#python #deep_learning #pre_trained #model #awesome #nlp #vision #paddlehub #ai_models

https://github.com/PaddlePaddle/PaddleHub

#scala #ai #apache_spark #azure #big_data #cognitive_services #data_science #databricks #deep_learning #http #lightgbm #machine_learning #microsoft #ml #model_deployment #onnx #opencv #pyspark #spark #synapse

https://github.com/microsoft/SynapseML

#cplusplus #deployment #model_converter #ncnn #onnxruntime #openvino #pplnn #sdk #tensorrt

https://github.com/open-mmlab/mmdeploy

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

https://github.com/hpcaitech/ColossalAI

#python #data_analysis #data_drift #data_science #deep_learning #jupyter_notebook #machine_learning #machinelearning #ml #mlops #model_monitoring #monitoring #performance_monitoring #visualization

https://github.com/NannyML/nannyml

#python #caffe #computer_vision #coreml #edgetpu #keras #mediapipe #model #model_zoo #models #onnx #openvino #pretrained_models #pytorch #tensorflow #tensorflow_lite #tensorflowjs #tf_trt #tfjs #tflite #tflite_models

https://github.com/PINTO0309/PINTO_model_zoo

#jupyter_notebook #ai #artificial_intelligence #image_generation #img2img #latent_diffusion #machine_learning #model_training #stable_diffusion #txt2img

https://github.com/JoePenna/Dreambooth-Stable-Diffusion

#python #data_drift #data_science #hacktoberfest #html_report #jupyter_notebook #machine_learning #machine_learning_operations #mlops #model_monitoring #pandas_dataframe #production_machine_learning

https://github.com/evidentlyai/evidently

#python #data_drift #data_science #data_validation #deep_learning #html_report #jupyter_notebook #machine_learning #ml #mlops #model_monitoring #model_validation #pandas_dataframe #pytorch

https://github.com/deepchecks/deepchecks

#python #ai #ai_alignment #ai_safety #ai_test #ai_testing #artificial_intelligence #cicd #explainable_ai #llmops #machine_learning #machine_learning_testing #ml #ml_safety #ml_test #ml_testing #ml_validation #mlops #model_testing #model_validation #quality_assurance

https://github.com/Giskard-AI/giskard

#python #ai #control #decision_making #distributed_computing #machine_learning #marl #model_based_reinforcement_learning #multi_agent_reinforcement_learning #pytorch #reinforcement_learning #rl #robotics #torch

https://github.com/pytorch/rl

#python #large_language_models #model_para #transformers

Megatron-LM and Megatron-Core are powerful tools for training large language models (LLMs) on NVIDIA GPUs. Megatron-Core offers GPU-optimized techniques and system-level optimizations, allowing you to train custom transformers efficiently. It supports advanced parallelism strategies, activation checkpointing, and distributed optimization to reduce memory usage and improve training speed. You can use Megatron-Core with other frameworks like NVIDIA NeMo for end-to-end solutions or integrate its components into your preferred training framework. This setup enables scalable training of models with hundreds of billions of parameters, making it beneficial for researchers and developers aiming to advance LLM technology.

https://github.com/NVIDIA/Megatron-LM

#python #billion_parameters #compression #data_parallelism #deep_learning #gpu #inference #machine_learning #mixture_of_experts #model_parallelism #pipeline_parallelism #pytorch #trillion_parameters #zero

DeepSpeed is a powerful tool for training and using large artificial intelligence models quickly and efficiently. It allows you to train models with billions or even trillions of parameters, which is much faster and cheaper than other methods. With DeepSpeed, you can achieve significant speedups, reduce costs, and improve the performance of your models. For example, it can train ChatGPT-like models 15 times faster than current state-of-the-art systems. This makes it easier to work with large language models without needing massive resources, making AI more accessible and efficient for everyone.

https://github.com/microsoft/DeepSpeed