* Some of the code is copied over from https://github.com/kubeflow/katib/tree/master/examples/GKEDemo
* I think it makes sense to centralize all the code in a single place.
* Update the controller program (git-issue-summarize-demo.go) so that can
specify the Docker image containing the training code.
* Create a ksonnet deployment for running the controller on the cluster.
* The HP tuning job isn't functional here's an incomplete list of issues
* The training jobs launched fail because they don't have GCP credentials
so they can't download the data.
* We don't actually extract and report metrics back to Katib.
Related to: kubeflow/katib#116
* Initialize search UI. Needs connection to search service
* Fix page title
* Add component for code search results, dummy values for now
* Fix title and manifest
* Add mock loading UI. Need to fill in real API results
* Wrap application into Dockerfile
* Added tutorial for object detection distributed training
Added steps on how to leverage kubeflow tooling to
submit a distributed object detection training job
in a small kubernetes cluster (minikube, 2-4 node cluster)
* Added Jobs to prepare the training data and model
* Updated instructions
* fixed typos and added export tf graph job
* Fixed paths in jobs and instructions
* Enhanced instructions and re-arranged folder structure
* Updated links to kubeflow user guide documentation
* Add similarity transformer body
* Update pipeline to Write a single CSV file
* Fix lint errors
* Use CSV writer to handle formatting rows
* Use direct transformer encoding methods with variable scopes
* Complete end-to-end training with new model and problem
* Read from mutliple csv files
* 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
* Update the Docker image for T2T to use a newer version of T2T library
* Add parameters to set the GCP secret; we need GCP credentials to
read from GCS even if reading a public bucket. We default
to the parameters that are created automatically in the case of a GKE
deployment.
* Create a v1alpha2 template for the job that uses PVC.
* Update the GH summarization example to Kubeflow 0.2 and TFJob v1alpha2.
* Upgrade the ksonnet app to Kubeflow 0.2 rc.1
* Add the examples package.
* Add a .gitignore file and ignore all environments so that we won't pick
up people's testing environments.
* Add tfjob-v1alpha2 component; this trains the model using Keras using
TFJob v1alpha2.
* Update the parameters so that we use the GCP secrets created as part
of the Kubeflow deployment.
* Remove jlewi environment.
* Verified that training ran successfully and outputted a model to GCS
* There was an error about some missing arguments to a logging statement
but this can be ignored although it would be good to fix.
* Started working on T2T v1alpha2. Seems to be messing up the app.
* Update the v1alpha2 template for the tensor2tensor job but it looks like
there is an error
2018-06-29 17:45:23,369] Found unknown flag: --problem=github_issue_summarization_problem
Traceback (most recent call last):
File "/home/jovyan/.conda/bin/t2t-trainer", line 32, in <module>
tf.app.run()
File "/home/jovyan/.conda/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 126, in run
_sys.exit(main(argv))
File "/home/jovyan/.conda/bin/t2t-trainer", line 28, in main
t2t_trainer.main(argv)
File "/home/jovyan/.conda/lib/python3.6/site-packages/tensor2tensor/bin/t2t_trainer.py", line 334, in main
exp_fn = create_experiment_fn()
File "/home/jovyan/.conda/lib/python3.6/site-packages/tensor2tensor/bin/t2t_trainer.py", line 158, in create_experiment_fn
problem_name=get_problem_name(),
File "/home/jovyan/.conda/lib/python3.6/site-packages/tensor2tensor/bin/t2t_trainer.py", line 115, in get_problem_name
problems = FLAGS.problems.split("-")
AttributeError: 'NoneType' object has no attribute 'split'
* Add new TF-Serving component with sample task
* Unify nmslib and t2t packages, need to be cohesive
* [WIP] update references to the package
* Replace old T2T problem
* Add representative code for encoding/decoding from tf serving service
* Add rest API port to TF serving (replaces custom http proxy)
* Fix linting
* Add NMSLib creator and server components
* Add docs to CLI module
* 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
* Add jobs derived from t2t component, GCP credentials assumed
* Add script to create IAM role bindings for Docker container to use
* Fix names to hyphens
* Add t2t-exporter wrapper
* Fix typos
* A temporary workaround for tensorflow/tensor2tensor#879
* Complete working pipeline of datagen, trainer and exporter
* Add docstring to create_secrets.sh
* Add a utility python package for indexing and serving the index
* Add CLI arguments, conditional GCS download
* Complete skeleton CLIs for serving and index creation
* Fix lint issues
* [WIP] initialize ksonnet app
* Push images to GCR
* Upgrade Docker container to run T2T entrypoint with appropriate env vars
* Add a tf-job based t2t-job
* Fix GPU parameters
* New tensor2tensor problem for function summarization
* Consolidate README with improved docs
* Remove old readme
* Add T2T Trainer using Transformer Networks
* Fix missing requirement for t2t-trainer
* Proposed repo strategy
Define and describe example types (end-to-end, component-based, third-party hosted)
Define requirements for housing examples
Update list of ideas for additional examples
* Add get involved
* Move descriptions into CONTRIBUTING
Add application-specific category
Add clarifying details
* Code Search Preprocessing Pipeline
* Add missing pipeline execution to git tree
* Move the preprocessing step into its own package
* Add docstrings
* Fix pylint errors
* edit TF example readme
* prefix tutorial steps with a number for nicer display in repo
* fix typo
* edit steps 4 and 5
* edit docs
* add navigation and formatting edits to example
* Improvements to the tensor2tensor traininer for the GitHub summarization example.
* Simplify the launcher; we can just pass through most command line arguments and not
use environment variables and command line arguments.
* This makes it easier to control the job just by setting the parameters in the template
rather than having to rebuild the images.
* Add a Makefile to build the image.
* Replace the tensor2tensor jsonnet with a newer version of the jsonnet used with T2T.
* Address reviewer comments.
* Install pip packages as user Jovyan
* Rely on implicit string conversion with concatenation in template file.
* Add a component to run TensorBoard.
* Autoformate jsonnet file.
* * Set a default of "" for logDir; there's not a really good default location
because it will depend on where the data is stored.
* 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.
* This is the first step to doing training and serving using a PV as opposed
to GCS.
* This will make the sample easier to run anyhere and in particular on Katacoda.
* This currently would work as follows
User creates a PVC
ks apply ${ENV} -c data-pvc
User runs a K8s job to download the data to PVC
ks apply ${ENV} -c data-downloader
In subsequent PRs we will update the train and serve steps to load the
model from the PVC as opposed to GCS.
Related to #91
* 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
* Updates to the demo docs (notebook and readme) which were out-dated in multiple places
* Removed unused tools/ dir
* Update main readme to reference the example
* Inclusion of kubeflow vendor/ tf-job code
* Illustrates how logging and rendering to an attached volume can simplify the process of viewing logs with TensorHub and exploring
render outputs.
* Storage in user-space allows it to be sym-linked into directory tree
watched by TensorHub extension (which is running tensorboard --logdir=/home/jovyan)
* I anticipate this current approach to controlling volume mounts for NFS through
ksonnet to be replaced by doing so with python as I demonstrated in
the enhance example so I wouldn't lose sleep over the ksonnet
prototypes in this commit.
* Add awscli tools container.
* Add initial readme.
* Add argo skeleton.
* Run a an argo job.
* Artifact support and argo test
* Use built container (#3)
* Fix artifacts and secrets
* Add work in progress tfflow (#14)
* Add kvc deployment to workflow.
* Switch aws repo.
* wip.
* Add working tfflow job.
* Add sidecar that waits for MASTER completion
* Pass in job-name
* Add volumemanager info step
* Add input parameters to step
* Adds nodeaffinity and hostpath
* Add fixes for workflow (#17)
- Use correct images for worker and ps
- Use correct aws keys
- Change volumemanager to mnist
- Comment unused steps
- Fix volume mount to correct containers
* Fix hostpath for tfjob
* Download all mnist files
* added GCS stored artifacts comptability to Argo
* Add initial inference workflow. (#30)
* Initial serving step (#31)
* Adds fixes to initial serving step
* Ready for rough demo: Workflow in working state
* Move conflicting readme.
* Initial commit, everything boots without crashing.
* Working, with some python errors.
* Adding explicit flags
* Working with ins-outs
* Letting training job exit on success
* Adding documentation skeletion
* trying to properly save model
* Almost working
* Working
* Adding export script, refactored to allow model more reusability
* Starting documentation
* little further on docs
* More doc updates, fixing sleep logic
* adding urls for mnist data
* Removing download logic, it's to tied in with build-in tf examples.
* Added argo workflow instructions, minor cleanups.
* Adding mnist client.
* Fixing typos
* Adding instructions for installing components.
* Added ksonnet container
* Adding new entrypoint.
* Added helm install instructions for kvc
* doing things with variables
* Typos.
* Added better namespace support
* S3 refactor.
* Added missing region variables.
* Adding tensorboard support.
* Addding Container for Tensorboard.
* Added temporary flag, added install instructions for CLI.
* Removing invalid ksonnet environment.
* Updating readme
* Cleanup currently unused pieces
* Add missint cluster-role
* Minor cleanup.
* Adding more parameters.
* added changes to allow model to train on multiple workers and fixed some doc typos
* Adding flag to enable/disable model serving. Adding s3 urls as outputs for future querying, renaming info step.
* Adding seperate deployer workflow.
* Split serving working.
* Adding split workflow.
* More parameters.
* updates as to elson comments
* Revert "added changes to allow model to train on multiple workers and fixed s…"
* Initial working pure-s3 workflow.
* Removed wait sidecars.
* Remove unused flag.
* Added part two, minor doc fixes
* Inverted links...
* Adding diff.
* Fix url syntax
* Documentation updates.
* Added AWS Cli
* Parameterized export.
* Fixing image in s3 version.
* Fixed documentation issues.
* KVC snippet changes, need to find last working helm chart.
* Temporarily pinning kvc version.
* working master model and some doc typos fixes (#13)
* added changes to allow model to train on multiple workers and fixed some doc typos
* Adding flag to enable/disable model serving. Adding s3 urls as outputs for future querying, renaming info step.
* Adding seperate deployer workflow.
* Split serving working.
* Adding split workflow.
* More parameters.
* updates as to elson comments
* working master model and some doc typos
* fixes as to Elson
* Removign whitespace differences
* updating diff
* Changing parameters.
* Undoing whitespace.
* Changing termination policy on s3 version due to unknown issue.
* Updating mnist diff.
* Changing train steps.
* Syncing Demo changes.
* Update README.md
* Going S3-native for initial example. Getting rid of Master.
* Minor documentation tweaks, adding params, swapping aws cli for minio.
* Updating KVC version.
* Switching ksonnet repo, removing model name from client.
* Updating git url.
* Adding certificate hack to avoid RBAC errors.
* Pinning KVC to commit while working on PR.
* Updating version.
* Updates README with additional details (#14)
* Updates README with additional details
* Adding clarity to kubectl config commands
* Fixed comma placement
* Refactoring notes for github and kubernetes credentials.
* Forgot to add an overview of the argo template.
* Updating example based on feedback.
- Removed superflous images
- Clarified use of KVC
- Added unaltered model
- Variable cleanup
* Refactored grpc image into generic base image.
* minor cleanup of resubmitting section.
* Switching Argo deployment to ksonnet, conslidating install instructions.
* Removing old cruft, clarifying cluster requirements.
* [WIP] Switching out model (#15)
* Switching to new mnist example.
* Parameterized model, testing export.
* Got CNN model exporting.
* Attempting to do distributed training with Estimator, removed seperate export.
* Adding master back, otherwise Estimator complains about not having a chief.
* Switching to tf.estimator.train_and_evaluate.
* Minor path/var name refactor.
* Adding test data and new client.
* Fixed documentation to reflect new client.
* Getting rid of tf job shim.
* Removing KVC from example, renaming directory
* Modifying parent README
* Removed reference to export.
* Adding reference to export.
* Removing unused Dockerfile.
* Removing uneeded files, simplifying how to get status, refactor model serving workflow step.
* Renaming directory
* Minor doc improvements, removed extra clis.
* Making SSL configurable for clusters without secured s3 endpoints.
* Added a tf-user account for workflow. Fixed serving bug.
* Updating gke version.
* Re-ran through instructions, fixed errata.
* Fixing lint issues
* Pylint errors
* Pylint errors
* Adding parenthesis back.
* pylint Hacks
* Disabling argument filter, model bombs without empty arg.
* Removing unneeded lambdas
Jeremy Lewi
Sanyam Kapoor
Daniel Castellanos
Michelle Casbon
Maerville
Christopher Beitel
Puneith Kaul
Julien Stroheker
Carol Willing
Elson Rodriguez
Ankush Agarwal
Lun-Kai Hsu