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Add instructions to deploy the seldon core model (#46) 

Update the issue summarization end to end tutorial
to deploy the seldon core model to the k8s cluster

Update the sample request and response

Related to https://github.com/kubeflow/examples/issues/11
This commit is contained in:
Ankush Agarwal 2018-03-15 14:31:23 -07:00 committed by k8s-ci-robot
parent 96c11b03cc
commit 45255b52e3
1 changed files with 52 additions and 11 deletions
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63
github_issue_summarization/serving_the_model.md
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@ -36,27 +36,68 @@ You can push the image by running `gcloud docker -- push gcr.io/gcr-repository-n
> You can find more details about wrapping a model with seldon-core [here](https://github.com/SeldonIO/seldon-core/blob/master/docs/wrappers/python.md)
## Sample request and response
# Deploying the model to your kubernetes cluster
Request
Now that we have an image with our model server, we can deploy it to our kubernetes cluster. We need to first deploy seldon-core to our cluster.
## Deploy Seldon Core
Install the CRD and it's controller using the seldon prototype
```bash
# Gives cluster-admin role to the default service account in the ${NAMESPACE}
kubectl create clusterrolebinding seldon-admin --clusterrole=cluster-admin --serviceaccount=${NAMESPACE}:default
# Install the kubeflow/seldon package
ks pkg install kubeflow/seldon
# Generate the seldon component and deploy it
ks generate seldon seldon --name=seldon --namespace=${NAMESPACE}
ks apply ${KF_ENV} -c seldon
```
Seldon Core should now be running on your cluster. You can verify it by running `kubectl get pods -n${NAMESPACE}`. You should see a pod named `seldon-cluster-manager-*`
## Deploying the actual model
Now that you have seldon core deployed, you can deploy the model using the `kubeflow/seldon-serve-simple` prototype.
```bash
ks generate seldon-serve-simple issue-summarization-model-serving \
--name=issue-summarization \
--image=gcr.io/gcr-repository-name/issue-summarization:0.1 \
--namespace=${NAMESPACE} \
--replicas=2
ks apply ${KF_ENV} -c issue-summarization-model-serving
```
# Sample request and response
Seldon Core uses ambassador to route it's requests. To send requests to the model, you can port-forward the ambassador container locally:
```
curl -X POST -d 'json={"data":{"ndarray":[["issue overview add a new property to disable detection of image stream files those ended with -is.yml from target directory. expected behaviour by default cube should not process image stream files if user does not set it. current behaviour cube always try to execute -is.yml files which can cause some problems in most of cases, for example if you are using kuberentes instead of openshift or if you use together fabric8 maven plugin with cube"]]}}' http://localhost:5000/predict
kubectl port-forward $(kubectl get pods -n ${NAMESPACE} -l service=ambassador -o jsonpath='{.items[0].metadata.name}') -n ${NAMESPACE} 8080:80
```
```
curl -X POST -H 'Content-Type: application/json' -d '{"data":{"ndarray":[["issue overview add a new property to disable detection of image stream files those ended with -is.yml from target directory. expected behaviour by default cube should not process image stream files if user does not set it. current behaviour cube always try to execute -is.yml files which can cause some problems in most of cases, for example if you are using kuberentes instead of openshift or if you use together fabric8 maven plugin with cube"]]}}' http://localhost:8080/seldon/issue-summarization/api/v0.1/predictions
```
Response
```
{
"meta": {
"puid": "2rqt023g11gt7vfr0jnfkf1hsa",
"tags": {
},
"routing": {
}
},
"data": {
"names": [
"t:0"
],
"ndarray": [
[
"add a new property to disable detection"
]
]
"names": ["t:0"],
"ndarray": [["add a new property"]]
}
}
```