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
- Notebook CI Test Results
- 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
- Notebook CI Test Results
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
- 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
- Notebook CI Test Results
- 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