Optimize a machine learning model with SageMaker Neo
SageMaker Neo enables to tune ML model once and uses the model to run anywhere in the cloud and edge. It optimizes the model for increased speed and less memory consumption. Neo provides the compilation of trained models from different frameworks. The following figure shows a summary of Neo compilation of trained models.
The pre-trained model formats to be uploaded for compilation should be
Create or Open a Notebook instance. Please follow steps 1-4 in Amazon SageMaker to create a new notebook instance.
Model optimization for Person detection model can be downloaded from SageMaker Neo compilation example. Please refer to How to work with notebooks in Jupyterlab to get an overview of Jupyterlab.
Run the notebook cells for Neo compilation. Once the notebook cells have been run, the optimized model can be downloaded from the S3 bucket specified in the notebook.
In the SageMaker → Compilation Jobs page, we can see the current status of our job.
Select the desired Compilation job from the list by clicking on the name. Here we have the job details.