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Kubernetes Deploy: NVIDIA Triton Inference Server Cluster

This repository includes a Helm chart and instructions for installing NVIDIA Triton Inference Server in an on-premises or AWS EC2 Kubernetes cluster. You can also use this repository to enable load balancing and autoscaling for your Triton cluster.

This guide assumes you already have a functional Kubernetes cluster with support for GPUs. See the NVIDIA GPU Operator documentation for instructions on how to install Kubernetes and enable GPU access in your Kubernetes cluster. You must also have Helm installed (see Installing Helm for instructions). Note the following requirements:

  • To deploy Prometheus and Grafana to collect and display Triton metrics, your cluster must contain sufficient CPU resources to support these services.

  • To use GPUs for inferencing, your cluster must be configured to contain the desired number of GPU nodes, with support for the NVIDIA driver and CUDA version required by the version of the inference server you are using.

  • To enable autoscaling, your cluster's kube-apiserver must have the aggregation layer enabled. This will allow the horizontal pod autoscaler to read custom metrics from the prometheus adapter.

This Helm chart is available from Triton Inference Server GitHub.

For more information on Helm and Helm charts, visit the Helm documentation.

Quickstart

First, clone this repository to a local machine. Then, execute the following commands:

Install helm

$ curl -fsSL -o get_helm.sh https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3
$ chmod 700 get_helm.sh
$ ./get_helm.sh

Deploy Prometheus and Grafana

$ helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
$ helm repo update
$ helm install example-metrics --set prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues=false prometheus-community/kube-prometheus-stack

Deploy Triton with default settings

helm install example ./deploy/k8s-onprem
<!-- The steps below describe how to set-up a model repository, use Helm to launch the inference server, and then send inference requests to the running server. You can access a Grafana endpoint to see real-time metrics reported by the inference server. -->

Installing Helm

Helm v3

If you do not already have Helm installed in your Kubernetes cluster, executing the following steps from the official Helm install guide will give you a quick setup.

If you are currently using Helm v2 and would like to migrate to Helm v3, see the official migration guide.

Model Repository

If you already have a model repository, you may use that with this Helm chart. If you do not have a model repository, you can check out a local copy of the server source repository to create an example model repository:

$ git clone https://github.com/triton-inference-server/server.git

Triton Server needs a repository of models that it will make available for inferencing. For this example, we are using an existing NFS server and placing our model files there. See the Model Repository documentation for other supported locations.

Following the QuickStart, download the example model repository to your system and copy it onto your NFS server. Then, add the url or IP address of your NFS server and the server path of your model repository to values.yaml.

Deploy Prometheus and Grafana

The inference server metrics are collected by Prometheus and viewable through Grafana. The inference server Helm chart assumes that Prometheus and Grafana are available so this step must be followed even if you do not want to use Grafana.

Use the kube-prometheus-stack Helm chart to install these components. The serviceMonitorSelectorNilUsesHelmValues flag is needed so that Prometheus can find the inference server metrics in the example release deployed in a later section.

$ helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
$ helm repo update
$ helm install example-metrics --set prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues=false prometheus-community/kube-prometheus-stack

Then port-forward to the Grafana service so you can access it from your local browser.

$ kubectl port-forward service/example-metrics-grafana 8080:80

Now you should be able to navigate in your browser to localhost:8080 and see the Grafana login page. Use username=admin and password=prom-operator to log in.

An example Grafana dashboard is available in dashboard.json. Use the import function in Grafana to import and view this dashboard.

Enable Autoscaling

To enable autoscaling, ensure that autoscaling tag in values.yamlis set to true. This will do two things:

  1. Deploy a Horizontal Pod Autoscaler that will scale replicas of the triton-inference-server based on the information included in values.yaml.

  2. Install the prometheus-adapter helm chart, allowing the Horizontal Pod Autoscaler to scale based on custom metrics from prometheus.

The included configuration will scale Triton pods based on the average queue time, as described in this blog post. To customize this, you may replace or add to the list of custom rules in values.yaml. If you change the custom metric, be sure to change the values in autoscaling.metrics.

If autoscaling is disabled, the number of Triton server pods is set to the minReplicas variable in values.yaml.

Enable Load Balancing

To enable load balancing, ensure that the loadBalancing tag in values.yaml is set to true. This will do two things:

  1. Deploy a Traefik reverse proxy through the Traefik Helm Chart.

  2. Configure two Traefik IngressRoutes, one for http and one for grpc. This will allow the Traefik service to expose two ports that will be forwarded to and balanced across the Triton pods.

To choose the port numbers exposed, or to disable either http or grpc, edit the configured variables in values.yaml.

Deploy the Inference Server

Deploy the inference server, autoscaler, and load balancer using the default configuration with the following commands.

Here, and in the following commands we use the name example for our chart. This name will be added to the beginning of all resources created during the helm installation.

$ cd <directory containing Chart.yaml>
$ helm install example .

Use kubectl to see status and wait until the inference server pods are running.

$ kubectl get pods
NAME                                               READY   STATUS    RESTARTS   AGE
example-triton-inference-server-5f74b55885-n6lt7   1/1     Running   0          2m21s

There are several ways of overriding the default configuration as described in this Helm documentation.

You can edit the values.yaml file directly or you can use the --set option to override a single parameter with the CLI. For example, to deploy a cluster with a minimum of two inference servers use --set to set the autoscaler.minReplicas parameter.

$ helm install example --set autoscaler.minReplicas=2 .

You can also write your own "config.yaml" file with the values you want to override and pass it to Helm. If you specify a "config.yaml" file, the values set will override those in values.yaml.

$ cat << EOF > config.yaml
namespace: MyCustomNamespace
image:
  imageName: nvcr.io/nvidia/tritonserver:custom-tag
  modelRepositoryPath: gs://my_model_repository
EOF
$ helm install example -f config.yaml .

Deploying the Inference Server on OpenShift or OKD

Because of the default security posture of OpenShift and OKD, the configuration of which uses OpenShift-specific APIs, the chart needs special consideration when targeting those environments. Any of the above discussed customizations and prerequisites hold for an OpenShift environment, except that you do not need to install Prometheus and Grafana and can instead enable monitoring for user-defined projects by following the OpenShift documentation on the topic.

To deploy the configurations to enable NFS mounts and the non-root UIDs used in the Triton deployment, a tag can be enabled alongside any other configurations discussed above. In the simplest case, to use --set on the command line, you can simply update the tags.openshift parameter.

$ cd <directory containing Chart.yaml>
$ helm install example --set tags.openshift=true .

Probe Configuration

In templates/deployment.yaml is configurations for livenessProbe, readinessProbe and startupProbe for the Triton server container. By default, Triton loads all the models before starting the HTTP server to respond to the probes. The process can take several minutes, depending on the models sizes. If it is not completed in startupProbe.failureThreshold * startupProbe.periodSeconds seconds then Kubernetes considers this as a pod failure and restarts it, ending up with an infinite loop of restarting pods, so make sure to sufficiently set these values for your use case. The liveliness and readiness probes are being sent only after the first success of a startup probe.

For more details, see the Kubernetes probe documentation and the feature page of the startup probe.

Using Triton Inference Server

Now that the inference server is running you can send HTTP or GRPC requests to it to perform inferencing. By default, this chart deploys Traefik and uses IngressRoutes to balance requests across all available nodes.

To send requests through the Traefik proxy, use the Cluster IP of the traefik service deployed by the Helm chart. In this case, it is 10.111.128.124.

$ kubectl get services
NAME                              TYPE           CLUSTER-IP       EXTERNAL-IP   PORT(S)                                                    AGE
...
example-traefik                   LoadBalancer   10.111.128.124   <pending>     8001:31752/TCP,8000:31941/TCP,80:30692/TCP,443:30303/TCP   74m
example-triton-inference-server   ClusterIP      None             <none>        8000/TCP,8001/TCP,8002/TCP                                 74m

Use the following command to refer to the Cluster IP:

cluster_ip=`kubectl get svc -l app.kubernetes.io/name=traefik -o=jsonpath='{.items[0].spec.clusterIP}'`

The Traefik reverse-proxy exposes an HTTP endpoint on port 8000, and GRPC endpoint on port 8001 and a Prometheus metrics endpoint on port 8002. You can use curl to get the meta-data of the inference server from the HTTP endpoint.

$ curl $cluster_ip:8000/v2

Follow the QuickStart to get the example image classification client that can be used to perform inferencing using image classification models on the inference server. For example,

$ image_client -u $cluster_ip:8000 -m inception_v3_onnx -s INCEPTION -c3 mug.jpg
Request 0, batch size 1
Image 'images/mug.jpg':
    504 (COFFEE MUG) = 0.723992
    968 (CUP) = 0.270953
    967 (ESPRESSO) = 0.00115997

Testing Load Balancing and Autoscaling

After you have confirmed that your Triton cluster is operational and can perform inference, you can test the load balancing and autoscaling features by sending a heavy load of requests. One option for doing this is using the perf_analyzer application.

You can apply a progressively increasing load with a command like:

perf_analyzer -m simple -u $cluster_ip:8000 --concurrency-range 1:10

From your Grafana dashboard, you should be able to see the number of pods increase as the load increases, with requests being routed evenly to the new pods.

Cleanup

After you have finished using the inference server, you should use Helm to delete the deployment.

$ helm list
NAME            REVISION  UPDATED                   STATUS    CHART                          APP VERSION   NAMESPACE
example         1         Wed Feb 27 22:16:55 2019  DEPLOYED  triton-inference-server-1.0.0  1.0           default
example-metrics	1       	Tue Jan 21 12:24:07 2020	DEPLOYED	prometheus-operator-6.18.0   	 0.32.0     	 default

$ helm uninstall example
$ helm uninstall example-metrics

For the Prometheus and Grafana services, you should explicitly delete CRDs:

$ kubectl delete crd alertmanagerconfigs.monitoring.coreos.com alertmanagers.monitoring.coreos.com podmonitors.monitoring.coreos.com probes.monitoring.coreos.com prometheuses.monitoring.coreos.com prometheusrules.monitoring.coreos.com servicemonitors.monitoring.coreos.com thanosrulers.monitoring.coreos.com