Labs for Training and Serving TensorFlow Models with Kubernetes and Kubeflow on Azure Container Service (AKS)
Prerequisites
- Have a valid Microsoft Azure subscription allowing the creation of an AKS cluster
- Docker client installed: Installing Docker
- Azure-cli (2.0) installed: Installing the Azure CLI 2.0 | Microsoft Docs
- Git cli installed: Installing Git CLI
- Kubectl installed: Installing Kubectl
- Helm installed: Installing Helm CLI (Note: On Windows you can extract the
tarfile using a tool like 7Zip.) - ksonnet installed: Installing ksonnet CLI
Clone this repository somewhere so you can easily access the different source files:
git clone https://github.com/Azure/kubeflow-labsContent Summary
| Module | Description | |
|---|---|---|
| 0 | Introduction | Introduction to this workshop. Motivations and goals. |
| 1 | Docker | Docker and containers 101. |
| 2 | Kubernetes | Kubernetes important concepts overview. |
| 3 | Helm | Introduction to Helm |
| 4 | Kubeflow | Introduction to Kubeflow and how to deploy it in your cluster. |
| 5 | JupyterHub | Learn how to run JupyterHub to create and manage Jupyter notebooks using Kubeflow |
| 6 | TFJob | Introduction to TFJob and how to use it to deploy a simple TensorFlow training. |
| 7 | Distributed Tensorflow | Learn how to deploy and monitor distributed TensorFlow trainings with TFJob |
| 8 | Hyperparameters Sweep with Helm | Using Helm to deploy a large number of trainings testing different hypothesis, and TensorBoard to monitor and compare the results |
| 9 | Serving | Using TensorFlow Serving to serve predictions |
| 10 | Going Further | Links and resources to go further: Autoscaling, Distributed Storage etc. |
Contributing
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