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accelerate

🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision

amazon-sagemaker-examples

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

amazon-transcribe-websocket-static

A static site demonstrating real-time audio transcription via Amazon Transcribe over a WebSocket.

aws-media-insights-engine

A serverless framework to accelerate the development of applications that discover next-generation insights in your video, audio, text, and image resources by utilizing AWS Machine Learning and Media services.

aws-parallelcluster-megatron

awsome-distributed-training

Collection of best practices, reference architectures, model training examples and utilities to train large models on AWS.

bert_sm_pl_ddp

Run a bert model build using Pytorch lightning on Amazon SageMaker.

cnn-lstm-network

Tensorflow implementation of embed CNN-LSTM network for sentiment analysis task.

create-react-app-auth-amplify

Implements a basic authentication flow for signing up/signing in users as well as protected client side routing using AWS Amplify.

deep-learning-containers

AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.

deeplearningexamples

Deep Learning Examples

deepspeed-llama-sagemaker

deepspeed-sagemaker-training

dgl-glmc-sagemaker

distributed-training-workshop

Distributed training workshop

dreambooth_sd_lora_sagemaker

enterprise-search-with-amazon-kendra-workshop

falcon-fine-tuning-sagemaker

falcon-pytorch-fsdp-sagemaker

Fine tune falcon 40b on Amazon SageMaker using Pytorch FSDP.

flan-t5-sagemaker-fine-tuning

flanul2-tuning-sagemaker

fraud-detection-workshop

gan_sm_pl_ddp

Run a GAN model built using Pytorch Lightning using SageMaker distributed training.

instruction-fine-tuning-gpt-sagemaker

llama-fine-tune-fsdp-sagemaker

llama2-70b-finetuning

llama2-fine-tuning-sagemaker

llama2-k8s

lora_fsdp_sagemaker_fine_tuning

ml-inference-workshop