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

Provision a VM quickly with everything you need to get your deep learning project started on Google Cloud. Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility. You can launch Compute Engine instances pre-installed with TensorFlow, PyTorch, scikit-learn, and more. You can also easily add Cloud GPU and Cloud TPU support. Deep Learning VM Image supports the most popular and latest machine learning frameworks, like TensorFlow and PyTorch. To accelerate your model training and deployment, Deep Learning VM Images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers and the Intel® Math Kernel Library. Get started immediately with all the required frameworks, libraries, and drivers pre-installed and tested for compatibility. Deep Learning VM Image delivers a seamless notebook experience with integrated support for JupyterLab.

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

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

Developers and professionals seeking a solution to get preconfigured VMs for deep learning applications

Audience

ML and AI Engineers, Software Developers

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

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

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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

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

Google
Founded: 1998
United States
cloud.google.com/deep-learning-vm

Company Information

Anyscale
Founded: 2019
United States
ray.io

Alternatives

Alternatives

Alternatives

AWS Neuron

AWS Neuron

Amazon Web Services
Keepsake

Keepsake

Replicate

Categories

Categories

Categories

Integrations

PyTorch
TensorFlow
Amazon EKS
Amazon SageMaker
Azure Kubernetes Service (AKS)
Caffe
Databricks Data Intelligence Platform
Flyte
Google Cloud Platform
Google Cloud TPU
Google Compute Engine
Google Kubernetes Engine (GKE)
JupyterLab
Kubernetes
MLflow
NVIDIA DRIVE
Python
Snowflake
Torch
Union Cloud

Integrations

PyTorch
TensorFlow
Amazon EKS
Amazon SageMaker
Azure Kubernetes Service (AKS)
Caffe
Databricks Data Intelligence Platform
Flyte
Google Cloud Platform
Google Cloud TPU
Google Compute Engine
Google Kubernetes Engine (GKE)
JupyterLab
Kubernetes
MLflow
NVIDIA DRIVE
Python
Snowflake
Torch
Union Cloud

Integrations

PyTorch
TensorFlow
Amazon EKS
Amazon SageMaker
Azure Kubernetes Service (AKS)
Caffe
Databricks Data Intelligence Platform
Flyte
Google Cloud Platform
Google Cloud TPU
Google Compute Engine
Google Kubernetes Engine (GKE)
JupyterLab
Kubernetes
MLflow
NVIDIA DRIVE
Python
Snowflake
Torch
Union Cloud
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