Prerequisites

Below are the prerequisites of a cluster installed with Run:AI.

Software Requirements¶

Kubernetes¶

Run:AI requires Kubernetes 1.19 or above. Kubernetes 1.21 is recommended (as of July 2021). Kubernetes 1.22 is not supported.

If you are using RedHat OpenShift. The minimal version is OpenShift 4.6.

Run:AI Supports Kubernetes Pod Security Policy if used.

NVIDIA¶

Run:AI requires the installation of NVIDIA software. These can be done in one of two ways. Installing the GPU Operator or installing NVIDIA software on each node. For more information, see the Cluster Installation documentation.

Prometheus¶

Run:AI requires Prometheus. The Run:AI Cluster installation will, by default, install Prometheus, but it can also connect to an existing Prometheus installed by the organization. In the latter case, it's important to:

• Verify that both Prometheus Node Exporter and kube-state-metrics are installed. Both are part of the default Prometheus installation
• Understand how Prometheus has been installed. Whether directly or with the Prometheus Operator. The distinction is important during the Run:AI Cluster installation.

Hardware Requirements¶

(see picture below)

• (Production only) Run:AI System Nodes: To reduce downtime and save CPU cycles on expensive GPU Machines, we recommend that production deployments will contain two or more worker machines, designated for Run:AI Software. The nodes do not have to be dedicated to Run:AI, but for Run:AI purposes we would need:

  • 4 CPUs
  • 8GB of RAM
  • 50GB of Disk space

• Shared data volume: Run:AI uses Kubernetes to abstract away the machine on which a container is running:

  • Researcher containers: The Researcher's containers need to be able to access data from any machine in a uniform way, to access training data and code as well as save checkpoints, weights, and other machine-learning-related artifacts.
  • The Run:AI system needs to save data on a storage device that is not dependent on a specific node.

  Typically, this is achieved via Network File Storage (NFS) or Network-attached storage (NAS). NFS is usually the preferred method for Researchers which may require multi-read/write capabilities.

• Docker Registry With Run:AI, Workloads are based on Docker images. For container images to run on any machine, these images must be downloaded from a docker registry rather than reside on the local machine (though this also is possible). You can use a public registry such as docker hub or set up a local registry on-prem (preferably on a dedicated machine). Run:AI can assist with setting up the repository.

• Kubernetes: Though out of scope for this document, Production Kubernetes installation requires separate nodes for the Kubernetes master.

User requirements¶

Usage of containers and images: The individual Researcher's work should be based on container images.

Network Requirements¶

Run:AI user interface runs from the cloud. All container nodes must be able to connect to the Run:AI cloud. Inbound connectivity (connecting from the cloud into nodes) is not required. If outbound connectivity is proxied/limited, the following exceptions should be applied:

During Installation¶

Run:AI requires an installation over the Kubernetes cluster. The installation access the web to download various images and registries. Some organizations place limitations on what you can pull from the internet. The following list shows the various solution components and their origin:

Post Installation¶

In addition, once running, Run:AI will send metrics to two sources: