Frameworks and Libraries
Examples on how to use different frameworks on SageMaker.
Apache MXNet
Deep Graph Library
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
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.
- Using TensorFlow Scripts in SageMaker - Quickstart
- TensorFlow script mode training and serving
- Visualize Amazon SageMaker Training Jobs with TensorBoard
- Using Amazon Elastic Inference with Neo-compiled TensorFlow model on SageMaker