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Jeremy Lewi de17011066 Upgrade and fix the serving components. (#348) 
* Upgrade and fix the serving components.

* Install a new version of the TFServing package so we can use the new template.

* Fix the UI image. Use the same requirements file as for Dataflow so we are
consistent w.r.t the version of TF and Tensor2Tesnro.

* remove nms.libsonnet; move all the manifests into the actual component
  files rather than using a shared library.

* Fix the name of the TFServing service and deployment; need to use the same
  name as used by the front end server.

* Change the port of TFServing; we are now using the built in http server
  in TFServing which uses port 8500 as opposed to our custom http proxy.

* We encountered an error importning nmslib; moving it to the top of the file
  appears to fix this.

* Fix lint.
 		2018-11-24 13:22:34 -08:00
..
demo Upgrade and fix the serving components. (#348) 2018-11-24 13:22:34 -08:00
docker Upgrade and fix the serving components. (#348) 2018-11-24 13:22:34 -08:00
kubeflow Upgrade and fix the serving components. (#348) 2018-11-24 13:22:34 -08:00
src Upgrade and fix the serving components. (#348) 2018-11-24 13:22:34 -08:00
.dockerignore Remove ksonnet registry from dockerignore file (#333) 2018-11-14 13:45:15 -08:00
.gitignore code search example make distributed training work; Create some components to train models (#317) 2018-11-08 16:13:01 -08:00
Makefile Upgrade and fix the serving components. (#348) 2018-11-24 13:22:34 -08:00
README.md Upgrade notebook commands and other relevant changes (#229) 2018-08-20 16:35:07 -07:00
code-search.ipynb update instruction with proper namespace (#307) 2018-11-05 20:47:46 -08:00
developer_guide.md Use conditionals and add test for code search (#291) 2018-11-02 09:52:11 -07:00

README.md

Code Search on Kubeflow

This demo implements End-to-End Code Search on Kubeflow.

Prerequisites

NOTE: If using the JupyterHub Spawner on a Kubeflow cluster, use the Docker image gcr.io/kubeflow-images-public/kubeflow-codelab-notebook which has baked all the pre-prequisites.

  • Kubeflow Latest This notebook assumes a Kubeflow cluster is already deployed. See Getting Started with Kubeflow.

  • Python 2.7 (bundled with pip) For this demo, we will use Python 2.7. This restriction is due to Apache Beam, which does not support Python 3 yet (See BEAM-1251).

  • Google Cloud SDK This example will use tools from the Google Cloud SDK. The SDK must be authenticated and authorized. See Authentication Overview.

  • Ksonnet 0.12 We use Ksonnet to write Kubernetes jobs in a declarative manner to be run on top of Kubeflow.

Getting Started

To get started, follow the instructions below.

NOTE: We will assume that the Kubeflow cluster is available at kubeflow.example.com. Make sure you replace this with the true FQDN of your Kubeflow cluster in any subsequent instructions.

  • Spawn a new JupyterLab instance inside the Kubeflow cluster by pointing your browser to https://kubeflow.example.com/hub and clicking "Start My Server".

  • In the Image text field, enter gcr.io/kubeflow-images-public/kubeflow-codelab-notebook:v20180808-v0.2-22-gcfdcb12. This image contains all the pre-requisites needed for the demo.

  • Once spawned, you should be redirected to the Jupyter Notebooks UI.

  • Spawn a new Terminal and run

    $ git clone --branch=master --depth=1 https://github.com/kubeflow/examples

    This will create an examples folder. It is safe to close the terminal now.

  • Navigate back to the Jupyter Notebooks UI and navigate to examples/code_search. Open the Jupyter notebook code-search.ipynb and follow it along.

Acknowledgements

This project derives from hamelsmu/code_search.