* 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
* Update model inference wrapping to use S2I and update docs
* Add s2i reference in docs
* Fix typo highlighted in review
* Add pyLint annotation to allow protected-access on keras make predict function method
* Create a test for submitting the TFJob for the GitHub issue summarization example.
* This test needs to be run manually right now. In a follow on PR we will
integrate it into CI.
* We use the image built from Dockerfile.estimator because that is the image
we are running train_test.py in.
* Note: The current version of the code now requires Python3 (I think this
is due to an earlier PR which refactored the code into a shared
implementation for using TF estimator and not TF estimator).
* Create a TFJob component for TFJob v1beta1; this is the version
in KF 0.4.
TFJob component
* Upgrade to v1beta to work with 0.4
* Update command line arguments to match the versions in the current code
* input & output are now single parameters rather then separate parameters
for bucket and name
* change default input to a CSV file because the current version of the
code doesn't handle unzipping it.
* Use ks_util from kubeflow/testing
* Address comments.
* Setup continuous building of Docker images and testing for GH Issue Summarization Example.
* This is the first step in setting up a continuously running CI test.
* Add support for building the Docker images using GCB; we will use GCB
to trigger the builds from our CI system.
* Make the Makefile top level (at root of GIS example) so that we can
easily access all the different resources.
* Add a .gitignore file to avoid checking in the build directory used by
the Makefile.
* Define an Argo workflow to use as the E2E test.
Related to #92: E2E test & CI for github issue summarization
* Trigger the test on pre & post submit
* Dockerfile.estimator don't install the data_download.sh script
* It doesn't look like we are currently using data_download.sh in the
DockerImage
* It looks like it only gets used vias the ksonnet job which mounts the
script via a config map
* Copying data_download.sh to the Docker image is currently weird
given the organization of the Dockerfile and context.
* Copy the test_data to the Docker images so that we can run the test
inside the images.
* Invoke the python unittest for training from our CI system.
* In a follow on PR we will update the test to emit a JUnit XML file to
report results to prow.
* Fix image build.
* 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.
* Add component parameters
Add model_url & port arguments to flask app
Add service_type, image, and model_url parameters to ui component
Fix problem argument in tensor2tensor component
* Fix broken UI component
Fix broken UI component structure by adding all, service, & deployment parts
Add parameter defaults for tfjob to resolve failures deploying other components
* Add missing imports in flask app
Fix syntax error in argument parsing
Remove underscores from parameter names to workaround ksonnet bug #554: https://github.com/ksonnet/ksonnet/issues/554
* Fix syntax errors in t2t instructions
Add CPU image build arg to docker build command for t2t-training
Fix link to ksonnet app dir
Correct param names for tensor2tensor component
Add missing params for tensor2tensor component
Fix apply command syntax
Swap out log view pod for t2t-master instead of tf-operator
Fix link to training with tfjob
* Fix model file upload
Update default params for tfjob-v1alpha2
Fix build directory path in Makefile
* Resolve lint issues
Lines too long
* Add specific image tag to tfjob-v1alpha2 default
* Fix defaults for training output files
Update image tag
Add UI image tag
* Revert service account secret details
Update associated readme
* Make it easier to demo serving and run in Katacoda
* Allow the model path to be specified via environment variables so that
we could potentially load the model from PVC.
* Continue to bake the model into the image so that we don't need to train
in order to serve.
* Parameterize download_data.sh so we could potentially fetch different sources.
* Update the Makefile so that we can build and set the image for the serving
component.
* Fix lint.
* Update the serving docs.
* Support training using a PVC for the data.
* This will make it easier to run the example on Katacoda and non-GCP platforms.
* Modify train.py so we can use a GCS location or local file paths.
* Update the Dockerfile. The jupyter Docker images and had a bunch of
dependencies removed and the latest images don't have the dependencies
needed to run the examples.
* Creat a tfjob-pvc component that trains reading/writing using PVC
and not GCP.
* * Address reviewer comments
* Ignore changes to the ksonnet parameters when determining whether to include
dirty and sha of the diff in the image. This way we can update the
ksonnet app with the newly built image without it leading to subsequent
images being marked dirty.
* Fix lint issues.
* Fix lint import issue.
* Add setup scripts & github token param
* Clarify instructions
Add pointers to resolution for common friction points of new cluster
setup: GitHub rate limiting and RBAC permissions
Setup persistent disk before Jupyterhub so that it is only setup once
Clarify instructions about copying trained model files locally
Add version number to frontend image build
Add github_token ks parameter for frontend
* Change port to 8080
Fix indentation of bullet points
* Fix var name & link spacing
* Update description of serving script
* Use a single ksonnet environment
Move ksonnet app out of notebooks subdirectory
Rename ksonnet app to ks-kubeflow
Update instructions & scripts
Remove instructions to delete ksonnet app directory
* Remove github access token
* Distributed training using tensor2tensor
* Use a transformer model to train the github issue summarization
problem
* Dockerfile for building training image
* ksonnet component for deploying tfjob
Fixes https://github.com/kubeflow/examples/issues/43
* Fix lint issues
* Rename issue_summarization.py to IssueSummarization.py
* The module name is supposed to be the same as the class name
* Fix the predict method signature
* Fix lint
* Github Issue Summarization - Train using TFJob
* Create a Dockerfile to build the image for tf-job
* Create a manifest to deploy the tf-job
* Create instructions on how to do all of this
Fixes https://github.com/kubeflow/examples/issues/43
* Address comments
* Add gcloud commands
* Add ks app
* Update Dockerfile base image
* Python train.py fixes
* Remove tfjob.yaml as it is replaced by ksonnet app
* Remove plot_model_history as it is not required for tfjob training
* Don't change WORKDIR
* Address reviewer comments
* Fix links
* Fix lint issues using yapf
* Sort imports
* Add .pylintrc
* Resolve lint complaints in agents/trainer/task.py
* Resolve lint complaints with flask app.py
* Resolve linting issues
Remove duplicate seq2seq_utils.py from workflow/workspace/src
* Use python 3.5.2 with pylint to match prow
Put pybullet import back into agents/trainer/task.py with a pylint ignore statement
Use main(_) to ensure it works with tf.app.run
* Add file copy instructions after training
Fix broken link in cluster setup
Fix broken env variable in Training notebook
Change notebook name from Tutorial to Training
* Fix app selector value
* Fix folder link
* Add detail to cluster setup instructions
Add a link to the image for this example.
In Tutorial.ipynb, move mounted directory into a variable to help avoid collisions on shared clusters.
* Create a end-to-end kubeflow example using seq2seq model (4/n)
* Move from a custom tornado server to a seldon-core model
Related to #11
* Update to use gcr.io registry for serving image
Amy
cliveseldon
Jeremy Lewi
Michelle Casbon
Jeremy Lewi
Ankush Agarwal
Hamel Husain
Michelle Casbon