* Updated Dockerfile.traning to use latest tensorflow
and tensorflow object detetion api.
* Updated tf-training-job component and added a chief
replica spec
* Corrected some typos and updated some instructions
* Replace double quotes for field values (ks convention)
* Recreate the ksonnet application from scratch
* Fix pip commands to find requirements and redo installation, fix ks param set
* Use sed replace instead of ks param set.
* Add cells to first show JobSpec and then apply
* Upgrade T2T, fix conflicting problem types
* Update docker images
* Reduce to 200k samples for vocab
* Use Jupyter notebook service account
* Add illustrative gsutil commands to show output files, specify index files glob explicitly
* List files after index creation step
* Use the model in current repository and not upstream t2t
* Update Docker images
* Expose TF Serving Rest API at 9001
* Spawn terminal from the notebooks ui, no need to go to lab
* Cherry pick changes to PredictionDoFn
* Disable lint checks for cherry picked file
* Update TODO and notebook install instructions
* Restore CUSTOM_COMMANDS todo
* Add a Jupyter notebook to be used for Kubeflow codelabs
* Add help command for create_function_embeddings module
* Update README to point to Jupyter Notebook
* Add prerequisites to readme
* Update README and getting started with notebook guide
* [wip]
* Update noebook with BigQuery previews
* Update notebook to automatically select the latest MODEL_VERSION
* Use the nicer tf.gfile interface for search index creation
* Update documentation and more maintainable interface to search server
* Add ability to control number of outputs
* Serve React UI from the Flask server
* Update Dockerfile for the unified server and ui
* adding batch-predict on GPU example
* Sync with TF-serving GPU example.
* adding visualization instructions
* change the title of readme.md
* changes according to the review comments from jlewi
* Replace the links to personal project with the one in kubeflow-example project in the yaml file
* change the procedure to build images
* polish the md file
* some minor md change
* fix a broken gs link
* fix more merge errors
* Upgrade TFJob and Ksonnet app
* Container name should be tensorflow. See #563.
* Working single node training and serving on Kubeflow
* Add issue link for fixme
* Remove redundant create secrets and use Kubeflow provided secrets
* Added Ksonnet prototypes to parametrize old yaml files
* Modified instructions
* Added tf-training-job component
* Removed yaml manifest files
Modified serving instructions
* Consolidate get-data and decompression jobs
* Deleted registry and prototypes
* Added components to ks-app dir
* Modified instructions
* Fixed references to user guide page
Improved instructions
* General improvements to components and instructions
* Removed obj-detection.libsonnet file
* used specific params in export-graph and create-tf-record
instead of list params like 'args' and 'command'
* Improved instructions and removed references to yaml files
* Update T2T problems to workaround memory limitations
* Add max_samples_for_vocab to prevent memory overflow
* Fix a base URL to download data from, sweet spot for max samples
* Convert class variables to class properties
* Fix lint errors
* Use Python2/3 compatible code for StringIO
* Fix lint errors
* Fix source data files format
* Move to Text2TextProblem instead of TranslateProblem
* Update details for num_shards and T2T problem dataset
* Update to a new dataflow package
* [WIP] updating docstrings, fixing redundancies
* Limit the scope of Github Transform pipeline, make everything unicode
* Add ability to start github pipelines from transformed bigquery dataset
* Upgrade batch prediction pipeline to be modular
* Fix lint errors
* Add write disposition to BigQuery transform
* Update documentation format
* Nicer names for modules
* Add unicode encoding to parsed function docstring tuples
* Use Apache Beam options parser to expose all CLI arguments
* Refactor the dataflow package
* Create placeholder for new prediction pipeline
* [WIP] add dofn for encoding
* Merge all modules under single package
* Pipeline data flow complete, wip prediction values
* Fallback to custom commands for extra dependency
* Working Dataflow runner installs, separate docker-related folder
* [WIP] Updated local user journey in README, fully working commands, easy container translation
* Working Batch Predictions.
* Remove docstring embeddings
* Complete batch prediction pipeline
* Update Dockerfiles and T2T Ksonnet components
* Fix linting
* Downgrade runtime to Python2, wip memory issues so use lesser data
* Pin master to index 0.
* Working batch prediction pipeline
* Modular Github Batch Prediction Pipeline, stores back to BigQuery
* Fix lint errors
* Fix module-wide imports, pin batch-prediction version
* Fix relative import, update docstrings
* Add references to issue and current workaround for Batch Prediction dependency.
* Add auto-downloads for the data
* Make top() a no-op, working export
* Fix lint errors
* Integrate NMSlib server with TF Serving
* Clarify data URLs purpose
* 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
Puneith Kaul
Puneith Kaul
Daniel Castellanos
Sanyam Kapoor
Michelle Casbon
Lun-Kai Hsu
Ankush Agarwal
Sanyam Kapoor
Richard Liu
Sam Shi
Roy Xue
Pete MacKinnon
Jeremy Lewi
Daniel Castellanos