Labs for Training and Serving TensorFlow Models with Kubernetes and Kubeflow on Azure Container Service (AKS)
- 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-labs
|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|
|7||Distributed Tensorflow||Learn how to deploy and monitor distributed TensorFlow trainings with
|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.|
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