machine learning projects are reproducible and enhancing collaboration among team members. This capability allows researchers to build on one another's efforts, promoting innovation in their fields while alleviating the pressure of managing errors and infrastructure. By streamlining these processes, teams can dedicate their energy to what truly matters—developing and enhancing their models while achieving greater efficiency and productivity. In this environment, creativity thrives as researchers are liberated from mundane tasks and can focus on advancing their work.

Media

Media

Integrations Supported

Amazon SageMaker
Amazon Web Services (AWS)
Apache Airflow
Google Cloud Platform
MLflow
TensorFlow
Azure Kubernetes Service (AKS)
Databricks
Feast
Flyte
Google Kubernetes Engine (GKE)
Kubernetes
LanceDB
PyTorch
Python
Snowflake
Union Cloud

Integrations Supported

Amazon SageMaker
Amazon Web Services (AWS)
Apache Airflow
Google Cloud Platform
MLflow
TensorFlow
Amazon S3
Apache Spark
Pachyderm

API Availability

Has API

API Availability

Pricing Information

Free
Open source. Consumption-based.
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Anyscale

Date Founded

2019

Company Location

United States

Company Website

ray.io

Company Facts

Organization Name

Determined AI

Company Location

United States

Company Website

www.determined.ai/

Categories and Features

Deep Learning

Not specified

Machine Learning

Not specified

ML Model Deployment

Not specified

Categories and Features

AI Development

Not specified

Deep Learning

Not specified

Popular Alternatives

Popular Alternatives

Gemini Enterprise Agent Platform Reviews & Ratings

Gemini Enterprise Agent Platform

Google
4.3 out of 5 stars
TensorFlow Reviews & Ratings

TensorFlow

5.0 out of 5 stars
Ray Reviews & Ratings

Ray

Anyscale