Virtualization Software for AI Infrastructure

Gain visibility and control over AI workloads
to increase GPU utilization

Run:AI has built the world’s first virtualization layer for deep learning training models. By abstracting workloads from underlying infrastructure, Run:AI creates a shared pool of resources that can be dynamically provisioned, enabling full utilization of expensive GPU resources.

Take Control of Training Times and Costs

Easily define and set policies for consumption of GPU compute

Gain control over the allocation of expensive GPU resources. Run:AI’s scheduling mechanism enables IT to control, prioritize and align data science computing needs with business goals. Using Run:AI’s advanced monitoring tools, queueing mechanisms, and automatic preemption of jobs based on priorities, IT gains full control over GPU utilization.

Gain Visibility into GPU Consumption

Reduce blind spots created by static allocation of GPU

By creating a flexible ‘virtual pool’ of compute resources, IT leaders can visualize their full infrastructure capacity and utilization across sites, whether on premises or in the cloud. The Run:AI GUI greatly improves productivity by giving IT leaders a holistic view of GPU infrastructure utilization, usage patterns, workload wait times, and costs.

The Run:AI Platform

Our platform manages all the training requests within an organization

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Our software automatically analyzes each workload’s computational complexity and optimizes computations

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Run:AI automatically chooses and executes the distributed training strategy, and optimizes resource allocation

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Optimize Deep Learning Training

Greater utilization out of existing infrastructure

Run:AI optimizes utilization of AI clusters by enabling flexible pooling and sharing of resources between users and teams. The software distributes workloads in an ‘elastic’ way – dynamically changing the number of resources allocated to a job – allowing data science teams to run more experiments on the same hardware.

Run Data Experiments at Maximum Speed

Faster time to achieving business goals

Provide data scientists with optimal speeds for training AI models by getting better utilization out of existing compute resources. By abstracting AI workloads from compute power, and then applying distributed computing principles (essentially allowing a guaranteed quota of GPUs for each project) enterprises see faster results from DL modeling.

Features

Run on-premises or in the cloud

Policy-based automated scheduling

Optimize utilization of costly resources

Elastic virtual pools of GPU

Full control, visibility and prioritization

No code changes required by the user

1-click execution of experiments

Kubernetes plug-in simple integration

Integrated with

Technology Partners

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