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.
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
Success with AI: A Benchmarking Model for AI Infrastructure Maturity
Essential Guide to GPU Machine Scheduling
Case Study: London Medical Imaging & AI Centre Speeds Up Research