Model Builder Azure Training Resources
The following is a guide to help you learn more about resources used to train models in Azure with Model Builder.
What is an Azure Machine Learning experiment?
An Azure Machine Learning experiment is a resource that needs to be created before running Model Builder training on Azure.
The experiment encapsulates the configuration and results for one or more machine learning training runs. Experiments belong to a specific workspace. The first time an experiment is created, its name is registered in the workspace. Any subsequent runs - if the same experiment name is used - are logged as part of the same experiment. Otherwise, a new experiment is created.
What is an Azure Machine Learning workspace?
A workspace is an Azure Machine Learning resource that provides a central place for all Azure Machine Learning resources and artifacts created as part of training run.
To create an Azure Machine Learning workspace, the following are required:
- Name: A name for your workspace between 3-33 characters. Names may only contain alphanumeric characters and hyphens.
- Region: The geographic location of the data center where your workspace and resources are deployed to. It is recommended that you choose a location close to where you or your customers are.
- Resource group: A container that contains all related resources for an Azure solution.
What is an Azure Machine Learning compute?
An Azure Machine Learning compute is a cloud-based Linux VM used for training.
To create an Azure Machine Learning compute, the following are required:
Name: A name for your compute between 2-16 characters. Names may only contain alphanumeric characters and hyphens.
Model Builder can use one of the following GPU-optimized compute types:
Size vCPU Memory: GiB Temp storage (SSD) GiB GPU GPU memory: GiB Max data disks Max NICs Standard_NC12 12 112 680 2 24 48 2 Standard_NC24 24 224 1440 4 48 64 4
Visit the NC-series Linux VM documentation for more details on GPU optimized compute types.
- Low-priority: Suited for tasks with shorter execution times. May be impacted by interruptions and lack of availability. Usually costs less because it takes advantage of surplus capacity in Azure.
- Dedicated: Suited for tasks of any duration, but especially long-running jobs. Not impacted by interruptions or lack of availability. Usually costs more because it reserves a dedicated set of compute resources in Azure for your tasks.
Training on Azure is only available for the Model Builder image classification scenario. The algorithm used to train these models is a Deep Neural Network based on the ResNet50 architecture. The training process takes some time and the amount of time may vary depending on the size of compute selected as well as amount of data. You can track the progress of your runs by selecting the "Monitor current run in Azure portal" link in Visual Studio.
Once training is complete, two projects are added to your solution with the following suffixes:
ConsoleApp: A C# .NET Core console application that provides starter code to build the prediction pipeline and make predictions.
Model: A C# .NET Standard application that contains the data models that define the schema of input and output model data as well as the following assets:
- bestModel.onnx: A serialized version of the model in Open Neural Network Exchange (ONNX) format. ONNX is an open source format for AI models that supports interoperability between frameworks like ML.NET, PyTorch and TensorFlow.
- bestModelMap.json: A list of categories used when making predictions to map the model output to a text category.
- MLModel.zip: A serialized version of the ML.NET prediction pipeline that uses the serialized version of the model bestModel.onnx to make predictions and maps outputs using the
Use the machine learning model
ModelOutput classes in the Model project define the schema of the model's expected input and output respectively.
In an image classification scenario, the
ModelInput contains two columns:
ImageSource: The string path of the image location.
Label: The actual category the image belongs to.
Labelis only used as an input when training and does not need to be provided when making predictions.
ModelOutput contains two columns:
Prediction: The image's predicted category.
Score: The list of probabilities for all categories (the highest belongs to the
Cannot create compute
If an error occurs during Azure Machine Learning compute creation, the compute resource may still exist, in an errored state. If you try to re-create the compute resource with the same name, the operation fails. To fix this error, either:
- Create the new compute with a different name
- Go to the Azure portal, and remove the original compute resource
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