* Fix#272Fix#272 where the `create-pet-record-job` pod produces this error: `models/research/object_detection/data/pet_label_map.pbtxt; No such file or directory`
* Update create-pet-record-job.jsonnet
* Fix gh-demo.kubeflow.org and make it easy to setup.
* Our public demo of the GitHub issue summarization example
(gh-demo.kubeflow.org) is down. It was running in one of our dev
clusters and with the the churn in dev clusters it ended up getting deleted.
* To make it more stable lets move it to project kubecon-gh-demo-1
and create a separate cluster for running it.
This cluster can also serve as a readily available Kubeflow cluster
setup for giving demos.
* Create the directory demo within the github_issue_summarization example
to contain all the required files.
* Add a makefile to make building the image work.
* The ksonnet app for the public demo was previously stored here
https://github.com/kubeflow/testing/tree/master/deployment/ks-app
* Fix the uiservice account.
* Address comments.
* add financial time series example
* fix ReadMe comments
* fix PyLint remarks
* clean up based on PR remarks
* Completing docstrings and fixing PR remarks
* Add tensorboard and check in vendor for the code search example.
* * Remove the default env; when I ran ks show I got errors but
removing it and adding a fresh env worked. It also won't point to
the correct cluster for users.
* Remove reviewers who are already approvers
Remove ScorpioCPH and zjj2wry due to inactivity (no PRs or comments on PRs).
* Add zjj2wry back on request
* Update demo script
Update demo script to include deploy script and notebook created by @drscott173
Simplify by removing unnecessary commands
Use default namespace instead of kubeflow
* Add yelp notebook readme
* Add cluster creation commands
Add instructions for highlighting changes resulting from each command
* Upgrade ks dir to 0.12.0
* Upgrade kubeflow to v0.2.0-rc.1
Use https://github.com/kubeflow/kubeflow/blob/master/scripts/upgrade_ks_app.py
to upgrade ks registry
Add t2tcpu-v1alpha2 component
* Rename t2tcpu-v1alpha2 -> t2tcpu
Rename t2tcpu -> t2tcpu-v1alpha1 and t2tcpu-v1alpha2 -> t2tcpu
Update demo_setup/README.md to reflect ks v0.12.0
Update REPO_PATH in demo_setup/kubeflow-demo-base.env
Update initialClusterVersion in k8s cluster creation script to 1.10.6-gke.2
Remove quotation marks from serving.deployHttpProxy so that it is parsed as a boolean instead of string
* Rename t2tgpu & t2ttpu
Rename t2tgpu -> t2tgpu-v1alpha1 and add t2tgpu-v1alpha2 as t2tgpu
Rename t2ttpu -> t2ttpu-v1alpha1 and add t2ttpu-v1alpha2 as t2ttpu
Resolve jsonnet parsing issues
* Upgrade kubeflow to v0.2.4
Add gke environment
* Add instructions for creating TPU clusters
* Replace hard-coded value with env var
* Update kf version to v0.2.4 in env var file
* Add non-gke requirements to t2tcpu component
Sync t2tgpu with t2tcpu
Remove non-gke statements from t2ttpu component
Add k8s v1.10.6 to minikube start command
* Fix bug with non-gke environment setup in t2t
Add service account setup and k8s secret creation instructions for serving & UI
* Single cluster with GPU & TPU
Add creation script for single cluster with access to CPU, GPU, & TPU
Update GPU driver installation to k8s-1.10
* Remove v1alpha1 components
* Update parameter values for t2t components
Increase disk size for minikube cluster creation since 0.2.4 is larger
Update gke cluster creation command
* Update TPU annotation to TF 1.9
* Update kf version to v0.2.5
Update tfJobImage version to v20180809-d2509aa
* Add estimator example for github issues
This is code input for doc about writing Keras for tfjob.
There are few todos:
1. bug in dataset injection, can't raise number of steps
2. intead of adding hostpath for data, we should have quick job + pvc
for this
* pyling
* wip
* confirmed working on minikube
* pylint
* remove t2t, add documentation
* add note about storageclass
* fix link
* remove code redundancy
* adress review
* small language fix
* new PR for XGBoost due to problems with history rewrite
* Update housing.py
* Update HousingServe.py
* Update housing.py
* added bitly
* removed test function
* reorder imports
* fix spaces
* fix spaces
* fixed lint errors
* renamed to xgboost_ames_housing
* 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
Konstantinos Samaras-Tsakiris