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A repository to host extended examples and tutorials
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Jeremy Lewi a32227f371 Fix the ksonnet by defining globals. (#354) 
* The latest changes to the ksonnet components require certain values
  to be defined as defaults.

* This is part of the move away from using a fake component to define
  parameters that should be reused across different modules.

  see #308

* Verify we can run ks show on a new environment and can evaluate the ksonnet.

Fix #353
 		2018-11-24 14:36:43 -08:00
agents Update PVC to /home/jovyan (#119) 2018-07-13 14:39:26 -07:00
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demos Simple pipeline demo (#322) 2018-11-16 11:16:12 -08:00
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
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pytorch_mnist [mnist_pytorch] fix train image (#342) 2018-11-24 13:22:28 -08:00
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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
    1. CPU
    2. GPU
  5. Serving with Seldon Core
  6. Flask front-end

Pytorch MNIST

Author: David Sabater

This example covers the following concepts:

  1. Distributed Data Parallel (DDP) training with Pytorch on CPU and GPU
  2. Shared persistent storage
  3. Training a Pytorch model
    1. CPU
    2. GPU
  4. Serving with Seldon Core
  5. 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.