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Explore Run:ai's Self-Guided Tours

Run:ai and Carahsoft have partnered together to provide a series of self-guided tours of Run:ai's products and features. Similar to a live demo, the self-guided tours explores how Run:ai's products and features applies to a specific technology vertical such as Artificial Intelligence.

 

Learn about Run:ai's benefits, watch a short pre-recorded demo video, and download related resources. If interested in furthering the conversation, you can also schedule a live demo with a Run:ai expert from Carahsoft. Start a Self-Guided Tour now by selecting one below: 

 

View All Artificial Intelligence Tours
Run:ai Artificial Intelligence Self-Guided Tour  

Run:ai Artificial Intelligence Self-Guided Tour

Run:ai’s cloud-native orchestration platform speeds companies into the AI Era. The Run:ai platform gives data scientists access to all the pooled compute power they need to accelerate AI development and deployment – whether on-premises or in the cloud. IT and MLOps gain real-time visibility and control over scheduling and provisioning of GPUs, and ultimately see more than 2X gains in utilization of existing AI infrastructure. Run:ai helps Machine Learning developer teams connect and integrate better with their complex AI infrastructures. By utilizing Run:ai’s AI Cluster Management, AL Workload Scheduler, and GPU Resource Optimization tools, ML teams can have massively improved control and visibility from their AI resources with the speed and efficiency they desire.


Want to dive further into Run:ai's analytical tools and features?
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GPU Optimization

Run:AI excels in optimizing GPU (Graphics Processing Unit) performance through a platform designed to allocate and oversee GPU resources within computing environments. It aims to enhance GPU utilization, making costly GPU resources more effective for AI and deep learning workloads. By concentrating on intelligent resource allocation, job scheduling, multi-tenancy, dynamic scaling and monitoring, Run:AI ensures the efficient utilization of valuable GPU resources specifically for AI and deep learning tasks.

Benefits:

  • Run:AI's Dynamic MIG and GPU Fractioning functionalities enable automatic splitting or merging of GPU resources, facilitating their sharing among users and tasks. This ensures optimal utilization of compute memory, maximizing its usage efficiency.
  • The Quota Management feature from Run:AI empowers administrators to establish a maximum resource size for each team. This approach ensures flexible access without causing disruptions to other teams' operations.
  • The Node Pools feature enables the creation of distinct compute sets tailored for each team and workload. This capability ensures optimal utilization of even the most diverse clusters, running various GPUs like T4s and H100s, at their peak efficiency.
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Cluster Management

Run:AI supports cluster management by offering tools and functionalities that simplify the allocation and enhancement of resources within computing clusters, specifically targeting GPU resources utilized for AI and deep learning tasks. Through its emphasis on resource efficiency, job scheduling, multi-tenancy, dynamic scaling and monitoring, Run:AI aids in optimizing cluster management, particularly for AI and deep learning workloads that heavily rely on GPUs.

Benefits:

  • Run:AI furnishes MLOps Managers and Admins with a single, comprehensive Dashboard consolidating all compute usage data, encompassing team statistics, job details and additional insights.
  • Put an end to the chaos surrounding compute access. Utilize Run:AI's Permissions and Roles functionality to establish roles and access levels for compute resources, teams and resources. This feature seamlessly syncs with your organization's Single Sign-On (SSO) and LDAP services.
  • The Detailed Audit Log feature ensures thorough monitoring of every action and job within your cluster, providing peace of mind that no activity goes unnoticed or overlooked.
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AI/ML Workflow Management

Run:AI supports AI workflow management through a specialized platform aimed at enhancing and simplifying the execution of AI and deep learning workflows. By prioritizing efficient resource allocation, job scheduling, multi-tenancy support, workflow orchestration and monitoring, Run:AI enhances the management of AI workflows, especially concerning GPU-intensive AI and deep learning tasks.

Benefits:

  • Data Scientists often have varying preferences when it comes to experiment management and training tools. Run:AI offers a range of pre-built integrations to cater to these diverse preferences.
  • Run:AI Workspaces simplifies the creation and configuration of a new environment—just a few clicks and you're ready to start.
  • Templates enable your teams to quickly initiate work using a predefined combination of Compute, Data Source and Image, all provisioned within seconds.

Run:ai's Benefits Snapshot:

 

  • Can run on any infrastructure – on-premises, edge or cloud
  • Automated and streamlined GPU scheduling
  • Run:ai is a NVIDIA Preferred Partner - Get the most out of your NVIDIA AI Investment

View Run:ai's AI Resources

Run:ai MLOComputer Platform powered by NVIDIA DGX Systems RUN:AI'S AI RESOURCE
To Maximize efficient utilization and ROI of AI infrastructure, Run:ai and NVIDIA offer the Run:ai MLOps Computer Platform (MCP) integrated joint solution. It includes world-class AI infrastructure with NVIDIA DGX systems, along with complete control and visibility of all compute resources with Run:ai Atlas in an easy-to-use solution.

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Run:ai Certified to Run NVIDIA AI Enterprise Software Suite RUN:AI'S AI RESOURCE
Run:ai, the leader in compute orchestration for AI workloads, announced that its Atlas Platform is certified to run NVIDIA AI Enterprise, an end-to-end, cloud-native suite of AI and data analytics software that is optimized to enable any organization to use AI.

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Efficient Compute Orchestration Yields More Productive AI RUN:AI'S AI RESOURCE
Run:ai’s Kubernetes-based software platform for orchestration of containerized AI workloads enables GPU clusters to be utilized for different Deep Learning workloads dynamically from building AI models, to training, to inference. With Run:ai, jobs at any stage get access to the compute power they need, automatically.

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