* Update tfjob components to v1beta1
Remove old version of tensor2tensor component
* Combine UI into a single jsonnet file
* Upgrade GH issue summarization to kf v0.4.0-rc.2
Use latest ksonnet v0.13.1
Use latest seldon v1alpha2
Remove ksonnet app with full kubeflow platform & replace with components specific to this example.
Remove outdated scripts
Add cluster creation links to Click-to-deploy & kfctl
Add warning not to use the Training with an Estimator guide
Replace commandline with bash for better syntax highlighting
Replace messy port-forwarding commands with svc/ambassador
Add modelUrl param to ui component
Modify teardown instructions to remove the deployment
Fix grammatical mistakes
* Rearrange tfjob instructions
* Unify the code for training with Keras and TF.Estimator
Create a single train.py and trainer.py which uses Keras inside TensorFlow
Provide options to either train with Keras or TF.TensorFlow
The code to train with TF.estimator doesn't worki
See #196
The original PR (#203) worked around a blocking issue with Keras and TF.Estimator by commenting
certain layers in the model architecture leading to a model that wouldn't generate meaningful
predictions
We weren't able to get TF.Estimator working but this PR should make it easier to troubleshoot further
We've unified the existing code so that we don't duplicate the code just to train with TF.estimator
We've added unitttests that can be used to verify training with TF.estimator works. This test
can also be used to reproduce the current errors with TF.estimator.
Add a Makefile to build the Docker image
Add a NFS PVC to our Kubeflow demo deployment.
Create a tfjob-estimator component in our ksonnet component.
changes to distributed/train.py as part of merging with notebooks/train.py
* Add command line arguments to specify paths rather than hard coding them.
* Remove the code at the start of train.py to wait until the input data
becomes available.
* I think the original intent was to allow the TFJob to be started simultaneously with the preprocessing
job and just block until the data is available
* That should be unnecessary since we can just run the preprocessing job as a separate job.
Fix notebooks/train.py (#186)
The code wasn't actually calling Model Fit
Add a unittest to verify we can invoke fit and evaluate without throwing exceptions.
* Address comments.
* Fix gh-demo.kubeflow.org and make it easy to setup.
* Our public demo of the GitHub issue summarization example
(gh-demo.kubeflow.org) is down. It was running in one of our dev
clusters and with the the churn in dev clusters it ended up getting deleted.
* To make it more stable lets move it to project kubecon-gh-demo-1
and create a separate cluster for running it.
This cluster can also serve as a readily available Kubeflow cluster
setup for giving demos.
* Create the directory demo within the github_issue_summarization example
to contain all the required files.
* Add a makefile to make building the image work.
* The ksonnet app for the public demo was previously stored here
https://github.com/kubeflow/testing/tree/master/deployment/ks-app
* Fix the uiservice account.
* Address comments.
Michelle Casbon
Jeremy Lewi