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 organization1 of 3
Our software automatically analyzes each workload’s computational complexity and optimizes computations2 of 3
Run:AI automatically chooses and executes the distributed training strategy, and optimizes resource allocation3 of 3
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.