1cc4550b7d
* Create a test for submitting the TFJob for the GitHub issue summarization example.
* This test needs to be run manually right now. In a follow on PR we will
integrate it into CI.
* We use the image built from Dockerfile.estimator because that is the image
we are running train_test.py in.
* Note: The current version of the code now requires Python3 (I think this
is due to an earlier PR which refactored the code into a shared
implementation for using TF estimator and not TF estimator).
* Create a TFJob component for TFJob v1beta1; this is the version
in KF 0.4.
TFJob component
* Upgrade to v1beta to work with 0.4
* Update command line arguments to match the versions in the current code
* input & output are now single parameters rather then separate parameters
for bucket and name
* change default input to a CSV file because the current version of the
code doesn't handle unzipping it.
* Use ks_util from kubeflow/testing
* Address comments.
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|---|---|---|
| agents | ||
| code_search | ||
| codelab-image | ||
| demos | ||
| financial_time_series | ||
| github_issue_summarization | ||
| mnist | ||
| object_detection | ||
| pipelines | ||
| pytorch_mnist | ||
| test/workflows | ||
| xgboost_ames_housing | ||
| .gitignore | ||
| .pylintrc | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| OWNERS | ||
| README.md | ||
| prow_config.yaml | ||
README.md
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
GitHub issue summarization
Author: Hamel Husain
This example covers the following concepts:
- Natural Language Processing (NLP) with Keras and Tensorflow
- Connecting to Jupyterhub
- Shared persistent storage
- Training a Tensorflow model
- CPU
- GPU
- Serving with Seldon Core
- Flask front-end
Pytorch MNIST
Author: David Sabater
This example covers the following concepts:
- Distributed Data Parallel (DDP) training with Pytorch on CPU and GPU
- Shared persistent storage
- Training a Pytorch model
- CPU
- GPU
- Serving with Seldon Core
- Flask front-end
MNIST
Author: Elson Rodriguez
This example covers the following concepts:
- Image recognition of handwritten digits
- S3 storage
- Training automation with Argo
- Monitoring with Argo UI and Tensorboard
- Serving with Tensorflow
Distributed Object Detection
Author: Daniel Castellanos
This example covers the following concepts:
- Gathering and preparing the data for model training using K8s jobs
- Using Kubeflow tf-job and tf-operator to launch a distributed object training job
- Serving the model through Kubeflow's tf-serving
Financial Time Series
Author: Sven Degroote
This example covers the following concepts:
- Deploying Kubeflow to a GKE cluster
- Exploration via JupyterHub (prospect data, preprocess data, develop ML model)
- Training several tensorflow models at scale with TF-jobs
- Deploy and serve with TF-serving
- Iterate training and serving
- Training on GPU
Component-focused
XGBoost - Ames housing price prediction
Author: Puneith Kaul
This example covers the following concepts:
- Training an XGBoost model
- Shared persistent storage
- GCS and GKE
- 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.
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.