Statistics for topic distributed-computing
RepositoryStats tracks 584,797 Github repositories, of these 196 are tagged with the distributed-computing topic. The most common primary language for repositories using this topic is Python (64). Other languages include: Go (16), C++ (13), Rust (13), Jupyter Notebook (12)
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Making large AI models cheaper, faster and more accessible
Distributed data engine for Python/SQL designed for the cloud, powered by Rust
Gliimly is a new programming language and framework for writing high-performance web services and web applications, with focus on simplicity.
Run serverless GPU workloads with fast cold starts on bare-metal servers, anywhere in the world
A crowdsourced distributed cluster for AI art and text generation
Gliimly is a new programming language and framework for writing high-performance web services and web applications, with focus on simplicity.
The default client software to create images for the AI-Horde
Run serverless GPU workloads with fast cold starts on bare-metal servers, anywhere in the world
A crowdsourced distributed cluster for AI art and text generation
Making large AI models cheaper, faster and more accessible
Gliimly is a new programming language and framework for writing high-performance web services and web applications, with focus on simplicity.
37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 20 datasets.
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
Distributed data engine for Python/SQL designed for the cloud, powered by Rust
Gliimly is a new programming language and framework for writing high-performance web services and web applications, with focus on simplicity.
The default client software to create images for the AI-Horde
Run serverless GPU workloads with fast cold starts on bare-metal servers, anywhere in the world
The PennyLane-Lightning plugin provides a fast state-vector simulator written in C++ for use with PennyLane
Super-Efficient RLHF Training of LLMs with Parameter Reallocation
Distributed data engine for Python/SQL designed for the cloud, powered by Rust
Making large AI models cheaper, faster and more accessible
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 20 datasets.
Tensor parallelism is all you need. Run LLMs on an AI cluster at home using any device. Distribute the workload, divide RAM usage, and increase inference speed.
Gliimly is a new programming language and framework for writing high-performance web services and web applications, with focus on simplicity.
Super-Efficient RLHF Training of LLMs with Parameter Reallocation
Small scale distributed training of sequential deep learning models, built on Numpy and MPI.
Run serverless GPU workloads with fast cold starts on bare-metal servers, anywhere in the world
Tensor parallelism is all you need. Run LLMs on an AI cluster at home using any device. Distribute the workload, divide RAM usage, and increase inference speed.
[NeurIPS 2024] AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising
Super-Efficient RLHF Training of LLMs with Parameter Reallocation
Making large AI models cheaper, faster and more accessible
Tensor parallelism is all you need. Run LLMs on an AI cluster at home using any device. Distribute the workload, divide RAM usage, and increase inference speed.
Distributed data engine for Python/SQL designed for the cloud, powered by Rust
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 20 datasets.
Tensor parallelism is all you need. Run LLMs on an AI cluster at home using any device. Distribute the workload, divide RAM usage, and increase inference speed.
The default client software to create images for the AI-Horde
A next-generation dynamic and high-performance language for AI and IOT with natural born distributed computing ability.
An understandable, fast and scalable Raft Consensus implementation