## Variational Autoencoders for Anomaly Detection With Tensorflow On SageMaker

This repository contains code to showcase how to detect anomalies using Variational Autoencoders and deploying multiple models to a single TensorFlow Serving multi-model endpoint. The deep learning framework in use is Tensorflow2. The dataset in use is MNIST.

## Environment Setup
First, run the following commands in your terminal to create a new conda environment named `tf2-p36` which has the required dependencies.

```bash
bash setup_env.sh
conda activate tf2-p36 # activate the environment
```

Then, run the following commands to add `tf2-p36` to IPython Kernel for Jupyter
```bash
conda install -c anaconda ipykernel
python -m ipykernel install --user --name=tf2-p36
```

Finally, choose `tf2-p36` as the Kernel where to run the notebooks on

## Repo Structure
```bash
+-- notebooks
|   +-- VAE_AnomalyDetection_Tensorflow.ipynb
+-- src
|   +-- config.py
|   +-- model_def.py
|   +-- train.py
+-- environment.yml
+-- README.md
+-- setup_env.sh
+-- CODE_OF_CONDUCT.md
+-- CONTRIBUTING.md
+-- LICENSE
```

## Security

See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.

## License

This library is licensed under the MIT-0 License. See the LICENSE file.