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
• Training and hosting SageMaker Models using the Apache MXNet Module API
• MNIST Training with MXNet and Gluon
• Training Graph Convolutional Matrix Completion by using the Deep Graph Library with MXNet backend on Amazon SageMaker
• Output

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

• Train an MNIST model with PyTorch
• Deploy a Trained PyTorch Model
• Word-level language modeling using PyTorch
• MNIST Training using PyTorch
• MNIST Training using PyTorch and deploy it with Elastic Inference
• MNIST Training using PyTorch

R

• R Examples
  • Using R with Amazon SageMaker - Basic Notebook
  • Using R in SageMaker Processing
  • Batch Transform Using R with Amazon SageMaker
  • Hyperparamter Optimization Using R with Amazon SageMaker
  • Hyperparameter Tuning Your Own R Algorithm with Your Own Container in Amazon SageMaker
  • R Serving with Plumber
  • Compare built-in Sagemaker classification algorithms for a binary classification problem using Iris dataset
  • Create model 1: XGBoost model in SageMaker
  • Create model 2: Linear Learner in SageMaker
  • Create model 3: KNN in SageMaker
  • Compare the AUC of 3 models for the test data
  • Clean up
  • R Serving with FastAPI
  • R Serving with RestRserve

Scikit-learn

• Develop, Train, Optimize and Deploy Scikit-Learn Random Forest
• Develop, Train, Register and Batch Transform Scikit-Learn Random Forest
• Inference Pipeline with Scikit-learn and Linear Learner
• Preprocessing data and training the model
• Serial Inference Pipeline with Scikit preprocessor and Linear Learner
• Iris Training and Prediction with Sagemaker Scikit-learn
• Iris Training and Prediction with Sagemaker 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
• Using Amazon Elastic Inference with Neo-compiled TensorFlow model on SageMaker

JAX

• Training and Deploying ML Models using JAX on SageMaker