#python #automl #computer_vision #data_science #deep_learning #distributed_computing #ensemble_learning #gluon #image_classification #machine_learning #mxnet #natural_language_processing #object_detection #pytorch #structured_data #transfer_learning

https://github.com/awslabs/autogluon

#python #distributed_computing #machine_learning #multiprocessing #sandbox

https://github.com/uber/fiber

#other #atomic_broadcast_protocol #consensus_algorithms #consistency #distributed_computing #distributed_systems #fault_tolerance #paxos #quorum_systems #replication #state_machine_replication #zookeeper

https://github.com/heidihoward/distributed-consensus-reading-list

#java #big_data #caching #data_in_motion #data_insights #distributed #distributed_computing #distributed_systems #hacktoberfest #hazelcast #in_memory #low_latency #real_time #scalability #stream_processing

https://github.com/hazelcast/hazelcast

#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 #ai #big_model #data_parallelism #deep_learning #distributed_computing #foundation_models #heterogeneous_training #hpc #inference #large_scale #model_parallelism #pipeline_parallelism

Colossal-AI is a powerful tool that helps make large AI models faster, cheaper, and easier to use. It uses special techniques like parallelism to speed up training on big models without needing expensive hardware. This means users can train complex AI models even on regular computers or laptops, saving time and money. Colossal-AI also supports various applications across industries like medicine, video generation, and chatbots, making it very versatile for developers.

https://github.com/hpcaitech/ColossalAI

#rust #artificial_intelligence #big_data #data_engineering #distributed_computing #machine_learning #multimodal #python #rust

Daft is a powerful, easy-to-use data engine that lets you process large-scale data using Python or SQL with high speed and efficiency. It supports complex data types like images and tensors, works well interactively for quick data exploration, and can scale to huge cloud clusters using Ray. Daft integrates smoothly with cloud storage and data catalogs, making it ideal for data engineering, analytics, and machine learning workflows. By using Daft, you can handle big, multimodal datasets faster and more flexibly, improving your ability to analyze and prepare data for AI models without complex setup or slowdowns.

https://github.com/Eventual-Inc/Daft