mirror of https://github.com/kubeflow/examples.git
cc93a80420
* A notebook to run the mnist E2E example on GCP.
This fixes a number of issues with the example
* Use ISTIO instead of Ambassador to add reverse proxy routes
* The training job needs to be updated to run in a profile created namespace in order to have the required service accounts
* See kubeflow/examples#713
* Running inside a notebook running on Kubeflow should ensure user
is running inside an appropriately setup namespace
* With ISTIO the default RBAC rules prevent the web UI from sending requests to the model server
* A short term fix was to not include the ISTIO side car
* In the future we can add an appropriate ISTIO rbac policy
* Using a notebook allows us to eliminate the use of kustomize
* This resolves kubeflow/examples#713 which required people to use
and old version of kustomize
* Rather than using kustomize we can use python f style strings to
write the YAML specs and then easily substitute in user specific values
* This should be more informative; it avoids introducing kustomize and
users can see the resource specs.
* I've opted to make the notebook GCP specific. I think its less confusing
to users to have separate notebooks focused on specific platforms rather
than having one notebook with a lot of caveats about what to do under
different conditions
* I've deleted the kustomize overlays for GCS since we don't want users to
use them anymore
* I used fairing and kaniko to eliminate the use of docker to build the images
so that everything can run from a notebook running inside the cluster.
* k8s_utils.py has some reusable functions to add some details from users
(e.g. low level calls to K8s APIs.)
* * Change the mnist test to just run the notebook
* Copy the notebook test infra for xgboost_synthetic to py/kubeflow/examples/notebook_test to make it more reusable
* Fix lint.
* Update for lint.
* A notebook to run the mnist E2E example.
Related to: kubeflow/website#1553
* 1. Use fairing to build the model. 2. Construct the YAML spec directly in the notebook. 3. Use the TFJob python SDK.
* Fix the ISTIO rule.
* Fix UI and serving; need to update TF serving to match version trained on.
* Get the IAP endpoint.
* Start writing some helper python functions for K8s.
* Commit before switching from replace to delete.
* Create a library to bulk create objects.
* Cleanup.
* Add back k8s_util.py
* Delete train.yaml; this shouldn't have been aded.
* update the notebook image.
* Refactor code into k8s_util; print out links.
* Clean up the notebok. Should be working E2E.
* Added section to get logs from stackdriver.
* Add comment about profile.
* Latest.
* Override mnist_gcp.ipynb with mnist.ipynb
I accidentally put my latest changes in mnist.ipynb even though that file
was deleted.
* More fixes.
* Resolve some conflicts from the rebase; override with changes on remote branch.
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| copy_secret.sh | ||