Update xgboost_synthetic to 0.7 #655
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Saw following error with image: gcr.io/kubeflow-images-public/tensorflow-1.14.0-notebook-cpu:v0.7.0 NameError Traceback (most recent call last) in init(self, model_file) in create_execution(self) NameError: name 'metadata' is not defined |
* Related to kubeflow#655 update xgboost_synthetic to use workload identity * Related to to kubeflow#665 no signal about xgboost_synthetic * We need to update the xgboost_synthetic example to work with 0.7.0; e.g. workload identity * This PR focuses on updating the test infra and some preliminary updates the notebook * More fixes to the test and the notebook are probably needed in order to get it to actually pass * Update job spec for 0.7; remove the secret and set the default service account. * This is to make it work with workload identity * Instead of using kustomize to define the job to run the notebook we can just modify the YAML spec using python. * Use the python API for K8s to create the job rather than shelling out. * Notebook should do a 0.7 compatible check for credentials * We don't want to assume GOOGLE_APPLICATION_CREDENTIALS is set because we will be using workload identity. * Take in repos as an argument akin to what checkout_repos.sh requires * Convert xgboost_test.py to a pytest. * This allows us to mark it as expected to fail so we can start to get signal without blocking * We also need to emit junit files to show up in test grid. * Convert the jsonnet workflow for the E2E test to a python function to define the workflow. * Remove the old jsonnet workflow.
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Just another point to note: |
… 0.7.0 (#666) * Update xgboost_synthetic test infra to use pytest and pyfunc. * Related to #655 update xgboost_synthetic to use workload identity * Related to to #665 no signal about xgboost_synthetic * We need to update the xgboost_synthetic example to work with 0.7.0; e.g. workload identity * This PR focuses on updating the test infra and some preliminary updates the notebook * More fixes to the test and the notebook are probably needed in order to get it to actually pass * Update job spec for 0.7; remove the secret and set the default service account. * This is to make it work with workload identity * Instead of using kustomize to define the job to run the notebook we can just modify the YAML spec using python. * Use the python API for K8s to create the job rather than shelling out. * Notebook should do a 0.7 compatible check for credentials * We don't want to assume GOOGLE_APPLICATION_CREDENTIALS is set because we will be using workload identity. * Take in repos as an argument akin to what checkout_repos.sh requires * Convert xgboost_test.py to a pytest. * This allows us to mark it as expected to fail so we can start to get signal without blocking * We also need to emit junit files to show up in test grid. * Convert the jsonnet workflow for the E2E test to a python function to define the workflow. * Remove the old jsonnet workflow. * Address comments. * Fix issues with the notebook * Install pip packages in user space * 0.7.0 images are based on TF images and they have different permissions * Install a newer version of fairing sdk that works with workload identity * Split pip installing dependencies out of util.py and into notebook_setup.py * That's because util.py could depend on the packages being installed by notebook_setup.py * After pip installing the modules into user space; we need to add the local path for pip packages to the python otherwise we get import not found errors.
* install newer version of fairing * modify preprocessor to use custom dockerfile * use newer 0.7 base image. * Fix endpoint. Related to: kubeflow#673 model doesn't deploy its crash looping Related to kubeflow#655 update example to work with 0.7
#682) * Fix issues with the xgboost_synthetic example and deploying the model. * install newer version of fairing * modify preprocessor to use custom dockerfile * use newer 0.7 base image. * Fix endpoint. Related to: #673 model doesn't deploy its crash looping Related to #655 update example to work with 0.7 * Add some comments to the notebook.
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In 0.7 we will use workload identity.
As such notebooks should no longer need to use/set GOOGLE_APPLICATION_CREDENTIALS
The notebook
https://github.com/kubeflow/examples/blob/master/xgboost_synthetic/build-train-deploy.ipynb
Is currently checking GOOGLE_APPLICATION_CREDENTIALS we will need to update that code to work with workload identity.
P0 because this is part of our demo script for 0.7.
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