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Expose TorchServe Metrics

This tutorial setups prometheus and granfana to the cluster with TorchServe metrics.

Install Istio with Grafana and Prometheus

Note: Make sure to enable prometheus and grafana while installing istio.

After installation Grafana and Prometheus can be accessed from the below links

# Grafana
istioctl dashboard grafana

# Prometheus
istioctl dashboard prometheus

Create the InferenceService

Enable prometheus scraping by adding annotations to deployment yaml, by default the torchserve's metrics port is 8082.

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: "torch-metrics"
  annotations:
    prometheus.io/scrape: 'true'
    prometheus.io/port: '8082'
spec:
  predictor:
    pytorch:
      storageUri: gs://kfserving-examples/models/torchserve/image_classifier/v1

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: "torch-metrics"
  annotations:
    prometheus.io/scrape: 'true'
    prometheus.io/port: '8082'
spec:
  predictor:
    model:
      modelFormat:
        name: pytorch
      storageUri: gs://kfserving-examples/models/torchserve/image_classifier/v1

Apply the CRD

kubectl apply -f metrics.yaml

Expected Output

$ inferenceservice.serving.kserve.io/torch-metrics created

Run a prediction

The first step is to determine the ingress IP and ports and set INGRESS_HOST and INGRESS_PORT

MODEL_NAME=mnist
SERVICE_HOSTNAME=$(kubectl get inferenceservice torch-metrics <namespace> -o jsonpath='{.status.url}' | cut -d "/" -f 3)

curl -v -H "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/${MODEL_NAME}:predict -d @./mnist.json

Expected Output

*   Trying 52.89.19.61...
* Connected to a881f5a8c676a41edbccdb0a394a80d6-2069247558.us-west-2.elb.amazonaws.com (52.89.19.61) port 80 (#0)
> PUT /v1/models/mnist:predict HTTP/1.1
> Host: torch-metrics.kserve-test.example.com
> User-Agent: curl/7.47.0
> Accept: */*
> Content-Length: 272
> Expect: 100-continue
>
< HTTP/1.1 100 Continue
* We are completely uploaded and fine
< HTTP/1.1 200 OK
< cache-control: no-cache; no-store, must-revalidate, private
< content-length: 1
< date: Fri, 23 Oct 2020 13:01:09 GMT
< expires: Thu, 01 Jan 1970 00:00:00 UTC
< pragma: no-cache
< x-request-id: 8881f2b9-462e-4e2d-972f-90b4eb083e53
< x-envoy-upstream-service-time: 5018
< server: istio-envoy
<
* Connection #0 to host a881f5a8c676a41edbccdb0a394a80d6-2069247558.us-west-2.elb.amazonaws.com left intact
{"predictions": ["2"]}

Check the dashboard

Prometheus graph view

  • Navigate to prometheus page
  • Add a query in the prometheus page

Add query Graph

Grafana dashboard

  • Navigate to grafana page
  • Add a dashboard from the top left + symbol
  • Click add query and enter the query

Add dashboard

For Exposing grafana and prometheus under istio ingress please refer to remotely accessing telemetry addons

Apply below deployment for a demo setup.

apiVersion: networking.istio.io/v1alpha3
kind: Gateway
metadata:
  name: grafana-gateway
  namespace: istio-system
spec:
  selector:
    istio: ingressgateway
  servers:
  - port:
      number: 80
      name: http-grafana
      protocol: HTTP
    hosts:
    - "grafana.example.com"
---
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: grafana-vs
  namespace: istio-system
spec:
  hosts:
  - "grafana.example.com"
  gateways:
  - grafana-gateway
  http:
  - route:
    - destination:
        host: grafana
        port:
          number: 3000
---
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
  name: grafana
  namespace: istio-system
spec:
  host: grafana
  trafficPolicy:
    tls:
      mode: DISABLE
---

All request with hostname grafana.example.com redirects to grafana.

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