az-deep-realtime-score/{{cookiecutter.project_name}}/Keras_Tensorflow/aks

Deploy Deep Learning CNN on Kubernetes Cluster with GPUs - Keras

To get started with the tutorial, please proceed with following steps in sequential order.

• Prerequisites
• Steps
• Cleaning up

Prerequisites

1. Linux (x64) with GPU enabled.
2. Anaconda Python
3. Docker installed.
4. Azure account.

The tutorial was developed on an Azure Ubuntu DSVM, which addresses the first three prerequisites.

Steps

Please follow these steps to set up your environment and run notebooks. They setup the notebooks to use Docker and Azure seamlessly.

1. Add your user to the docker group:

   sudo usermod -aG docker $USER
   newgrp docker
   

   To verify whether you have correct configuration, try executing docker ps command. You should not get permission denied errors.

2. Navigate to the directory which is the framework you have chosen (e.g. Keras_Tensorflow).

3. Create the Python virtual environment using the environment.yml:

   conda env create -f environment.yml
   

4. Activate the virtual environment:

   source activate deployment_aml
   

5. Login to Azure:

   az login
   

6. If you have more than one Azure subscription, select it:

   az account set --subscription <Your Azure Subscription>
   

7. Start the Jupyter notebook server in the virtual environment:

   jupyter notebook
   

8. Select correct kernel: set the kernel to be Python [conda env: deployment_aml](or Python 3 if that option does not show).

9. After following the setup instructions above, run the Jupyter notebooks in order starting with the first notebook 00_AMLSetup.ipynb.

Cleaning up

To remove the conda environment created see here. The last Jupyter notebook also gives details on deleting Azure resources associated with this repository.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repositories using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.