AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
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Updated
May 15, 2024 - Python
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
A library for training and deploying machine learning models on Amazon SageMaker
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
Write local debuggable Python which traverses your powerful remote infra. Deploy as-is. Unobtrusive, unopinionated, PyTorch-like APIs.
Terraform module to create and manage a SageMaker studio
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
This repo provides sample generative AI stacks built atop the AWS Generative AI CDK Constructs.
A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere) using AWS CDK on AWS
An easy way to enhance DeepRacer model training using DRfC functionalities through Jupyter Notebooks. Reproducing some core functionalities provided by AWS SageMaker Notebook
A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.
Foundation model benchmarking tool. Run any model on Amazon SageMaker and benchmark for performance across instance type and serving stack options.
Stable-Diffusion-WebUI. One simple notebook for two environments: Colab/Kaggle.
AI Makerspace: Blueprints for developing machine learning applications with state-of-the-art technologies.
Demo to run the MNIST handwritten digit model on a locally running SageMaker endpoint
A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)
A lambda function split preprocessed data into training and validation used for starting a training job within AWS SageMaker.
Add a description, image, and links to the sagemaker topic page so that developers can more easily learn about it.
To associate your repository with the sagemaker topic, visit your repo's landing page and select "manage topics."