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1P_kmeans_highlevel
1P_kmeans_lowlevel
chainer_cifar10
chainer_mnist
chainer_sentiment_analysis
mxnet_gluon_cifar10
mxnet_gluon_mnist
mxnet_gluon_sentiment
mxnet_mnist
mxnet_onnx_export
mxnet_onnx_superresolution
pytorch_cnn_cifar10
pytorch_lstm_word_language_model
pytorch_mnist
scikit_learn_inference_pipeline
scikit_learn_iris
sparkml_serving_emr_mleap_abalone
tensorflow_abalone_age_predictor_using_keras
tensorflow_abalone_age_predictor_using_layers
tensorflow_distributed_mnist
tensorflow_iris_dnn_classifier_using_estimators
tensorflow_keras_cifar10
tensorflow_pipemode_example
tensorflow_resnet_cifar10_with_tensorboard
tensorflow_script_mode_horovod
tensorflow_script_mode_quickstart
tensorflow_script_mode_using_shell_commands
tensorflow_serving_container
tensorflow_using_elastic_inference_with_your_own_model
README.md

README.md

Amazon SageMaker Examples

Amazon SageMaker Pre-Built Framework Containers and the Python SDK

Pre-Built Deep Learning Framework Containers

These examples focus on the Amazon SageMaker Python SDK which allows you to write idiomatic TensorFlow or MXNet and then train or host in pre-built containers.

Pre-Built Machine Learning Framework Containers

These examples focus on building standard Machine Learning models powered by frameworks like Apache Spark or Scikit-learn using SageMaker Python SDK.