RayAnyscale
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NVIDIA Run:aiNVIDIA
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Related Products
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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.
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About
NVIDIA Run:ai is an enterprise platform designed to optimize AI workloads and orchestrate GPU resources efficiently. It dynamically allocates and manages GPU compute across hybrid, multi-cloud, and on-premises environments, maximizing utilization and scaling AI training and inference. The platform offers centralized AI infrastructure management, enabling seamless resource pooling and workload distribution. Built with an API-first approach, Run:ai integrates with major AI frameworks and machine learning tools to support flexible deployment anywhere. It also features a powerful policy engine for strategic resource governance, reducing manual intervention. With proven results like 10x GPU availability and 5x utilization, NVIDIA Run:ai accelerates AI development cycles and boosts ROI.
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
ML and AI Engineers, Software Developers
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Audience
Enterprises and AI teams seeking to optimize and scale GPU resources for AI training and inference across hybrid and multi-cloud environments
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Supported
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API
Offers API
Not Supported
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
Free
Open source. Consumption-based.
Free Version
Supported
Free Trial
Supported
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Supported
Live Online
Supported
In Person
Supported
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Company InformationAnyscale
Founded: 2019
United States
ray.io
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Company InformationNVIDIA
Founded: 1993
United States
www.nvidia.com/en-us/software/run-ai/
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Alternatives |
Alternatives |
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Categories |
Categories |
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Integrations
Amazon EC2 Trn2 Instances
Supported
Amazon EKS
Supported
Amazon SageMaker
Supported
Amazon Web Services (AWS)
Supported
Apache Airflow
Supported
Dask
Supported
Databricks
Supported
Feast
Supported
Flyte
Supported
Google Kubernetes Engine (GKE)
Supported
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Integrations
Amazon EC2 Trn2 Instances
Not Supported
Amazon EKS
Not Supported
Amazon SageMaker
Not Supported
Amazon Web Services (AWS)
Not Supported
Apache Airflow
Not Supported
Dask
Not Supported
Databricks
Not Supported
Feast
Not Supported
Flyte
Not Supported
Google Kubernetes Engine (GKE)
Not Supported
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