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features
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support

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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.

Description

High-performance tasks associated with data-heavy AI, IoT, and HPC workloads have traditionally relied on costly, top-tier processors or accelerators like Graphics Processing Units (GPUs) to function optimally. Additionally, organizations utilizing cloud-based platforms for demanding computational tasks frequently encounter trade-offs that can be less than ideal. For instance, the outdated nature of processors and hardware in cloud infrastructures often fails to align with the latest software applications, while also raising concerns over excessive energy consumption and environmental implications. Furthermore, users often find certain features of cloud services to be cumbersome and challenging, which hampers their ability to create tailored cloud solutions that meet specific business requirements. This difficulty in achieving a perfect balance can lead to complications in identifying appropriate billing structures and obtaining adequate support for their unique needs. Ultimately, these issues highlight the pressing need for more adaptable and efficient cloud solutions in today's technology landscape.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon EC2 Trn2 Instances Yes 
Amazon EKS Yes 
Amazon SageMaker Yes 
Anyscale Yes 
Apache Airflow Yes 
Azure Kubernetes Service (AKS) Yes 
Dask Yes 
Databricks Yes 
Feast Yes 
Flyte Yes 
Google Cloud Platform Yes 
Google Kubernetes Engine (GKE) Yes 
Kubernetes Yes 
LanceDB Yes 
MLflow Yes 
PyTorch Yes 
Python Yes 
Snowflake Yes 
TensorFlow Yes 
Union Cloud Yes 

Integrations

Amazon EC2 Trn2 Instances No 
Amazon EKS No 
Amazon SageMaker No 
Anyscale No 
Apache Airflow No 
Azure Kubernetes Service (AKS) No 
Dask No 
Databricks No 
Feast No 
Flyte No 
Google Cloud Platform No 
Google Kubernetes Engine (GKE) No 
Kubernetes No 
LanceDB No 
MLflow No 
PyTorch No 
Python No 
Snowflake No 
TensorFlow No 
Union Cloud No 

Pricing Details

Free
Open source. Consumption-based.
Free Trial Yes 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) Yes 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Anyscale

Founded

2019

Country

United States

Website

ray.io

Vendor Details

Company Name

ScaleMatrix

Founded

2011

Country

United States

Website

www.scalematrix.com/scalecloud

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Product Features

Cloud Management

Access Control No 
Billing & Provisioning No 
Capacity Analytics No 
Cost Management No 
Demand Monitoring No 
Multi-Cloud Management No 
Performance Analytics No 
SLA Management No 
Supply Monitoring No 
Workflow Approval No 

HPC

Alternatives

Alternatives

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Azure HPC

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