# kubeflow-examples

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

This repository is home to the following types of examples and demos:
* [End-to-end](#end-to-end)
* [Component-focused](#component-focused)
* [Demos](#demos)

## End-to-end

### [GitHub issue summarization](./github_issue_summarization)
Author: [Hamel Husain](https://github.com/hamelsmu)

This example covers the following concepts:
1. Natural Language Processing (NLP) with Keras and Tensorflow
1. Connecting to Jupyterhub
1. Shared persistent storage
1. Training a Tensorflow model
    1. CPU
    1. GPU
1. Serving with Seldon Core
1. Flask front-end

### [Pytorch MNIST](./pytorch_mnist)
Author: [David Sabater](https://github.com/dsdinter)

This example covers the following concepts:
1. Distributed Data Parallel (DDP) training with Pytorch on CPU and GPU
1. Shared persistent storage
1. Training a Pytorch model
    1. CPU
    1. GPU
1. Serving with Seldon Core
1. Flask front-end

### [MNIST](./mnist)

Author: [Elson Rodriguez](https://github.com/elsonrodriguez)

This example covers the following concepts:
1. Image recognition of handwritten digits
1. S3 storage
1. Training automation with Argo
1. Monitoring with Argo UI and Tensorboard
1. Serving with Tensorflow

### [Distributed Object Detection](./object_detection)

Author: [Daniel Castellanos](https://github.com/ldcastell)

This example covers the following concepts:
1. Gathering and preparing the data for model training using K8s jobs
1. Using Kubeflow tf-job and tf-operator to launch a distributed object training job
1. Serving the model through Kubeflow's tf-serving

### [Financial Time Series](./financial_time_series)

Author: [Sven Degroote](https://github.com/Svendegroote91)

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](./xgboost_ames_housing)
Author: [Puneith Kaul](https://github.com/puneith)

This example covers the following concepts:
1. Training an XGBoost model
1. Shared persistent storage
1. GCS and GKE
1. Serving with Seldon Core

## Demos

Demos are for showing Kubeflow or one of its components publicly, with the
intent of highlighting product vision, not necessarily teaching. In contrast,
the goal of the **examples** is to provide a self-guided walkthrough of
Kubeflow or one of its components, for the purpose of teaching you how to
install and use the product.

In an *example*, all commands should be embedded in the process and explained.
In a *demo*, most details should be done behind the scenes, to optimize for
 on-stage rhythm and limited timing.

You can find the demos in the [`/demos` directory](demos/).

## Third-party hosted

| Source | Example | Description |
| ------ | ------- | ----------- |
| | | | |

## Get Involved

* [Slack Channel: #kubeflow-examples](https://join.slack.com/t/kubeflow/shared_invite/enQtMjgyMzMxNDgyMTQ5LWUwMTIxNmZlZTk2NGU0MmFiNDE4YWJiMzFiOGNkZGZjZmRlNTExNmUwMmQ2NzMwYzk5YzQxOWQyODBlZGY2OTg)
* [Twitter](http://twitter.com/kubeflow)
* [Mailing List](https://groups.google.com/forum/#!forum/kubeflow-discuss)

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](https://github.com/kubeflow/community/blob/master/CODE_OF_CONDUCT.md), which we encourage everybody to read before participating.