PyTorch Neural Networks with Azure ML Jobs ML Studio
Watch the video below for an overview of how to convert notebook code to Azure ML jobs and execute PyTorch training at scale:
Note:
The notebook demonstrated in this video is fully annotated and can be completed without the video tutorial if that works best for you.
What You'll Learn
- Transform notebook code: Convert interactive PyTorch notebooks into structured job scripts
- Configure job environments: Set up Python environments, dependencies, and compute targets
- Submit and manage jobs: Launch training jobs and monitor their progress through the Azure ML interface
- Handle data and outputs: Manage input datasets and retrieve trained models and metrics
- Implement logging: Add comprehensive logging and experiment tracking to your PyTorch workflows
Why Azure ML Jobs over Notebooks?
Scalability: Automatic compute scaling and resource management
Reproducibility: Version-controlled environments and consistent execution
Cost Efficiency: Compute instances start and stop automatically
Monitoring: Manage and view all past logging in one space