Run:ai

Define the training workload specification, including the required GPU resources and AI framework.

Submit the training job to Run:ai.

Run:ai automatically allocates the necessary GPU resources to the job.

The training job runs on the allocated GPUs, leveraging GPU virtualization for optimal performance.

Monitor the training progress and resource utilization through Run:ai's dashboard.

Create a Run:ai project and invite team members.

Define resource quotas and allocation policies for the project.

Team members submit their AI workloads to the project.

Run:ai automatically manages resource allocation and scheduling across the team.

Team members can monitor their workload performance and resource utilization through Run:ai's dashboard.

Deploy Run:ai on your Kubernetes cluster.

Run:ai automatically discovers and virtualizes the available GPUs.

Define resource pools and allocation policies based on workload requirements.

Run:ai dynamically allocates GPU resources to workloads based on demand.

Monitor GPU utilization through Run:ai's dashboard and identify opportunities for optimization.

Define the production workload specification, including resource requirements and deployment settings.

Submit the workload to Run:ai.

Run:ai automatically deploys and manages the workload across the Kubernetes cluster.

Monitor the workload performance and resource utilization through Run:ai's dashboard.

Scale the workload automatically based on demand.

Deploy Run:ai on a Kubernetes cluster.

Configure the necessary dependencies for each AI framework (e.g., TensorFlow, PyTorch).

Define workload specifications for each framework.

Submit the workloads to Run:ai.

Run:ai automatically manages the workloads, ensuring compatibility and optimal performance.