kubeflow
/
examples
mirror of https://github.com/kubeflow/examples.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Add kubeflow namespace for kubectl commands in Readme for financial time series example (#706)

This commit is contained in:
Svendegroote91 2019-12-20 20:07:33 +01:00 committed by Kubernetes Prow Robot
parent d93c18f66e
commit d925823716
1 changed files with 9 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
financial_time_series/README.md
View File

@ -92,9 +92,9 @@ Next we can launch the tf-job to our Kubeflow cluster and follow the progress vi
```
kubectl apply -f CPU/tfjob1.yaml
POD_NAME=$(kubectl get pods --selector=tf-job-name=tfjob-flat \
POD_NAME=$(kubectl get pods -n kubeflow --selector=tf-job-name=tfjob-flat \
--template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}')
kubectl logs -f $POD_NAME
kubectl logs -f $POD_NAME -n kubeflow
```
In the logs you can see that the trained model is being exported to google cloud storage. This saved model will be used later on for serving requests. With these parameters, the accuracy on the test set is approximating about 60%. 
@ -118,13 +118,13 @@ After running these commands, a deployment and service will be launched on Kuber
Let's check if the model is loaded successfully.
```
POD=`kubectl get pods --selector=app=model | awk '{print $1}' | tail -1`
kubectl logs -f $POD
POD=`kubectl get pods -n kubeflow --selector=app=model | awk '{print $1}' | tail -1`
kubectl logs -f $POD -n kubeflow
```
We will do a local test via HTTP to illustrate how to get results from this serving component. Once the pod is up we can set up port-forwarding to our localhost.
```
kubectl port-forward $POD 8500:8500 2>&1 >/dev/null &
kubectl port-forward $POD 8500:8500 -n kubeflow 2>&1 >/dev/null &
```
Now the only thing we need to do is send a request to ```localhost:8500``` with the expected input of the saved model and it will return a prediction.
@ -162,9 +162,9 @@ kubectl apply -f CPU/tfjob2.yaml
Verify the logs via:
```
POD_NAME=$(kubectl get pods --selector=tf-job-name=tfjob-deep \
POD_NAME=$(kubectl get pods -n kubeflow --selector=tf-job-name=tfjob-deep \
--template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}')
kubectl logs -f $POD_NAME
kubectl logs -f $POD_NAME -n kubeflow
```
You should notice that the training now takes a few minutes instead of less than one minute.
@ -206,9 +206,9 @@ First the pod will be unschedulable as there are no gpu-pool nodes available. Th
Once the pod is up, you can check the logs and verify that the training time is reduced compared to the previous tf-job.
```
POD_NAME=$(kubectl get pods --selector=tf-job-name=tfjob-deep-gpu \
POD_NAME=$(kubectl get pods -n kubeflow --selector=tf-job-name=tfjob-deep-gpu \
--template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}')
kubectl logs -f $POD_NAME
kubectl logs -f $POD_NAME -n kubeflow
```
### Kubeflow Pipelines