Example notebooks

You can transform a training code in an existing workspace environment and any associated data processing code and datasets into a SageMaker training job. The following notebooks show you how to customize your environment, job settings, and more for an image classification problem, using the XGBoost algorithm and Hugging Face.

The quick_start notebook contains the following code examples:

• How to customize your job settings with a configuration file.

• How to invoke Python functions as jobs, asynchronously.

• How to customize the job runtime environment by bringing in additional dependencies.

• How to use local dependencies with the @remote function method.

The following notebooks provide additional code examples for different ML problems types and implementations.

• To see code examples to use the @remote decorator for an image classification problem, open the pytorch_mnist.ipynb notebook. This classification problem recognizes handwritten digits using the Modified National Institute of Standards and Technology (MNIST) sample dataset.

• To see code examples for using the @remote decorator for the previous image classification problem with a script, see the Pytorch MNIST sample script, train.py.

• To see how the XGBoost algorithm implemented with an @remote decorator: Open the xgboost_abalone.ipynb notebook.

• To see how Hugging Face is integrated with an @remote decorator: Open the huggingface.ipynb notebook.