Distributed Computing Collection

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🔮Psychic

★★★★

“Making large AI models cheaper, faster and more accessible”

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📦Normal

★★★

“Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang”

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⚡Thunder

★★★

“Hazelcast is a unified real-time data platform combining stream processing with a fast data store, allowing customers to act instantly on data-in-motion for real-time insights.”

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✨Fairy

★★★

“Automate code & data workflows with interactive Elixir notebooks”

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⚙️Steel

★★★

“High-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scale”

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🌊Water

★★★

“Proto Actor - Ultra fast distributed actors for Go, C# and Java/Kotlin”

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🌊Water

★★

“Fast, efficient, and scalable distributed map/reduce system, DAG execution, in memory or on disk, written in pure Go, runs standalone or distributedly.”

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🔮Psychic

★★

“A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.”

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🔮Psychic

★★

“Accelerated deep learning R&D”

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🔮Psychic

★★

“Training and serving large-scale neural networks with auto parallelization.”

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🔥Fire

★★

“Distributed LLM inference. Connect home devices into a powerful cluster to accelerate LLM inference. More devices means faster inference.”

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⚙️Steel

★★

“zenoh unifies data in motion, data in-use, data at rest and computations. It carefully blends traditional pub/sub with geo-distributed storages, queries and computations, while retaining a level of time and space efficiency that is well beyond any of the mainstream stacks.”

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⚙️Steel

★★

“Drop-in Apache Spark replacement written in Rust, unifying batch processing, stream processing, and compute-intensive AI workloads.”

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📦Normal

★★

“A list of papers about distributed consensus.”

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🔮Psychic

★★

“Bare bone examples of machine learning in TensorFlow”

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🔥Fire

★★

“Open-source software for volunteer computing and grid computing.”

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🔮Psychic

★★

“Microsoft Graph Engine”

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🔮Psychic

★★

“A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.”