Model Builder Handshake
Model Builder Redesign
This notebook highlights the new changes made to ModelBuilder and related utilities
- Latest Container Image Utility function
- Handshake with ModelTrainer
- Unified Deployment from ModelBuilder
Inital Setup
Integration with ModelTrainer
The handshake between ModelTrainer and ModelBuilder is made seamlessly as in this example. The created model trainer object is directly fed into the model attribute of ModelBuilder through resource chaining . Fetching of the model artifacts is done internally within the ModelBuilder.
Note:
- Other than the ModelTrainer, the ModelBuilder also supports chaining of attributes such as Estimator or sagemaker-core's TrainingJob into the model attribute.
Other than this there is an upgrade designed for retrieving images for a particular framework. The enhanced image_uris.retrieve() method will fetch the latest version of an image automatically if the version is not provided.
Once the model has been built , it can be deployed directly through the model_builder.deploy() method. This abstracts out information that was previously used commonly in workflows for different deployment modes. The deploy() method takes in an optional parameter inference_config. This determines attributes for modes such as serverless, async, batch and multi-model/multi-container endpoints. If the inference_config is not provided, the default real-time deployment is carried out.