Foundation model benchmarking tool. Run any model on Amazon SageMaker and benchmark for performance across instance type and serving stack options.
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Updated
May 29, 2024 - Jupyter Notebook
Foundation model benchmarking tool. Run any model on Amazon SageMaker and benchmark for performance across instance type and serving stack options.
Write local debuggable Python which traverses your powerful remote infra. Deploy as-is. Unobtrusive, unopinionated, PyTorch-like APIs.
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
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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Probabilistic time series modeling in Python
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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
A library for training and deploying machine learning models on Amazon SageMaker
Stable-Diffusion-WebUI. One simple notebook for two environments: Colab/Kaggle.
A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)
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.
A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.
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
Amazon SageMaker Local Mode Examples
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