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Arch diag + other minor additions #108

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Jun 29, 2018
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Arch diag + other minor additions #108

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Architecture Details
animeshsingh Jun 29, 2018
4485b78
Architecture Details
animeshsingh Jun 29, 2018
7d105f1
Architecture Details
animeshsingh Jun 29, 2018
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12 changes: 10 additions & 2 deletions README.md
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Expand Up @@ -35,13 +35,21 @@ To know more about the architectural details, please read the [design document](

## Usage Scenarios

* If you already have a FfDL deployment up and running, you can jump to [FfDL User Guide](docs/user-guide.md) to use FfDL for training your deep learning models.
* If you have a FfDL deployment up and running, you can jump to [FfDL User Guide](docs/user-guide.md) to use FfDL for training your deep learning models.

* If you have FfDL confiugured to use GPUs, and want to train using GPUs, follow steps [here](docs/gpu-guide.md)

* If you have used FfDL to train your models, and want to use a GPU enabled public cloud hosted service for further training and serving, please follow instructions [here](etc/converter/ffdl-wml.md) to train and serve your models using [Watson Studio Deep Learning](https://www.ibm.com/cloud/deep-learning) service

* If you are getting started and want to setup your own FfDL deployment, please follow the steps below.
* If you are getting started and want to setup your own FfDL deployment, please follow the steps [below](#1-quick-start).

* If you want to leverage Jupyter notebooks to launch training on your FfDL cluster, please follow [these instructions](etc/notebooks/art)

* To invoke [Adversarial Robustness Toolbox](https://github.com/IBM/adversarial-robustness-toolbox) to find vulnerabilities in your models, follow the [instructions here](etc/notebooks/art)

* To deploy your trained models, follow [the integration guide with Seldon](community/FfDL-Seldon)

* If you are looking for related collateral, slides, webinars, blogs and other materials related to FfDL, please [find them here](demos)

## Steps

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6 changes: 4 additions & 2 deletions etc/notebooks/art/README.md
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# Jupyter Notebook Using ART to Test the Robustness of Deep Learning Models

This [Jupyter](http://jupyter.org/install) notebook shows how to use the [Adversarial Robustness Toolbox (ART)](https://github.com/IBM/adversarial-robustness-toolbox)
to test the robustness of Deep Learning models against adversarial attacks.
This [Jupyter](http://jupyter.org/install) notebook shows how to

- Launch training on your FfDL cluster using Jupyter notebook as a client
- Use [Adversarial Robustness Toolbox (ART)](https://github.com/IBM/adversarial-robustness-toolbox) to test the robustness of Deep Learning models against adversarial attacks.

## Prerequisites

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