We think it's great that you want to contribute to Kubeflow examples! To get started, please refer to the short guide below.
The Kubeflow project is dedicated to making machine learning on Kubernetes simple, portable, and scalable. We need your support in making this repo the destination for top models and examples that highlight the power of Kubeflow.
This repository is home to three types of examples:
End-to-end examples are complete, soup to nuts walkthroughs that provide a starting point and architectural guidelines. They are extensible over time and updated as new features appear in Kubeflow and new tools/frameworks are supported. They can be large and are designed to be choose-your-own-adventure style.
End-to-end examples each cover these concepts at a minimum:
- Installing Kubeflow itself and any prerequisites
- Training a model
- Serving the trained model
- Accessing and displaying predictions
Component-focused examples assume an existing Kubeflow installation and are shorter in length. They focus on a single concept such as a component or feature, e.g. illustrating the use of persistent disks as opposed to guiding someone through their first use of Kubeflow. They are designed to be combined with other examples in any number of configurations.
Application-specific examples highlight a common machine learning technique such as recommendation, classification, or vision. Like component-focused examples, these are also smaller in scope than end-to-end examples.
This is a list of examples maintained by third parties that demonstrate Kubeflow usage. This category includes examples that highlight integration with other systems (as opposed to support for them), such as case studies.
Other examples that belong in this list are media formats that are not appropriate for inclusion in the repo directly, such as videos, blog posts, tweets, screenshots, etc. This also includes cases where it does not make sense to conform pre-existing code to be consistent with this repo.
Suggestions are always welcome for migration from third-party to directly hosted in this repo, given increased usefulness and/or alignment with release content.
Improvements to existing end-to-end, component-focused, and application-specific examples are always welcome. This could mean smoothing out any rough edges, filling out support for additional platforms, fixing bugs, and improving clarity in the instructions.
Additional component-focused and application-specific examples for highly requested concepts are also encouraged.
Extensions to our list of third-party hosted examples are also welcome. If it's useful to the community, it belongs here!
Once you have identified an area for improvement, follow these steps:
- Create a Github issue with the details and self-assign
- Send a PR to this repo with the related fix or new content
Examples housed in this repo should have the following characteristics:
A good example fits in with existing examples. It follows the same conventions, has similar structure, and does not stick out. Ideally, all examples follow the same setup/usage README template.
Basic tests such as linting are run automatically when changes are added to a PR, which must pass before it is eligible for merging.
A good example has longevity and is resistant to getting stale by using widely recognized architectures and stable libraries. It is robust against releases by including broad coverage and specific, focused tests designed to highlight issues related to future changes in dependencies.
A good example is not too complicated, including a small number of files and covering a single, common use case. It makes use of well-known or straightforward datasets and performs well-understood functions.
A good example is easily digestible by our user base. The examples hosted here must serve a number of diverse, but overlapping skillsets. Each example does not need to cover all users, but should accommodate at least one set of expected core competencies from the roles in this list:
- DevOps engineer
- ML engineer
- Data engineer
- Data scientist
A good example can be extended to include additional components, platforms, and/or techniques. It does not need to be provider-agnostic, but should be straightforward for others to add support. A good example inspires extension by other members of the community.
A good example is a self-contained landing spot for new users, which might mean that it includes a link to end-to-end examples or cluster setup. It should contain enough information for a user to forklift the code into their own environment, without describing common processes in multiple places across the repository.
A good example starts with an overview, usually containing the following pieces:
- Goals
- Non-goals
- Steps involved
- Intended audience
- Prerequisites
- Assumptions
It includes clear, well-organized step-by-step instructions that call out assumptions and provide warnings about potential points of contention.
This is a list of sources for potential new examples. Please feel free to self-assign by putting your github ID in the appropriate column, creating a corresponding GitHub issue, and submitting a PR. It is not an exhaustive list, only the result of brainstorming for inspiration. Feel free to add to this list and/or reprioritize.
Priority guidance:
- P0: Very important, try to self-assign if there is a P0 available
- P1: Important, try to self-assign if there is no P0 available
- P2: Nice to have
| Example | What does it accomplish? | Priority | Priority reasoning | ML framework | Owner (github_id) | Company | Github issue |
|---|---|---|---|---|---|---|---|
| GitHub issue summarization | How to perform TensorFlow serving on Kubeflow e2e | P0 | Desire to extend current support of Seldon serving | TensorFlow | |||
| Zillow housing prediction | Zillow's home value prediction on Kaggle | P0 | High prize Kaggle competition w/ opportunity to show XGBoost | XGBoost | puneith | issue #16 | |
| Mercari price suggestion challenge | Automatically suggest product process to online sellers | P0 | |||||
| Airbnb new user bookings | Where will a new guest book their first travel experience | ||||||
| TensorFlow object detection | Object detection using TensorFlow API | TensorFlow | ldcastell | Intel | issue #73 | ||
| TFGAN | Define, Train and Evaluate GAN | GANs are of great interest currently | |||||
| Nested LSTM | TensorFlow implementation of nested LSTM cell | LSTM are the canonical implementation of RNN to solve vanishing gradient problem and widely used for Time Series | |||||
| How to solve 90% of NLP problems: A step by step guide on Medium | Medium post on how to solve 90% of NLP problems from Emmanuel Ameisen | Solves a really common problem in a generic way. Great example for people who want to do NLP and don't know how to do 80% of stuff like tokenzation, basic transforms, stop word removal etc and are boilerplate across every NLP task | |||||
| Tour of top-10 algorithms for ML newbies | Top 10 algorithms for ML newbies | Medium post with 8K claps and a guide for ML newbies to get started with ML |