Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Add integration test for filtering #1155

Closed
rileyjbauer opened this issue Apr 12, 2019 · 1 comment
Closed

Add integration test for filtering #1155

rileyjbauer opened this issue Apr 12, 2019 · 1 comment
Assignees
@rileyjbauer
Labels
area/testing kind/bug priority/p0

Comments

@rileyjbauer
Copy link
Contributor

Similar to #1152
A regression was introduced into the backend that broke filtering. One of the ways that the bug manifested was in the archive page no longer working on the frontend #1150 since it operates using filters, and this was not caught by any tests.

#1150 was fixed by #1151

There should be an integration test for this, possibly a frontend integration test.

Perhaps there is a gap in the backend tests as well though?
@rileyjbauer rileyjbauer self-assigned this Apr 12, 2019
@rileyjbauer
Copy link
Contributor Author

Filtering is covered now as of #1175 by the helloworld frontend integration test

magdalenakuhn17 pushed a commit to magdalenakuhn17/pipelines that referenced this issue Oct 22, 2023
@adriangonz
Add support for MLServer in the SKLearn predictor (kubeflow#1155)
82ec9ce 
* Add ginkgo suite support to v1beta1 package

* Start adding MLServer predictor spec

* Add MLServer config to PredictorsConfig

* Add default config for MLServer

* Add defaulter

* Add GetContainer method

* Change comment

* Add MLServer to IS spec

* Remove comment

* Move MLServer to SKLearn predictor

* Remove other references to MLServer predictor

* Use strconv instead of fmt

* Add MLServer constants (to use later in XGBoost)

* Add defaults for missing model-settings.json

* Add SKLearn example for v1beta1

* Default model URI to models mount path

* Point to Seldon's GCS to use a newer version of SKLearn

* Update resource

* Bump version to 0.1.2

* Add registry prefix

* Fix docs

* Fix a couple references on the README

* Add protocol field to predictorspec

* Add SKLearnV2 predictor

* Default predictor to V1 protocol

* Build v1 or v2 container depending on flag

* Add tests for V1

* Fix image logic

* Add protocolVersion flag to example

* re-generate CRD

* Change configmap structure for sklearn predictor

* Add new structure to test overlay

* Add test case for SKLearn v2

* Add missing license block

* Re-generate Python SDK to add ProtocolVersions object

* Fix test case for V2

* Re-generate SDK to fix wrong test
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees

@rileyjbauer rileyjbauer

Labels
area/testing kind/bug priority/p0
Projects
None yet
Milestone
No milestone
Development

No branches or pull requests
2 participants
@vicaire @rileyjbauer