These examples provide and introduction to SageMaker Debugger which allows debugging and monitoring capabilities for training of machine learning and deep learning algorithms. Note that although these notebooks focus on a specific framework, the same approach works with all the frameworks that Amazon SageMaker Debugger supports. The notebooks below are listed in the order in which we recommend you review them.
- Using a built-in rule with TensorFlow
- Using a custom rule with TensorFlow Keras
- Interactive tensor analysis in notebook with MXNet
- Visualizing Debugging Tensors of MXNet training
- Real-time analysis in notebook with MXNet
- Using a built in rule with XGBoost
- Real-time analysis in notebook with XGBoost
- Using SageMaker Debugger with Managed Spot Training and MXNet
- Reacting to CloudWatch Events from Rules to take an action based on status with TensorFlow
- Using SageMaker Debugger with a custom PyTorch container