* Remove modules from .pylintrc
* Add lint inline exceptions
* Add lint inline exceptions as all as the specific exception is not available for Pylint 1.8
* Fix string formatting logging message and remove unnecessary Pylint exception
* Update app.yaml with correct environment details
* Update readme for xgboost-synthetic and remove outdated yaml file.
* Update the class name to be more general.
* Update readme.
* Set google_application_credentials in the notebook.
* Install fairing from master branch.
* Do not set credentials again.
* Update readme.
* Install required pip packages not included in the base package.
* Use Kaniko builder to build the base image first.
* Directly install packages from requirements.txt to be more flexible.
* Add xgboost-ames-housing demo from Kubecon EU 2019.
* fix links in the .ipynb in the xgboost-ames-housing demo
* update to the xgboost demo example from kubecon
- move example to its own directory
- remove unnecessarry files
- modify util and update notebook
* change the names related to kubecon and update readme
* use fairing instead of own fairing_util in the notebook
* remove fairing_util and move the remaining to util instead
* update synthetic data example as comments
- generalize yaml
- remove updating github procedures
- update readme
- rename files
* fix pylint.
* fix pylint.
* Add build and test presubmit jobs for Pytorch nmist example
Keep postsubmit jobs as original release job to push images to examples registry
* Refactor all jobs like mnist and GIS, will drop using release jobs
* Implement test scripts and Ksonnet artifacts from mnist example to enable E2E tests
* Remove release components as they are no longer used
* Refactor YAML manifests as Ksonnet components
* Update documentation to submit training jobs from Ksonnet
* Updated to point to correct component and refactor to PytorchJob
* Add seldon image build
Add train CPU and GPU in jsonnet to build workflow
Add Dockerfile.ksonnet and entrypoint
* Commented out calls to tf-util until
https://github.com/kubeflow/pytorch-operator/issues/108 is implemented
* Refactor to PytorchJob
* Add seldon image build
Add train CPU and GPU in jsonnet to build workflow
Add Dockerfile.ksonnet and entrypoint
* Refactor to PytorchJob
* Rename workflow to avoid dns issue with "_"
* Add TODO note to convert to GRPC
* Rename workflow to avoid dns issue with "_"
* Rename workflow to avoid dns issue with "_"
* Fix path to build Seldon image in Makefile
* Fix tabs in Makefile
* Fix tabs in Makefile
* Fix rule in Makefile
* Add sleep in Makefile to wait for docker ps
* Change node worker image to have docker
* Remove seldon image step from Makefile
Add steps to wrap model with Seldon
Add boolean flag to build Seldon steps
* Add step id build- in jsonnet
* Skip pull step for Seldon
* Fix wait for in Seldon build
* Fix lint errors
* Set useimagecache to false first time the pipeline is executed to avoid error
* Set contextDir as absolute path for Seldon step
* Remove unnecessary argument and Dockerfile in Seldon step
* Add absolute path for build in Seldon steps
* Include absolute path inside jsonnet hardcoded to GCB /workspace/
Remove setting rootDir from Makefile
* Update images with new naming from E2E tests
* Change test-worker image version
* Update images with new naming from E2E tests
* Set useimagecache to true now that we have first images built
* Fix cachelist in Seldon build
* Fix cachelist in Seldon build
* Leverage tf-operator test framework for test_runner
As per https://github.com/kubeflow/pytorch-operator/issues/108
* Consolidate testing imports
Rename testing package as https://github.com/kubeflow/tf-operator/pull/945
Added correct path to import test framework from tf-operator
* Add test framework in PYTHONPATH in build_template
* Remove old release jobs to build images
* Update stepimage to same as GIS example
* Bump up supported Pytorch operator versions from v1alpha2/v1beta1 to v1beta1/v1beta2 to support Kubeflow 0.5
- Refactor training manifests from v1alpha2 to v1beta2
- Update documents
* Update KF cluster version to latest to run tests
* Update KF cluster zone
* Add pylint exception while importing test_runner class from tf-operator
* Pass dummy tests to train, deploy and predict
Remove no longer used test_data and conftest
* Pass dummy tests to train, deploy and predict
Remove no longer used test_data and conftest
* use gcs client libs to copy checkpoint dir
* more minor cleanup, use tagged image, use newer pipeline param spec. syntax.
pylint cleanup.
added set_memory_limit() to notebook pipeline training steps.
modified the pipelines definitions to use the user-defined params as defaults.
* put a retry loop around the copy_blob
* initial import of Pipelines Github issue summarization examples & lab
* more linting/cleanup, fix tf version to 1.12
* bit more linting; pin some lib versions
* last? lint fixes
* another attempt to fix linting issues
* ughh
* changed test cluster config info
* update ktext package in a test docker image
* hmm, retrying fix for the ktext package update
* Remove top level approvers and reviewers who aren't very active.
* We should also be building out the per directory OWNERs files to do
a better job auto-assigning reviewers.
Rebecca McFadden
Xiao Kou
David Sabater Dinter
Chun-Hsiang Wang
Chun-Hsiang Wang
Zane Durante
Jin Chi He
Nick Harvey
Michelle Casbon
Sarah Maddox
Iman Tabrizian
Svendegroote91
Zane Durante
Karthik Vadla
Amy
ssivart
Amy
Hamel Husain
Daniel Castellanos
Jeremy Lewi
Derek Hao Hu
Daniel Sanche
zabbasi