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aml-real-time-ai

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Easily deploy models to FPGAs for ultra-low latency with Azure Machine Learning powered by Project Brainwave

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# Microsoft Azure Machine Learning Hardware Accelerated Models Powered by Project Brainwave

## IMPORTANT!
This service is now generally available, and this repo will be shut down.  Please use the [updated notebooks](http://aka.ms/aml-accel-models-notebooks) and read the [updated documentation](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-fpga-web-service).

Easily create and train a model using various deep neural networks (DNNs) as a featurizer for deployment on Azure for ultra-low latency inferencing.  These models are currently available:

* ResNet 50
* ResNet 152
* DenseNet-121
* VGG-16

## How to get access

Azure ML Hardware Accelerated Models is currently in preview.

### Step 1: Create an Azure ML workspace

Follow [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python) to install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.

**Note:** Only workspaces in the **East US 2** region are currently supported.

Once you have set up your environment, install the contrib extras:

```sh
pip install --upgrade azureml-sdk[contrib]
```

Currently only tensorflow version<=1.10 is supported, so install it at the end:

```sh
pip install "tensorflow==1.10"
```

Go to the [documentation](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-fpga-web-service) page for any questions.

### Step 2: Deploy your service

Check out the sample notebooks [here](https://aka.ms/aml-notebook-proj-brainwave).

**Note:** You can deploy one FPGA service.  If you want to deploy more than one service, you must [request quota](https://aka.ms/aml-real-time-ai-request) by submitting the form.  You will need information from your workspace created in Step 1 ([learn how to get workspace information](docs/README.md)).  You will receive an email if your quota request has been successful.

## Support
Read the [docs](docs) or visit the [forum](https://aka.ms/aml-forum).

# 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 repos using our CLA.

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

## Build Status

System | Unit tests 
--- | ---
Ubuntu 16.04 | [![Build Status](https://dev.azure.com/coverste/aml-rt-ai/_apis/build/status/Azure.aml-real-time-ai?branchName=master)](https://dev.azure.com/coverste/aml-rt-ai/_build/latest?definitionId=1&branchName=master)

aml-real-time-ai

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