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. Your cluster must contain sufficient CPU resources to support these services. At a minimum you will likely require 2 CPU nodes with machine type of n1-standard-2 or greater.
-
If you want Triton Server 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.
This helm chart is available from Triton Inference Server GitHub or from the NVIDIA GPU Cloud (NGC).
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.
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.
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.
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 a Google Cloud Storage bucket.
$ gsutil mb gs://triton-inference-server-repository
Following the QuickStart download the example model repository to your system and copy it into the GCS bucket.
$ gsutil cp -r docs/examples/model_repository gs://triton-inference-server-repository/model_repository
Make sure the bucket permissions are set so that the inference server can access the model repository. If the bucket is public then no additional changes are needed and you can proceed to "Deploy Prometheus and Grafana" section.
If bucket premissions need to be set with the GOOGLE_APPLICATION_CREDENTIALS environment variable then perform the following steps:
-
Generate Google service account JSON with proper permissions called gcp-creds.json.
-
Create a Kubernetes secret from gcp-creds.json:
$ kubectl create configmap gcpcreds --from-literal "project-id=myproject"
$ kubectl create secret generic gcpcreds --from-file gcp-creds.json
- Modify templates/deployment.yaml to include the GOOGLE_APPLICATION_CREDENTIALS environment variable:
env:
- name: GOOGLE_APPLICATION_CREDENTIALS
value: /secret/gcp-creds.json
- Modify templates/deployment.yaml to mount the secret in a volume at /secret:
volumeMounts:
- name: vsecret
mountPath: "/secret"
readOnly: true
...
volumes:
- name: vsecret
secret:
secretName: gcpcreds
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.
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 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: gs://my_model_repository
EOF
$ helm install example -f config.yaml .
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_graphdef -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
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 GCS bucket you created to hold the model repository.
$ gsutil rm -r gs://triton-inference-server-repository