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

Prerequisites in a Nutshell¶

The following is a checklist of the Run:ai prerequisites:

There are also specific hardware, operating system and network access requirements. A pre-install script is available to test if the prerequisites are met before installation.

Software Requirements¶

Operating System¶

• Run:ai will work on any Linux operating system that is supported by both Kubernetes and NVIDIA.
• An important highlight is that GKE (Google Kubernetes Engine) will only work with Ubuntu, as NVIDIA does not support the default Container-Optimized OS with Containerd image.
• Run:ai performs its internal tests on Ubuntu 20.04 and CoreOS for OpenShift.

Kubernetes¶

Run:ai requires Kubernetes. Run:ai is been certified with the following Kubernetes distributions:

Run:ai has been tested with the following Kubernetes distributions. Please contact Run:ai Customer Support for up to date certification details:

Following is a Kubernetes support matrix for the latest Run:ai releases:

For an up-to-date end-of-life statement of Kubernetes see Kubernetes Release History.

Pod Security Admission¶

Run:ai version 2.15 and above supports restricted policy for Pod Security Admission (PSA) on OpenShift only. Other Kubernetes distributions are only supported with Privileged policy.

For Run:ai on OpenShift to run with PSA restricted policy:

1. The runai namespace should still be marked as privileged as described in Pod Security Admission. Specifically, label the namespace with the following labels:

pod-security.kubernetes.io/audit=privileged
pod-security.kubernetes.io/enforce=privileged
pod-security.kubernetes.io/warn=privileged

NVIDIA¶

Run:ai has been certified on NVIDIA GPU Operator 22.9 to 24.6. Older versions (1.10 and 1.11) have a documented NVIDIA issue.

Follow the Getting Started guide to install the NVIDIA GPU Operator, or see the distribution-specific instructions below:

kubectl create ns gpu-operator

apiVersion: v1
kind: ResourceQuota
metadata:
  name: gcp-critical-pods
  namespace: gpu-operator
spec:
  scopeSelector:
    matchExpressions:
    - operator: In
      scopeName: PriorityClass
      values:
      - system-node-critical
      - system-cluster-critical

Ingress Controller¶

Run:ai requires an ingress controller as a prerequisite. The Run:ai cluster installation configures one or more ingress objects on top of the controller.

There are many ways to install and configure an ingress controller and configuration is environment-dependent. A simple solution is to install & configure *NGINX_:

helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
helm repo update
helm upgrade -i nginx-ingress ingress-nginx/ingress-nginx   \
    --namespace nginx-ingress --create-namespace \
    --set controller.kind=DaemonSet \
    --set controller.service.externalIPs="{<INTERNAL-IP>,<EXTERNAL-IP>}" # (1)

helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
helm repo update
helm install nginx-ingress ingress-nginx/ingress-nginx \
    --namespace nginx-ingress --create-namespace 

For support of ingress controllers different than NGINX please contact Run:ai customer support.

Cluster URL¶

The Run:ai cluster creation wizard requires a domain name (FQDN) to the Kubernetes cluster as well as a trusted certificate for that domain. The domain name needs to be accessible inside the organization only.

Use an HTTPS-based domain (e.g. https://my-cluster.com) as the cluster URL. Make sure that the DNS is configured with the cluster IP.

In addition, to configure HTTPS for your URL, you must create a TLS secret named runai-cluster-domain-tls-secret in the runai namespace. The secret should contain a trusted certificate for the domain:

kubectl create ns runai
kubectl create secret tls runai-cluster-domain-tls-secret -n runai \
    --cert /path/to/fullchain.pem  \ # (1)
    --key /path/to/private.pem # (2)

1. The domain's cert (public key).
2. The domain's private key.

For more information on how to create a TLS secret see: https://kubernetes.io/docs/concepts/configuration/secret/#tls-secrets.

Prometheus¶

If not already installed on your cluster, install the full kube-prometheus-stack through the Prometheus community Operator.

Then install the Prometheus stack by running:

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update
helm install prometheus prometheus-community/kube-prometheus-stack \
    -n monitoring --create-namespace --set grafana.enabled=false # (1)

1. The Grafana component is not required for Run:ai.

Optional Software Requirements¶

The following software enables specific features of Run:ai

Distributed Training¶

Distributed training allows the Researcher to train models over multiple nodes. Run:ai supports the following distributed training frameworks:

• TensorFlow
• PyTorch
• XGBoost
• MPI

All are part of the Kubeflow Training Operator. Run:ai supports Training Operator version 1.7 and up. To install run:

kubectl apply -k "github.com/kubeflow/training-operator/manifests/overlays/standalone?ref=v1.7.0"

The Kuberflow Training Operator is packaged with MPI version 1.0 which is not supported by Run:ai. You need to separately install MPI v2beta1:

kubectl apply -f https://raw.githubusercontent.com/kubeflow/mpi-operator/v0.4.0/deploy/v2beta1/mpi-operator.yaml

Inference¶

To use the Run:ai inference module you must pre-install Knative Serving. Follow the instructions here to install. Run:ai is certified on Knative 1.4 to 1.12.

Post-install, you must configure Knative to use the Run:ai scheduler and allow pod affinity, by running:

kubectl patch configmap/config-features \
  --namespace knative-serving \
  --type merge \
  --patch '{"data":{"kubernetes.podspec-schedulername":"enabled","kubernetes.podspec-affinity":"enabled","kubernetes.podspec-tolerations":"enabled","kubernetes.podspec-volumes-emptydir":"enabled","kubernetes.podspec-securitycontext":"enabled","kubernetes.podspec-persistent-volume-claim":"enabled","kubernetes.podspec-persistent-volume-write":"enabled","multi-container":"enabled","kubernetes.podspec-init-containers":"enabled"}}'

Inference Autoscaling¶

Run:ai allows to autoscale a deployment according to various metrics:

1. GPU Utilization (%)
2. CPU Utilization (%)
3. Latency (milliseconds)
4. Throughput (requests/second)
5. Concurrency
6. Any custom metric

Additional installation may be needed for some of the metrics as follows:

• Using Throughput or Concurrency does not require any additional installation.
• Any other metric will require installing the HPA Autoscaler.
• Using GPU Utilization_, Latency or Custom metric will also* require the Prometheus adapter. The Prometheus adapter is part of the Run:ai installer and can be added by setting the prometheus-adapter.enabled flag to true. See Customizing the Run:ai installation for further information.

If you wish to use an existing Prometheus adapter installation, you will need to configure it manually with the Run:ai Prometheus rules, specified in the Run:ai chart values under prometheus-adapter.rules field. For further information please contact Run:ai customer support.

Accessing Inference from outside the Cluster¶

Inference workloads will typically be accessed by consumers residing outside the cluster. You will hence want to provide consumers with a URL to access the workload. The URL can be found in the Run:ai user interface under the deployment screen (alternatively, run kubectl get ksvc -n <project-namespace>).

However, for the URL to be accessible outside the cluster you must configure your DNS as described here.

curl http://<external-ip>/<container-specific-path>
    -H 'Host: <host-name>'

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:

• 8 CPUs

• 16GB 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 Kubernetes Storage class based on Network File Storage (NFS) or Network-attached storage (NAS).

• 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: Production Kubernetes installation requires separate nodes for the Kubernetes master. For more details see your specific Kubernetes distribution documentation.

User requirements¶

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

Network Access Requirements¶

Internal networking: Kubernetes networking is an add-on rather than a core part of Kubernetes. Different add-ons have different network requirements. You should consult the documentation of the specific add-on on which ports to open. It is however important to note that unless special provisions are made, Kubernetes assumes all cluster nodes can interconnect using all ports.

Outbound network: 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 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 requires an outbound network connection to the following targets:

Network Proxy¶

If you are using a Proxy for outbound communication please contact Run:ai customer support

Pre-install Script¶

Once you believe that the Run:ai prerequisites are met, we highly recommend installing and running the Run:ai pre-install diagnostics script. The tool:

• Tests the below requirements as well as additional failure points related to Kubernetes, NVIDIA, storage, and networking.
• Looks at additional components installed and analyze their relevance to a successful Run:ai installation.

To use the script download the latest version of the script and run:

chmod +x preinstall-diagnostics-<platform>
./preinstall-diagnostics-<platform>

If the script shows warnings or errors, locate the file runai-preinstall-diagnostics.txt in the current directory and send it to Run:ai technical support.

For more information on the script including additional command-line flags, see here.