RoseTTAFold with Azure Machine Learning

Introduction

Azure is collaborating with the Baker Lab at the University of Washington to easily run the RoseTTAFold protein folding model using Azure Machine Learning (Azure ML).

For more information on RoseTTAFold, visit the project's GitHub repo and the project announcement.

This repo contains three notbooks that will guide you through setting up your Azure ML workspace, submitting jobs for processing, and setting up a batch endpoint for scalable inferencing from the command line interface (CLI) or REST APIs.

Note: the RoseTTAFold deployments demonstrated here are not designed to run in production environments. They are is strictly for non-production test environments.

Notebooks

This repo contains the following notebooks:

• 1-setup-workspace.ipynb
  • Run one-time setup steps to prepare your Azure ML Workspace with the dependency Datasets and a Compute Cluster.
• 2-run-experiment.ipynb
  • Run RosettaFold as an Azure ML Experiment.
  • Create an input file, submit a Run, check status, and get the results.
• 3-batch-endpoint.ipynb
  • Create a Batch Endpoint that can be called from the Azure CLI or as a REST call.

Getting Started

1. Clone this repo: git clone https://github.com/microsoft/rosettafold-azureml.git
2. Open the folder in VS Code (installation instructions here, if needed): code rosettafold-azureml
3. Open each notebook and follow along, starting with 1-setup-workspace.ipynb.

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.opensource.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., status check, 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. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

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