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 13, 2024 - Jupyter Notebook
Foundation model benchmarking tool. Run any model on Amazon SageMaker and benchmark for performance across instance type and serving stack options.
A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)
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 lambda function split preprocessed data into training and validation used for starting a training job within AWS SageMaker.
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Write local debuggable Python which traverses your powerful remote infra. Deploy as-is. Unobtrusive, unopinionated, PyTorch-like APIs.
Stable-Diffusion-WebUI. One simple notebook for two environments: Colab/Kaggle.
A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.
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
Hands on lab for Neo4j and Amazon Bedrock
This repo provides sample generative AI stacks built atop the AWS Generative AI CDK Constructs.
Enabling DRfC functionalities via Jupyter Notebook as AWS SageMaker does to empower DeepRacer Model Training
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