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.

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.

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.

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