Eliminate Your Deep Learning GPU Idle Time

Remove GPU scheduling and allocation from your to-do list, and focus on solving the next big challenge.

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Don’t let GPU resourcing occupy your routine

For too long Data Scientists found themselves needing to cope with shortage of AI resources and cumbersome work.

Run:ai Atlas helps Deep Learning teams focus on running models from Build, to Train, to Production, and worry less about resource allocation and provisioning.

Dynamic resource allocation and smart scheduling

Run:ai puts an end to the challenges of scheduling and securing GPU-time for Data Scientist by replacing manual work with sophisticated scheduling platforms.

GPU availability shortage solved

GPU fractioning, Virtualization, Over-Quota Management: these are the main features that assure Data Science teams can run experiments at scale and don’t have to wait for GPUs to become available for days.

Visibility into experiments management at scale

Run:ai offers a centralized dashboard giving Data Science and IT teams clear visibility into which experiments are running, queued, and prioritized.

Integration with AI practitioner ML tool-of-choice

Run:ai connects seamlessly with popular tools and IDEs such as Jupyter Notebook, PyCharm, Weights & Biases, ML Flow, etc.

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AI Infrastructure made simple

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.

Gain Visibility & Control

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.

Built for Cloud-Native

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.

Improve ROI >2x

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.