#python #asynchronous_programming #asyncio #deep_learning #deep_neural_networks #dht #distributed_systems #distributed_training #hivemind #mixture_of_experts #pytorch #volunteer_computing

https://github.com/learning-at-home/hivemind

#python #deep_learning #distributed_training #efficiency #machine_learning #neural_network #paddlepaddle #scalability

https://github.com/PaddlePaddle/Paddle

#python #data_parallelism #deep_learning #distributed_training #hpc #large_scale #model_parallelism #pipeline_parallelism

https://github.com/hpcaitech/ColossalAI

#python #blockchain #deep_learning #distributed_training #edge_ai #federated_learning #inference_engine #machine_learning #marketplace #mlops #on_device_training #privacy #security

https://github.com/FedML-AI/FedML

#python #cloud_computing #cloud_management #data_science #deep_learning #distributed_training #gpu #hyperparameter_tuning #job_queue #job_scheduler #machine_learning #ml_infrastructure #multicloud #serverless #spot_instances #tpu

https://github.com/skypilot-org/skypilot

#go #data_science #deep_learning #distributed_training #hyperparameter_optimization #hyperparameter_search #hyperparameter_tuning #kubernetes #machine_learning #ml_infrastructure #ml_platform #mlops #pytorch #tensorflow

https://github.com/determined-ai/determined

#python #augmix #convnext #distributed_training #dual_path_networks #efficientnet #image_classification #imagenet #maxvit #mixnet #mobile_deep_learning #mobilenet_v2 #mobilenetv3 #nfnets #normalization_free_training #pretrained_models #pretrained_weights #pytorch #randaugment #resnet #vision_transformer_models

PyTorch Image Models (`timm`) is a comprehensive library that includes a wide range of state-of-the-art image models, layers, utilities, optimizers, and training scripts. Here are the key benefits `timm` offers over 300 pre-trained models from various families like Vision Transformers, ResNets, EfficientNets, and more, allowing you to choose the best model for your task.
- **Pre-trained Weights** You can easily extract features at different levels of the network using `features_only=True` and `out_indices`, making it versatile for various applications.
- **Optimizers and Schedulers** It provides several augmentation techniques like AutoAugment, RandAugment, and regularization methods like DropPath and DropBlock to enhance model performance.
- **Reference Training Scripts**: Included are high-performance training, validation, and inference scripts that support multiple GPUs and mixed-precision training.

Overall, `timm` simplifies the process of working with deep learning models for image tasks by providing a unified interface and extensive tools for training and evaluation.

https://github.com/huggingface/pytorch-image-models