Deep Learning (DL) orchestration

Run:AI Under the Hood

Build and Train Models with Unlimited Compute

Introducing Run:AI

The Run:AI software platform decouples data science workloads from the underlying hardware. By pooling resources and applying an advanced scheduling mechanism to data science workflows, Run:AI greatly increases the ability of data science teams to fully utilize all available resources, essentially creating unlimited compute. Data scientists can increase the number of experiments they run, speed time to results, and ultimately meet the business goals of their AI initiatives. IT gains control and visibility over the full AI infrastructure stack.

From Static Allocations to Guaranteed Quotas

Run:AI’s virtualization software builds off of powerful distributed computing and scheduling concepts from High Performance Computing (HPC), but is implemented as a simple Kubernetes plugin. The product speeds up data science workflows and creates visibility for IT teams who can now manage expensive resources more efficiently, and ultimately reduce idle GPU time.

Why Virtualize AI

Decouple data science from hardware

AI accelerators are typically deployed in data centers as bare metal and are allocated statically to data scientists. Working in this way frustrates the process of experimentation, slowing down, and holding back innovation. Managing bare metal creates infrastructure hassles for IT teams as management, visibility, maintenance of hardware, and other critical tasks are cumbersome. Run:AI solves these challenges with a new virtualization paradigm, tailored for AI infrastructure.

Speed Data Science Workflows

Never hit compute or memory bottlenecks again

Run:AI virtualization software gives data scientists the flexibility to automatically run as many compute intensive experiments as needed, eliminating static allocation limitations and the dependency on IT for provisioning resources. By simplifying data scientists’ workflows, Run:AI accelerates their productivity and the quality of their science.

Speed Deep Learning by Optimizing Compute

See how you can move AI models into production faster – simply by optimizing GPU resources with Run:AI.

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