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A repository to host extended examples and tutorials
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Sanyam Kapoor f9873e6ac4 Upgrade notebook commands and other relevant changes (#229) 
* 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
 		2018-08-20 16:35:07 -07:00
agents Update PVC to /home/jovyan (#119) 2018-07-13 14:39:26 -07:00
code_search Upgrade notebook commands and other relevant changes (#229) 2018-08-20 16:35:07 -07:00
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mnist Fixed distributed training for LINEAR model (#130) 2018-06-13 11:57:28 -07:00
object_detection Small fix to TF serving gpu (#221) 2018-08-14 14:27:35 -07:00
test/workflows Fix failing test due to https://github.com/kubeflow/testing/pull/111 (#95) 2018-04-24 12:11:00 -07:00
.gitignore Create a deployment to run the HP/Katib controller for the GitHub issue example. (#161) 2018-07-11 08:46:25 -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
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OWNERS fix typo in OWNER (#193) 2018-07-24 08:04:55 -07:00
README.md Added tutorial for object detection distributed training (#74) 2018-07-03 14:10:20 -07:00
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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

Component-focused

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.