mirror of https://github.com/kubeflow/website.git
181 lines
8.0 KiB
Markdown
181 lines
8.0 KiB
Markdown
+++
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title = "Examples and tutorials"
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description = "A summary of recommended walkthroughs, blog posts, tutorials, and codelabs"
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weight = 80
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+++
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{{< blocks/content-section title="Kubeflow samples" color="light" >}}
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{{% blocks/content-item %}}
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This section introduces the examples in the
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[kubeflow/examples](https://github.com/kubeflow/examples) repo.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Semantic code search"
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url="https://github.com/kubeflow/examples/tree/master/code_search" %}}
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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).
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Financial time series"
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url="https://github.com/kubeflow/examples/tree/master/financial_time_series" %}}
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Train and serve a model for financial time series analysis using TensorFlow
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on GCP.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="GitHub issue summarization"
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url="https://github.com/kubeflow/examples/tree/master/github_issue_summarization" %}}
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Infer summaries of GitHub issues from the descriptions, using a Sequence to
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Sequence natural language processing model. You can run the tutorial in a
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Jupyter notebook or using TFJob. You use Seldon Core to serve the model.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="MNIST image classification"
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url="https://github.com/kubeflow/examples/tree/master/mnist" %}}
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Train and serve an image classification model using the MNIST dataset. You can
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choose to train the model locally, using GCP, or using Amazon S3. Serve the
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model using TensorFlow.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Object detection - cats and dogs"
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url="https://github.com/kubeflow/examples/tree/master/object_detection" %}}
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Train a distributed model for recognizing breeds of cats and
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dogs with the TensorFlow Object Detection API. Serve the model using TensorFlow.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="PyTorch MNIST"
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url="https://github.com/kubeflow/examples/tree/master/pytorch_mnist" %}}
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Train a distributed PyTorch model on GCP and serve the model with Seldon Core.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Ames housing value prediction"
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url="https://github.com/kubeflow/examples/tree/master/xgboost_ames_housing" %}}
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Train an XGBoost model using the Kaggle Ames Housing Prices prediction on GCP.
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Use Seldon Core to serve the model locally, or GCP to serve it in the cloud.
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{{% /blocks/content-item %}}
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{{< /blocks/content-section >}}
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{{< blocks/content-section title="Codelabs, workshops, and walkthroughs" color="white" >}}
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{{% blocks/content-item %}}
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Below is a list of recommended end-to-end tutorials, workshops, walkthroughs,
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and codelabs that are hosted outside the Kubeflow repositories.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="OpenShift Kubeflow Workshop"
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url="https://github.com/AICoE/openshift_kubeflow_workshop" %}}
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Run Kubeflow on Red Hat [OpenShift](https://www.openshift.com/).
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Katacoda scenarios"
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url="https://www.katacoda.com/kubeflow" %}}
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Follow the tutorials to deploy Kubeflow and run a machine learning model.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Introduction to Kubeflow Codelab"
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url="https://codelabs.developers.google.com/codelabs/kubeflow-introduction/index.html" %}}
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Run MNIST with Kubeflow on Google Kubernetes Engine.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Introduction to Kubeflow Qwiklab"
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url="https://www.qwiklabs.com/catalog_lab/933" %}}
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Run MNIST with resources provided by Qwiklabs.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Kubeflow End to End Codelab"
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url="https://codelabs.developers.google.com/codelabs/cloud-kubeflow-e2e-gis/index.html" %}}
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Run GitHub Issue Summarization with Kubeflow on Google Kubernetes Engine.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Kubeflow End to End Qwiklab"
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url="https://www.qwiklabs.com/catalog_lab/1046" %}}
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Run GitHub Issue Summarization with resources provided by Qwiklabs.
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{{% /blocks/content-item %}}
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{{< /blocks/content-section >}}
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{{< blocks/content-section title="Blog posts" color="light" >}}
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{{% blocks/content-item %}}
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The following blog posts provide detailed examples and use cases. Be aware that
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a blog post describes the interfaces at the time of publication of the post.
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Some interfaces are under rapid development and therefore may change frequently.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="The Kubeflow blog"
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url="https://medium.com/kubeflow" %}}
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Visit the Kubeflow blog to keep up to date with news about the project and to
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learn how to use the latest features.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Using Kubeflow to train and serve a PyTorch model in Google Cloud Platform"
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date="January 23, 2019"
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url="https://medium.com/kubeflow/end-to-end-kubeflow-tutorial-using-a-pytorch-model-in-google-cloud-platform-10fef557a089" %}}
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This example demonstrates how you can use Kubeflow to train and serve a
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distributed Machine Learning model with PyTorch on a Google Kubernetes Engine
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cluster in Google Cloud Platform (GCP).
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Getting started with Kubeflow Pipelines"
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date="November 16, 2018"
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url="https://cloud.google.com/blog/products/ai-machine-learning/getting-started-kubeflow-pipelines" %}}
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This article describes how you can tackle ML workflow operations with
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Kubeflow Pipelines, and highlights some examples that you can try
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yourself. The examples revolve around a TensorFlow ‘taxi fare tip prediction’
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model, with data pulled from a public BigQuery dataset of Chicago taxi trips.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="How to create and deploy a Kubeflow machine learning pipeline"
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date="November 22 - December 4, 2018"
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url="https://towardsdatascience.com/how-to-create-and-deploy-a-kubeflow-machine-learning-pipeline-part-1-efea7a4b650f" %}}
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A series of articles that walk you through the process of taking an existing
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real-world TensorFlow model and operationalizing the training, evaluation,
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deployment, and retraining of that model using Kubeflow Pipelines.
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[Part 1](https://towardsdatascience.com/how-to-create-and-deploy-a-kubeflow-machine-learning-pipeline-part-1-efea7a4b650f)
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(creating and deploying a pipeline), and
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[part 2](https://towardsdatascience.com/how-to-deploy-jupyter-notebooks-as-components-of-a-kubeflow-ml-pipeline-part-2-b1df77f4e5b3)
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(using Jupyter notebooks).
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{{% /blocks/content-item %}}
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{{< /blocks/content-section >}}
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{{< blocks/content-section title="Videos" color="white" >}}
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{{% blocks/content-item %}}
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Tutorials and overviews published in video format. Be aware that a video
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describes the interfaces at the time of publication of the video.
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Some interfaces are under rapid development and therefore may change frequently.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Machine Learning as Code: and Kubernetes with Kubeflow"
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date="December 15, 2018"
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url_text="Watch"
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url="https://www.youtube.com/watch?v=VXrGp5er1ZE" %}}
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Presenters: Jason "Jay" Smith and David Aronchick.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Artificial Intelligence at Cisco with Kubeflow"
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date="October 19, 2018"
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url_text="Watch"
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url="https://www.youtube.com/watch?v=ZzPyBY42wh8" %}}
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Presenter: Debo Dutta, Distinguished Engineer at Cisco.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="CNCF (Cloud Native Computing Foundation) channel"
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url_text="Watch"
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url="https://www.youtube.com/channel/UCvqbFHwN-nwalWPjPUKpvTA/search?query=kubeflow" %}}
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A YouTube search for Kubeflow in the CNCF (Cloud Native Computing Foundation)
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channel.
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{{% /blocks/content-item %}}
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{{% blocks/content-item title="Google Cloud Platform channel"
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url_text="Watch"
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url="https://www.youtube.com/user/googlecloudplatform/search?query=kubeflow" %}}
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A YouTube search for Kubeflow in the Google Cloud Platform
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channel.
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{{% /blocks/content-item %}}
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{{< /blocks/content-section >}}
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