# 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\$1start notebook](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-remote-function/quick_start/quick_start.ipynb) 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\$1mnist.ipynb](https://github.com/aws/amazon-sagemaker-examples/tree/main/sagemaker-remote-function/pytorch_mnist_sample_notebook) 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](https://github.com/aws/amazon-sagemaker-examples/tree/main/sagemaker-remote-function/pytorch_mnist_sample_script). + To see how the XGBoost algorithm implemented with an @remote decorator: Open the [xgboost\$1abalone.ipynb](https://github.com/aws/amazon-sagemaker-examples/tree/main/sagemaker-remote-function/xgboost_abalone) notebook. + To see how Hugging Face is integrated with an @remote decorator: Open the [huggingface.ipynb](https://github.com/aws/amazon-sagemaker-examples/tree/main/sagemaker-remote-function/huggingface_text_classification) notebook.