Run:ai Atlas is a compute orchestration platform that speeds up data science initiatives by pooling all available GPU resources and then dynamically allocating resources as you need them. One-click execution of experiments, no code changes required by the user, and most importantly, no more waiting around to access GPUs. Atlas automates provisioning of multiple GPU or fractions of GPU across teams, users, clusters and nodes, and IT gains control and visibility over the full AI infrastructure stack through comprehensive, easy-to-use dashboards.
• Meet the SLA of your AI/ML in production / inference with optimal speeds and better utilization of existing compute resources.
• Enables execution of AI/ML initiatives according to business priorities through defined policies.
• The Run:AI GUI gives IT leaders a holistic view of GPU infrastructure utilization, usage patterns, workload wait times, and costs.
• Enables flexible pooling and sharing of resources between users and teams.
• Converts spare capacity to speed by automatically distributing your model training tasks over multiple GPUs when they're available.
• Uses fractions of GPU to run multiple inferencing on the same GPU for cost savings.
• Automated scheduling based on set policies and user priority for consumption of GPU compute on-prem and in the cloud
• Run multiple workloads on the same hardware with dynamic resource allocation
• Simple integration via Kubernetes plug-in
• Build and run ML pipelines (Integrated with Kubeflow)
• One-click execution of experiments, no need for data scientists to code
