In order to create a custom training pipeline in Azure Machine Learning Studio for generative AI (such as training a model like GPT), you can follow these steps:
- Set Up Azure Machine Learning Workspace
- Ensure you have an Azure ML workspace set up, or create one via the Azure portal.
- Create a Compute Target
- Choose a compute target (like a VM or Azure ML compute cluster) for training.
- Define the Training Script
- Create a custom Python script for training the generative model (e.g., GPT). This script could use libraries like transformers for training.
- Create an Estimator
- Use the Estimator class in Azure ML to run your script on the compute target.
- Create the Training Pipeline
- Define the pipeline and add the training step.
Here is the code you can refer to:
In the above code, we are using the following key points:
- Workspace Setup: Connect to Azure ML workspace.
- Compute Target: Set up a compute target for model training.
- Training Script: Define the script for your generative model (e.g., GPT).
- Estimator and Pipeline: Use an Estimator to run the model on Azure ML compute.
- Monitor: Track the training progress.
Hence, this approach allows you to build a custom pipeline in Azure Machine Learning Studio to train generative AI models efficiently.
