# cmd

conda create --name sfm python=3.8 -y
conda activate sfm

pip install "git+https://github.com/Jordan-Pierce/Metashape-Azure.git"

If you are also running Metashape locally, you will need to run the install.py script as well:

# cmd

python install.py

This will add dependencies to the Metashape python environment. Finally, to run the application, use the following command:

# cmd

metashape-azure-mls

# cmd

conda activate sfm

pip install -U "git+https://github.com/Jordan-Pierce/Metashape-Azure.git"

1. Open Azure Machine Learning Studio in Microsoft Edge and login
2. Navigate to your workspace
3. Copy the following credentials from the top-right of the page:
   • Subscription ID
   • Resource Group
   • Workspace Name
4. Enter credentials in the application:
   • Use "Save Credentials" to store for future use
   • Click "Authenticate"
5. Select your compute cluster from the dropdown
6. Configure project paths:
   • Input Path: Azure URI to image folder
   • Output Path: Azure URI for project destination
   • Project Name: Unique name for your project

     Note: {output path}/{project name} must not already exist

7. Select desired SfM parameters
8. Click "Run on Azure" to start processing

• Azure Machine Learning Studio and Authentication must be done in Microsoft Edge
• You must be connected to the network (i.e., VPN) to access the Azure services.