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A repository to host extended examples and tutorials
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Jeremy Lewi 26c400a4cd Create a component to submit the Dataflow job to compute embeddings for code search (#324) 
* Create a component to submit the Dataflow job to compute embeddings for code search.

* Update Beam to 2.8.0
* Remove nmslib from Apache beam requirements.txt; its not needed and appears
  to have problems installing on the Dataflow workers.

* Spacy download was failing on Dataflow workers; reinstalling the spacy
  package as a pip package appears to fix this.

* Fix some bugs in the workflow for building the Docker images.

* * Split requirements.txt into separate requirements for the Dataflow
  workers and the UI.

* We don't want to install unnecessary dependencies in the Dataflow workers.
  Some unnecessary dependencies; e.g. nmslib were also having problems
  being installed in the workers.
 		2018-11-14 13:45:09 -08:00
agents Update PVC to /home/jovyan (#119) 2018-07-13 14:39:26 -07:00
code_search Create a component to submit the Dataflow job to compute embeddings for code search (#324) 2018-11-14 13:45:09 -08:00
codelab-image Update Ksonnet version, Add Python2 pip (#216) 2018-08-07 22:58:20 -07:00
demos Upgrade demo to KF v0.3.1 (#278) 2018-10-26 12:58:00 -07:00
financial_time_series minor fixes for instructions (#267) 2018-10-15 10:02:17 -07:00
github_issue_summarization A bunch of changes to support distributed training using tf.estimator (#265) 2018-11-07 16:23:59 -08:00
mnist example mnist upgrade to v1alpha2 (#246) 2018-09-09 13:01:21 -07:00
object_detection Fix #272 (#273) 2018-10-22 14:57:24 -07:00
pipelines Delete readme (#294) 2018-11-01 19:41:55 -07:00
test/workflows Use conditionals and add test for code search (#291) 2018-11-02 09:52:11 -07:00
xgboost_ames_housing new PR for XGBoost due to problems with history rewrite (#232) 2018-08-22 06:01:36 -07:00
.gitignore Add estimator example for github issues (#203) 2018-08-24 18:10:27 -07:00
.pylintrc Add namespace to ksonnet apply command (#57) 2018-04-02 09:41:02 -07:00
CONTRIBUTING.md Added tutorial for object detection distributed training (#74) 2018-07-03 14:10:20 -07:00
LICENSE Initial commit 2018-02-01 13:13:10 -08:00
OWNERS Remove inactive reviewers/approvers. (#296) 2018-11-02 08:34:20 -07:00
README.md add financial time series example (#252) 2018-10-12 08:04:07 -07:00
prow_config.yaml Skeleton testing framework (#18) 2018-03-01 21:30:50 -08:00

README.md

kubeflow-examples

A repository to share extended Kubeflow examples and tutorials to demonstrate machine learning concepts, data science workflows, and Kubeflow deployments. They illustrate the happy path, acting as a starting point for new users and a reference guide for experienced users.

This repository is home to three types of examples:

  1. End-to-end
  2. Component-focused
  3. Application-specific

End-to-end

GitHub issue summarization

Author: Hamel Husain

This example covers the following concepts:

  1. Natural Language Processing (NLP) with Keras and Tensorflow
  2. Connecting to Jupyterhub
  3. Shared persistent storage
  4. Training a Tensorflow model
  5. CPU
  6. GPU
  7. Serving with Seldon Core
  8. Flask front-end

MNIST

Author: Elson Rodriguez

This example covers the following concepts:

  1. Image recognition of handwritten digits
  2. S3 storage
  3. Training automation with Argo
  4. Monitoring with Argo UI and Tensorboard
  5. Serving with Tensorflow

Distributed Object Detection

Author: Daniel Castellanos

This example covers the following concepts:

  1. Gathering and preparing the data for model training using K8s jobs
  2. Using Kubeflow tf-job and tf-operator to launch a distributed object training job
  3. Serving the model through Kubeflow's tf-serving

Financial Time Series

Author: Sven Degroote

This example covers the following concepts:

  1. Deploying Kubeflow to a GKE cluster
  2. Exploration via JupyterHub (prospect data, preprocess data, develop ML model)
  3. Training several tensorflow models at scale with TF-jobs
  4. Deploy and serve with TF-serving
  5. Iterate training and serving
  6. Training on GPU

Component-focused

XGBoost - Ames housing price prediction

Author: Puneith Kaul

This example covers the following concepts:

  1. Training an XGBoost model
  2. Shared persistent storage
  3. GCS and GKE
  4. Serving with Seldon Core

Application-specific

Third-party hosted

Source Example Description

Get Involved

In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.

The Kubeflow community is guided by our Code of Conduct, which we encourage everybody to read before participating.