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Run:ai’s Atlas delivers simple, on-demand access to compute resources to AI Practitioners while giving Engineering teams peace- of-mind. Atlas assures GPU resources are constantly optimized, accessed, and utilized to their fullest on any infrastructure architecture and size.
Run:ai Atlas offers dashboards and analytics giving IT insight across all resources and workloads. Align resource allocation to business goals by setting policies and priorities across departments, projects or users.
Dynamically allocate resources using a Smart Scheduler built as a simple Kubernetes plug-in, built from the ground up to work with containers and cloud-native architectures.
Advanced technology for sharing and optimizing GPUs improves utilization by more than 2X, and significantly increases ROI on existing GPU and CPU infrastructure.
AI practitioners can easily consume resources in a self-service model using native Run:ai workflows to build, train and deploy models; or by using 3rd party integrations, such as MLflow, Kubeflow and more.
Build your AI infrastructure with cloud-like resource accessibility and management, on any infrastructure, and enable researchers to use any ML and DS tools they choose.
Run:AI’s platform builds off of powerful distributed computing and scheduling concepts from High Performance Computing (HPC), but is implemented as a simple Kubernetes plugin.
Our AI Cloud Platform speeds up data science workflows and creates visibility for IT teams who can now manage valuable resources more efficiently, and ultimately reduce idle GPU time.
Simplify management of GPU allocation and ensure sharing between users, teams, and projects according to business policies & goals.
Run:AI brings a cloud-like experience to resource management wherever your resources are: cloud, on-premises or hybrid cloud.
Super-powerful scheduling built as a simple Kubernetes plug-in, built from the ground up to work with containers and cloud-native architectures.
Make use of fractional GPUs, integer GPUs, and multiple-nodes of GPUs, for distributed training on Kubernetes.
Improves utilization by more than 2X, and significantly increases ROI on existing GPU and CPU infrastructure.
Run workloads on fractions of GPUs, converting spare capacity to speed and increasing infrastructure efficiency.