website/content/docs/examples/resources.md

+++ title = "Examples and tutorials" description = "A summary of recommended walkthroughs, blog posts, tutorials, and codelabs" weight = 80 +++

{{< blocks/content-section title="Kubeflow samples" color="light" >}}

{{% blocks/content-item %}} This section introduces the examples in the kubeflow/examples repo. {{% /blocks/content-item %}}

{{% blocks/content-item title="Semantic code search" url="https://github.com/kubeflow/examples/tree/master/code_search" %}} Use a Sequence to Sequence natural language processing model to perform a semantic code search. This tutorial runs in a Jupyter notebook and uses Google Cloud Platform (GCP). {{% /blocks/content-item %}}

{{% blocks/content-item title="Financial time series" url="https://github.com/kubeflow/examples/tree/master/financial_time_series" %}} Train and serve a model for financial time series analysis using TensorFlow on GCP. {{% /blocks/content-item %}}

{{% blocks/content-item title="GitHub issue summarization" url="https://github.com/kubeflow/examples/tree/master/github_issue_summarization" %}} Infer summaries of GitHub issues from the descriptions, using a Sequence to Sequence natural language processing model. You can run the tutorial in a Jupyter notebook or using TFJob. You use Seldon Core to serve the model. {{% /blocks/content-item %}}

{{% blocks/content-item title="MNIST image classification" url="https://github.com/kubeflow/examples/tree/master/mnist" %}} Train and serve an image classification model using the MNIST dataset. You can choose to train the model locally, using GCP, or using Amazon S3. Serve the model using TensorFlow. {{% /blocks/content-item %}}

{{% blocks/content-item title="Object detection - cats and dogs" url="https://github.com/kubeflow/examples/tree/master/object_detection" %}} Train a distributed model for recognizing breeds of cats and dogs with the TensorFlow Object Detection API. Serve the model using TensorFlow. {{% /blocks/content-item %}}

{{% blocks/content-item title="PyTorch MNIST" url="https://github.com/kubeflow/examples/tree/master/pytorch_mnist" %}} Train a distributed PyTorch model on GCP and serve the model with Seldon Core. {{% /blocks/content-item %}}

{{% blocks/content-item title="Ames housing value prediction" url="https://github.com/kubeflow/examples/tree/master/xgboost_ames_housing" %}} Train an XGBoost model using the Kaggle Ames Housing Prices prediction on GCP. Use Seldon Core to serve the model locally, or GCP to serve it in the cloud. {{% /blocks/content-item %}} {{< /blocks/content-section >}}

{{< blocks/content-section title="Codelabs, workshops, and walkthroughs" color="white" >}}

{{% blocks/content-item %}} Below is a list of recommended end-to-end tutorials, workshops, walkthroughs, and codelabs that are hosted outside the Kubeflow repositories. {{% /blocks/content-item %}}

{{% blocks/content-item title="OpenShift Kubeflow Workshop" url="https://github.com/AICoE/openshift_kubeflow_workshop" %}} Run Kubeflow on Red Hat OpenShift. {{% /blocks/content-item %}}

{{% blocks/content-item title="Katacoda scenarios" url="https://www.katacoda.com/kubeflow" %}} Follow the tutorials to deploy Kubeflow and run a machine learning model. {{% /blocks/content-item %}}

{{% blocks/content-item title="Introduction to Kubeflow Codelab" url="https://codelabs.developers.google.com/codelabs/kubeflow-introduction/index.html" %}} Run MNIST with Kubeflow on Google Kubernetes Engine. {{% /blocks/content-item %}}

{{% blocks/content-item title="Introduction to Kubeflow Qwiklab" url="https://www.qwiklabs.com/catalog_lab/933" %}} Run MNIST with resources provided by Qwiklabs. {{% /blocks/content-item %}}

{{% blocks/content-item title="Kubeflow End to End Codelab" url="https://codelabs.developers.google.com/codelabs/cloud-kubeflow-e2e-gis/index.html" %}} Run GitHub Issue Summarization with Kubeflow on Google Kubernetes Engine. {{% /blocks/content-item %}}

{{% blocks/content-item title="Kubeflow End to End Qwiklab" url="https://www.qwiklabs.com/catalog_lab/1046" %}} Run GitHub Issue Summarization with resources provided by Qwiklabs. {{% /blocks/content-item %}}

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{{< blocks/content-section title="Blog posts" color="light" >}}

{{% blocks/content-item %}} The following blog posts provide detailed examples and use cases. Be aware that a blog post describes the interfaces at the time of publication of the post. Some interfaces are under rapid development and therefore may change frequently. {{% /blocks/content-item %}}

{{% blocks/content-item title="The Kubeflow blog" url="https://medium.com/kubeflow" %}} Visit the Kubeflow blog to keep up to date with news about the project and to learn how to use the latest features. {{% /blocks/content-item %}}

{{% blocks/content-item title="Using Kubeflow to train and serve a PyTorch model in Google Cloud Platform" date="January 23, 2019" url="https://medium.com/kubeflow/end-to-end-kubeflow-tutorial-using-a-pytorch-model-in-google-cloud-platform-10fef557a089" %}} This example demonstrates how you can use Kubeflow to train and serve a distributed Machine Learning model with PyTorch on a Google Kubernetes Engine cluster in Google Cloud Platform (GCP). {{% /blocks/content-item %}}

{{% blocks/content-item title="Getting started with Kubeflow Pipelines" date="November 16, 2018" url="https://cloud.google.com/blog/products/ai-machine-learning/getting-started-kubeflow-pipelines" %}} This article describes how you can tackle ML workflow operations with Kubeflow Pipelines, and highlights some examples that you can try yourself. The examples revolve around a TensorFlow ‘taxi fare tip prediction’ model, with data pulled from a public BigQuery dataset of Chicago taxi trips. {{% /blocks/content-item %}}

{{% blocks/content-item title="How to create and deploy a Kubeflow machine learning pipeline" date="November 22 - December 4, 2018" url="https://towardsdatascience.com/how-to-create-and-deploy-a-kubeflow-machine-learning-pipeline-part-1-efea7a4b650f" %}} A series of articles that walk you through the process of taking an existing real-world TensorFlow model and operationalizing the training, evaluation, deployment, and retraining of that model using Kubeflow Pipelines. Part 1 (creating and deploying a pipeline), and part 2 (using Jupyter notebooks). {{% /blocks/content-item %}} {{< /blocks/content-section >}}

{{< blocks/content-section title="Videos" color="white" >}}

{{% blocks/content-item %}} Tutorials and overviews published in video format. Be aware that a video describes the interfaces at the time of publication of the video. Some interfaces are under rapid development and therefore may change frequently. {{% /blocks/content-item %}}

{{% blocks/content-item title="Machine Learning as Code: and Kubernetes with Kubeflow" date="December 15, 2018" url_text="Watch" url="https://www.youtube.com/watch?v=VXrGp5er1ZE" %}} Presenters: Jason "Jay" Smith and David Aronchick. {{% /blocks/content-item %}}

{{% blocks/content-item title="Artificial Intelligence at Cisco with Kubeflow" date="October 19, 2018" url_text="Watch" url="https://www.youtube.com/watch?v=ZzPyBY42wh8" %}} Presenter: Debo Dutta, Distinguished Engineer at Cisco. {{% /blocks/content-item %}}

{{% blocks/content-item title="CNCF (Cloud Native Computing Foundation) channel" url_text="Watch" url="https://www.youtube.com/channel/UCvqbFHwN-nwalWPjPUKpvTA/search?query=kubeflow" %}} A YouTube search for Kubeflow in the CNCF (Cloud Native Computing Foundation) channel. {{% /blocks/content-item %}}

{{% blocks/content-item title="Google Cloud Platform channel" url_text="Watch" url="https://www.youtube.com/user/googlecloudplatform/search?query=kubeflow" %}} A YouTube search for Kubeflow in the Google Cloud Platform channel. {{% /blocks/content-item %}}

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