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.
Pool GPU Compute
Guaranteed Quotas
Automatic and dynamic provisioning of GPUs to break the limitations of static allocations
Elasticity
Kubernetes-based Scheduler
Easily orchestrate distributed training with batch scheduling, gang scheduling and topology awareness
Gradient Accumulation
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.
Success with AI: A Benchmarking Model for AI Infrastructure Maturity
Case Study: London Medical Imaging & AI Centre Speeds Up Research