Edit

Request Payload Logging with ELK

An example of payload logging of Seldon Deployment requests and responses.

Prerequisites

  • A kubernetes cluster with kubectl configured

  • curl

  • grpcurl

  • pygmentize

Setup Seldon Core

Install Seldon Core as described in docs

Then port-forward to that ingress on localhost:8003 in a separate terminal either with:

  • Ambassador:

kubectl port-forward $(kubectl get pods -n seldon -l app.kubernetes.io/name=ambassador -o jsonpath='{.items[0].metadata.name}') -n seldon 8003:8080```

* Istio: 

```bash 
kubectl port-forward $(kubectl get pods -l istio=ingressgateway -n istio-system -o jsonpath='{.items[0].metadata.name}') -n istio-system 8003:80```



```python
!kubectl create namespace seldon
Error from server (AlreadyExists): namespaces "seldon" already exists
!kubectl config set-context $(kubectl config current-context) --namespace=seldon
Context "kind-kind" modified.
from IPython.core.magic import register_line_cell_magic


@register_line_cell_magic
def writetemplate(line, cell):
    with open(line, "w") as f:
        f.write(cell.format(**globals()))
VERSION = !cat ../../../version.txt
VERSION = VERSION[0]
VERSION
'1.5.0-dev'

Deploy a Request Logger

This will echo CloudEvents it receives.

!pygmentize message-dumper.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: logger
spec:
  selector:
    matchLabels:
      run: logger
  replicas: 1
  template:
    metadata:
      labels:
        run: logger
    spec:
      containers:
      - name: logger
        image: mendhak/http-https-echo
        ports:
        - containerPort: 80
---
apiVersion: v1
kind: Service
metadata:
  name: logger
  labels:
    run: logger
spec:
  ports:
  - port: 80
    targetPort: 80
    protocol: TCP
  selector:
    run: logger

    
!kubectl apply -f message-dumper.yaml -n seldon
deployment.apps/logger created
service/logger created
!kubectl rollout status deploy/logger
Waiting for deployment "logger" rollout to finish: 0 of 1 updated replicas are available...
deployment "logger" successfully rolled out

Create a Model with Logging

%%writetemplate model_logger.yaml
apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: model-logs
spec:
  name: model-logs
  predictors:
  - componentSpecs:
    - spec:
        containers:
        - image: seldonio/mock_classifier:{VERSION}
          name: classifier
    graph:
      children: []
      endpoint:
        type: REST
      name: classifier
      type: MODEL
      logger:
        url: http://logger.seldon/
        mode: all
    name: logging
    replicas: 1

!kubectl apply -f model_logger.yaml -n seldon
seldondeployment.machinelearning.seldon.io/model-logs created
!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=model-logs -o jsonpath='{.items[0].metadata.name}')
Waiting for deployment "model-logs-logging-0-classifier" rollout to finish: 0 of 1 updated replicas are available...
deployment "model-logs-logging-0-classifier" successfully rolled out

Send a Prediction Request

res=!curl -s -d '{"data": {"ndarray":[[1.0, 2.0, 5.0]]}}' \
   -X POST http://localhost:8003/seldon/seldon/model-logs/api/v1.0/predictions \
   -H "Content-Type: application/json";
print(res)
import json
j=json.loads(res[0])
assert(j["data"]["ndarray"][0][0]>0.2)
['{"data":{"names":["proba"],"ndarray":[[0.43782349911420193]]},"meta":{}}']

Check Logger

!kubectl logs $(kubectl get pods -l run=logger -n seldon -o jsonpath='{.items[0].metadata.name}') logger
-----------------
{
    "path": "/",
    "headers": {
        "host": "logger.seldon",
        "user-agent": "Go-http-client/1.1",
        "content-length": "39",
        "ce-endpoint": "logging",
        "ce-id": "e276a0c3-a522-4499-b509-5e98e06e96fe",
        "ce-inferenceservicename": "model-logs",
        "ce-modelid": "classifier",
        "ce-namespace": "seldon",
        "ce-requestid": "33fb419e-8d6b-44e0-a084-8bd4bd4dbf7b",
        "ce-source": "http://:8000/",
        "ce-specversion": "1.0",
        "ce-time": "2020-11-06T09:35:09.171644169Z",
        "ce-traceparent": "00-9c7cc210d352f7b68be6422c1b4b78f4-a7e8a2a38709bbc9-00",
        "ce-type": "io.seldon.serving.inference.request",
        "content-type": "application/json",
        "traceparent": "00-9c7cc210d352f7b68be6422c1b4b78f4-b49edd5ad25552ae-00",
        "accept-encoding": "gzip"
    },
    "method": "POST",
    "body": "{\"data\": {\"ndarray\":[[1.0, 2.0, 5.0]]}}",
    "fresh": false,
    "hostname": "logger.seldon",
    "ip": "::ffff:10.244.1.65",
    "ips": [],
    "protocol": "http",
    "query": {},
    "subdomains": [],
    "xhr": false,
    "os": {
        "hostname": "logger-766f99b9b7-mqtql"
    },
    "connection": {},
    "json": {
        "data": {
            "ndarray": [
                [
                    1,
                    2,
                    5
                ]
            ]
        }
    }
}
::ffff:10.244.1.65 - - [06/Nov/2020:09:35:09 +0000] "POST / HTTP/1.1" 200 1220 "-" "Go-http-client/1.1"
-----------------
{
    "path": "/",
    "headers": {
        "host": "logger.seldon",
        "user-agent": "Go-http-client/1.1",
        "content-length": "73",
        "ce-endpoint": "logging",
        "ce-id": "68345c32-d144-4e21-a840-55e7e809c002",
        "ce-inferenceservicename": "model-logs",
        "ce-modelid": "classifier",
        "ce-namespace": "seldon",
        "ce-requestid": "33fb419e-8d6b-44e0-a084-8bd4bd4dbf7b",
        "ce-source": "http://:8000/",
        "ce-specversion": "1.0",
        "ce-time": "2020-11-06T09:35:09.180317759Z",
        "ce-traceparent": "00-cbb2fa5d83dbc42f2f8e9f8957b5c121-c15418121da2e992-00",
        "ce-type": "io.seldon.serving.inference.response",
        "content-type": "application/json",
        "traceparent": "00-cbb2fa5d83dbc42f2f8e9f8957b5c121-ce0a53c967ee8077-00",
        "accept-encoding": "gzip"
    },
    "method": "POST",
    "body": "{\"data\":{\"names\":[\"proba\"],\"ndarray\":[[0.43782349911420193]]},\"meta\":{}}\n",
    "fresh": false,
    "hostname": "logger.seldon",
    "ip": "::ffff:10.244.1.65",
    "ips": [],
    "protocol": "http",
    "query": {},
    "subdomains": [],
    "xhr": false,
    "os": {
        "hostname": "logger-766f99b9b7-mqtql"
    },
    "connection": {},
    "json": {
        "data": {
            "names": [
                "proba"
            ],
            "ndarray": [
                [
                    0.43782349911420193
                ]
            ]
        },
        "meta": {}
    }
}
::ffff:10.244.1.65 - - [06/Nov/2020:09:35:09 +0000] "POST / HTTP/1.1" 200 1312 "-" "Go-http-client/1.1"
modelids = !kubectl logs $(kubectl get pods -l run=logger -n seldon -o jsonpath='{.items[0].metadata.name}') logger | grep "ce-modelid"
print(modelids)
assert modelids[0].strip() == '"ce-modelid": "classifier",'
['        "ce-modelid": "classifier",', '        "ce-modelid": "classifier",']

Clean Up

!kubectl delete -f model_logger.yaml -n seldon
seldondeployment.machinelearning.seldon.io "model-logs" deleted
!kubectl delete -f message-dumper.yaml -n seldon
deployment.apps "logger" deleted
service "logger" deleted
PreviousKEDA Autoscaling exampleNextCustom Metrics with Grafana & Prometheus

Last updated

Was this helpful?