aws--amazon-sagemaker-examples/sagemaker-python-sdk

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.

• MNIST with MXNet Gluon
• MNIST with MXNet
• CIFAR-10 with Chainer and ChainerMN
• Sentiment Analysis with Chainer
• MNIST with Chainer
• Sentiment Analysis with MXNet Gluon
• IRIS with Scikit-learn
• Visualize Amazon SageMaker Training Jobs with TensorBoard (CIFAR-10, TensorFlow 2.2)
• Managed Spot Training on MXNet
• Managed Spot Training on TensorFlow

Using pre-trained ONNX models with Amazon SageMaker

• Importing and hosting Super Resolution ONNX model in MXNet
• Hosting ONNX models with Amazon Elastic Inference
• Exporting ONNX Models with MXNet

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.

• Inference with SparkML Serving
• Pipeline Inference with Scikit-learn and LinearLearner