Frameworks and Libraries
Examples on how to use different frameworks on SageMaker.
Apache MXNet
- Building an image embedding server with Gluon
- Sentiment Analysis with Apache MXNet and Gluon
- Implementing a Recommender System with SageMaker, MXNet, and Gluon
- Importing and hosting an ONNX model with MXNet
- Exporting ONNX Models with MXNet
- Using the Apache MXNet Module API with SageMaker Training and Batch Transformation
- Using Amazon Elastic Inference with MXNet on Amazon SageMaker
Deep Graph Library
- Graph convolutional matrix completion hyperparameter tuning with Amazon SageMaker and Deep Graph Library with MXNet backend
- Training Amazon SageMaker models for molecular property prediction by using DGL with PyTorch backend
- Hyperparameter tuning with Amazon SageMaker for molecular property prediction
- Training Amazon SageMaker models by using the Deep Graph Library with MXNet backend
- Hyperparameter tuning with Amazon SageMaker and Deep Graph Library with MXNet backend
- Training Amazon SageMaker models by using the Deep Graph Library with PyTorch backend
- Hyperparameter tuning with Amazon SageMaker and Deep Graph Library with PyTorch backend
PyTorch
R
Scikit-learn
TensorFlow
- Train an MNIST model with TensorFlow
- Deploy a Trained TensorFlow V2 Model
- Migrating scripts from Framework Mode to Script Mode
- Horovod Distributed Training with SageMaker TensorFlow script mode.
- TensorFlow Script Mode with Pipe Mode Input
- Using TensorFlow Scripts in SageMaker - Quickstart
- TensorFlow Script Mode - Using Shell scripts
- TensorFlow Eager Execution with Amazon SageMaker Script Mode and Automatic Model Tuning
- TensorFlow script mode training and serving
- Visualize Amazon SageMaker Training Jobs with TensorBoard