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Kubernetes Deploy: Triton Inference Server Cluster

A helm chart for installing a single cluster of Triton Inference Server is provided. By default the cluster contains a single instance of the inference server but the replicaCount configuration parameter can be set to create a cluster of any size, as described below.

This guide assumes you already have a functional Kubernetes cluster and helm installed (see below for instructions on installing helm). Note the following requirements:

  • The helm chart deploys Prometheus and Grafana to collect and display Triton metrics. To use this helm chart you must install Prometheus and Grafana in your cluster as described below and your cluster must contain sufficient CPU resources to support these services.

  • If you want Triton Server to use GPUs for inferencing, your cluster must be configured to contain the desired number of GPU nodes (A10 GPU instances recommended) with support for the NVIDIA driver and CUDA version required by the version of the inference server you are using.

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.

Notes for OKE cluster

When creating your node pool, the default value for the boot volume is 46.6GB. Due to the size of the server container, it is recommended to increase this value to 150GB and set a cloud-init script to increase the partition:

#!/bin/bash
curl --fail -H "Authorization: Bearer Oracle" -L0 http://169.254.169.254/opc/v2/instance/metadata/oke_init_script | base64 --decode >/var/run/oke-init.sh
bash /var/run/oke-init.sh
sudo /usr/libexec/oci-growfs -y

Installing Helm

Using Cloud Shell from OCI Web Console

It is possible to access your OKE Cluster directly from the OCI Web Console. Helm v3 is already available from the Cloud Shell.

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're currently using Helm v2 and would like to migrate to Helm v3, please see the official migration guide.

Helm v2

NOTE: Moving forward this chart will only be tested and maintained for Helm v3.

Below are example instructions for installing Helm v2.

$ curl https://raw.githubusercontent.com/helm/helm/master/scripts/get | bash
$ kubectl create serviceaccount -n kube-system tiller
serviceaccount/tiller created
$ kubectl create clusterrolebinding tiller-cluster-rule --clusterrole=cluster-admin --serviceaccount=kube-system:tiller
$ helm init --service-account tiller --wait

If you run into any issues, you can refer to the official installation guide here.

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 checkout a local copy of the inference 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 you will place the model repository in an S3 compatible OCI Object Storage Bucket.

$ oci os bucket create --compartment-id <COMPARTMENT_OCID> --name triton-inference-server-repository

Following the QuickStart download the example model repository to your system and copy it into the OCI Bucket.

$ oci os object bulk-upload -bn triton-inference-server-repository --src-dir docs/examples/model_repository/

OCI Model Repository

To load the model from the OCI Object Storage Bucket, you need to convert the following OCI credentials in the base64 format and add it to the values.yaml

echo -n 'REGION' | base64

echo -n 'SECRECT_KEY_ID' | base64

echo -n 'SECRET_ACCESS_KEY' | base64

You also need to adapt modelRepositoryPath in values.yaml to your namespace and OCI region.

s3://https://<OCI_NAMESPACE>.compat.objectstorage.<OCI_REGION>.oraclecloud.com:443/triton-inference-server-repository

Deploy Prometheus and Grafana

The inference server metrics are collected by Prometheus and viewable by Grafana. The inference server helm chart assumes that Prometheus and Grafana are available so this step must be followed even if you don't want to use Grafana.

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

$ 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 login.

Note that it is also possible to set a load balancer service for the grafana dashboard by running:

$ helm install example-metrics --set prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues=false --set grafana.service.type=LoadBalancer prometheus-community/kube-prometheus-stack

You can then see the Public IP of you grafana dashboard by running:

$ kubectl get svc
NAME                                       TYPE           CLUSTER-IP     EXTERNAL-IP       PORT(S)                      AGE
alertmanager-operated                      ClusterIP      None           <none>            9093/TCP,9094/TCP,9094/UDP   2m33s
example-metrics-grafana                    LoadBalancer   10.96.82.33    141.145.220.114   80:31005/TCP                 2m38s

The default load balancer created comes with a fixed shape and a bandwidth of 100Mbps. You can switch to a flexible shape and adapt the bandwidth according to your OCI limits in case the bandwidth is a bottleneck.

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

Deploy the Inference Server

Deploy the inference server using the default configuration with the following commands.

$ 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 of four inference servers use --set to set the replicaCount parameter.

$ helm install example --set replicaCount=4 .

You can also write your own "config.yaml" file with the values you want to override and pass it to helm.

$ cat << EOF > config.yaml
namespace: MyCustomNamespace
image:
  imageName: nvcr.io/nvidia/tritonserver:custom-tag
  modelRepositoryPath: s3://https://<OCI_NAMESPACE>.compat.objectstorage.<OCI_REGION>.oraclecloud.com:443/triton-inference-server-repository
EOF
$ helm install example -f config.yaml .

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, the inferencing service is exposed with a LoadBalancer service type. Use the following to find the external IP for the inference server. In this case it is 34.83.9.133.

$ kubectl get services
NAME                             TYPE           CLUSTER-IP     EXTERNAL-IP   PORT(S)                                        AGE
...
example-triton-inference-server  LoadBalancer   10.18.13.28    34.83.9.133   8000:30249/TCP,8001:30068/TCP,8002:32723/TCP   47m

The inference server 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 34.83.9.133:8000/v2

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

$ image_client -u 34.83.9.133:8000 -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

Cleanup

Once you've 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

You may also want to delete the OCI bucket you created to hold the model repository.

$ oci os bucket delete --bucket-name triton-inference-server-repository --empty