Write local debuggable Python which traverses your powerful remote infra. Deploy as-is. Unobtrusive, unopinionated, PyTorch-like APIs.
-
Updated
May 23, 2024 - Python
Write local debuggable Python which traverses your powerful remote infra. Deploy as-is. Unobtrusive, unopinionated, PyTorch-like APIs.
This is the Docker container based on open source framework XGBoost (https://xgboost.readthedocs.io/en/latest/) to allow customers use their own XGBoost scripts in SageMaker.
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
Foundation model benchmarking tool. Run any model on Amazon SageMaker and benchmark for performance across instance type and serving stack options.
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.
Know How Guide and Hands on Guide for AWS
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.
META LLAMA3 GENAI Real World UseCases End To End Implementation Guide
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
Amazon SageMaker Local Mode Examples
Probabilistic time series modeling in Python
AI book for everyone
This repository features three demos that can be effortlessly integrated into your AWS environment. They serve as a practical guide to leveraging AWS services for crafting a sophisticated Large Language Model (LLM) Generative AI, geared towards creating a responsive Question and Answer Bot and localizing content generation.
Stable-Diffusion-WebUI. One simple notebook for two environments: Colab/Kaggle.
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."