Ray

Ray

Anyscale
+
+

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About

Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have contributed to the popularity of deep learning by reducing the effort and skills needed to design, train, and use deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) provides a consistent way to run these deep-learning frameworks as a service on Kubernetes. The FfDL platform uses a microservices architecture to reduce coupling between components, keep each component simple and as stateless as possible, isolate component failures, and allow each component to be developed, tested, deployed, scaled, and upgraded independently. Leveraging the power of Kubernetes, FfDL provides a scalable, resilient, and fault-tolerant deep-learning framework. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes.

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.

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

Neural networks experts looking for a solution to run deep-learning frameworks as a service on Kubernetes

Audience

ML and AI Engineers, Software Developers

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

No information available.
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

IBM
Founded: 1911
United States
developer.ibm.com/open/projects/fabric-for-deep-learning-ffdl/

Company Information

Anyscale
Founded: 2019
United States
ray.io

Alternatives

Alternatives

Caffe

Caffe

BAIR
Vertex AI

Vertex AI

Google
AWS Neuron

AWS Neuron

Amazon Web Services

Categories

Categories

Integrations

Kubernetes
PyTorch
TensorFlow
Amazon EC2 Trn2 Instances
Amazon EKS
Anyscale
Apache Airflow
Azure Kubernetes Service (AKS)
Caffe
Dask
Databricks Data Intelligence Platform
Feast
Google Cloud Platform
Google Kubernetes Engine (GKE)
LanceDB
MLflow
Python
Torch
Union Cloud
io.net

Integrations

Kubernetes
PyTorch
TensorFlow
Amazon EC2 Trn2 Instances
Amazon EKS
Anyscale
Apache Airflow
Azure Kubernetes Service (AKS)
Caffe
Dask
Databricks Data Intelligence Platform
Feast
Google Cloud Platform
Google Kubernetes Engine (GKE)
LanceDB
MLflow
Python
Torch
Union Cloud
io.net
Claim Fabric for Deep Learning (FfDL) and update features and information
Claim Fabric for Deep Learning (FfDL) and update features and information
Claim Ray and update features and information
Claim Ray and update features and information