* Add e2e test for xgboost housing example
* fix typo
add ks apply
add [
modify example to trigger tests
add prediction test
add xgboost ks param
rename the job name without _
use - instead of _
libson params
rm redudent component
rename component in prow config
add ames-hoursing-env
use - for all names
use _ for params names
use xgboost_ames_accross
rename component name
shorten the name
change deploy-test command
change to xgboost-
namespace
init ks app
fix type
add confest.py
change path
change deploy command
change dep
change the query URL for seldon
add ks_app with seldon lib
update ks_app
use ks init only
rerun
change to kf-v0-4-n00 cluster
add ks_app
use ks-13
remove --namespace
use kubeflow as namespace
delete seldon deployment
simplify ks_app
retry on 503
fix typo
query 1285
move deletion after prediction
wait 10s
always retry till 10 mins
move check to retry
fix pylint
move clean-up to the delete template
* set up xgboost component
* check in ks component& run it directly
* change comments
* add comment on why use 'ks delete'
* add two modules to pylint whitelist
* ignore tf_operator/py
* disable pylint per line
* reorder import
* Create an E2E test for TFServing using the rest API
* We use the pytest framework because
1. it has really good support for using command line arguments
2. can emit junit xml file to report results to prow.
Related to #270: Create a generic test runner
* Address comments.
* Fix lint.
* Add retries to the prediction.
* Add some comments.
* Fix model path.
* * Fix the workflow labels
* Set the K8s service name correctly on the test.
* Fix the workflow.
* Fix lint.
* Add the TFServing component
* Create TFServing components.
* The model.py code doesn't appear to be exporting a model in saved model
format; it was a missing a call to export.
* I'm not sure how this ever worked.
* It also looks like there is a bug in the code in that its using the cnn input fn even if the model is the linear one. I'm going to leave that as is for now.
* Create a namespace for each test run; delete the namespace on teardown
* We need to copy the GCP service account key to the new namespace.
* Add a shell script to do that.
* Update training to use Kubeflow 0.4 and add testing.
* To support testing we need to create a ksonnet template to train
the model so we can easily subsitute in different parameters during
training.
* We create a ksonnet component for just training; we don't use Argo.
This makes the example much simpler.
* To support S3 we add a generic ksonnet parameter to take environment
variables as a comma separated list of variables. This should make it
easy for users to set the environment variables needed to talk to S3.
This is compatible with the existing Argo workflow which supports S3.
* By default the training job runs non-distributed; this is because to
run distributed the user needs a shared filesystem (e.g. S3/GCS/NFS).
* Update the mnist workflow to correctly build the images.
* We didn't update the workflow in the previous example to actually
build the correct images.
* Update the workflow to run the tfjob_test
* Related to #460 E2E test for mnist.
* Add a parameter to specify a secret that can be used to mount
a secret such as the GCP service account key.
* Update the README with instructions for GCS and S3.
* Remove the instructions about Argo; the Argo workflow is outdated.
Using Argo adds complexity to the example and the thinking is to remove
that to provide a simpler example and to mirror the pytorch example.
* Add a TOC to the README
* Update prerequisite instructions.
* Delete instructions for installing Kubeflow; just link to the
getting started guide.
* Argo CLI should no longer be needed.
* GitHub token shouldn't be needed; I think that was only needed
for ksonnet to pull the registry.
* * Fix instructions; access keys shouldn't be stored as ksonnet parameters
as these will get checked into source control.
Zhenghui Wang
Jeremy Lewi