Search Results for "distributed computing"
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Showing 199 open source projects for "distributed computing"
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1
NumPy
The fundamental package for scientific computing with Python
Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. ... -
2
fugue
A unified interface for distributed computing
Fugue is a unified interface for distributed computing that lets users execute Python, Pandas, and SQL code on Spark, Dask, and Ray with minimal rewrites. -
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Dask
Parallel computing with task scheduling
Dask is a Python library for parallel and distributed computing, designed to scale analytics workloads from single machines to large clusters. It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines. -
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Parallax
Parallax is a distributed model serving framework
Parallax is a decentralized inference framework designed to run large language models across distributed computing resources. Instead of relying on centralized GPU clusters in data centers, the system allows multiple heterogeneous machines to collaborate in serving AI inference workloads. Parallax divides model layers across different nodes and dynamically coordinates them to form a complete inference pipeline. A two-stage scheduling architecture determines how model layers are allocated to available hardware and how requests are routed across nodes during execution. ... -
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5
CUDA Python
Performance meets Productivity
...It integrates tightly with the broader Python GPU ecosystem, including Numba for kernel compilation and CCCL for parallel primitives, allowing developers to write performant code without leaving Python. The toolkit also includes utilities for profiling, memory management, distributed computing, and numerical operations, making it suitable for scientific computing, AI, and data processing workloads. -
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Numba
NumPy aware dynamic Python compiler using LLVM
...Special decorators can create universal functions that broadcast over NumPy arrays just like NumPy functions do. Numba also works great with Jupyter notebooks for interactive computing, and with distributed execution frameworks, like Dask and Spark. -
7
Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best... -
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Data-Juicer
Data processing for and with foundation models
Data-Juicer is an open-source data processing and augmentation framework designed to enhance the quality and diversity of datasets for machine learning tasks. It includes a modular pipeline for scalable data transformation. -
9
Xtuner
A Next-Generation Training Engine Built for Ultra-Large MoE Models
Xtuner is a large-scale training engine designed for efficient training and fine-tuning of modern large language models, particularly mixture-of-experts architectures. The framework focuses on enabling scalable training for extremely large models while maintaining efficiency across distributed computing environments. Unlike traditional 3D parallel training strategies, XTuner introduces optimized parallelism techniques that simplify scaling and reduce system complexity when training massive models. The engine supports training models with hundreds of billions of parameters and enables long-context training with sequence lengths reaching tens of thousands of tokens. ... -
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10
HolmesGPT
CNCF Sandbox Project
HolmesGPT is an open-source AI agent designed to help DevOps and site reliability engineering teams diagnose and resolve production incidents. The system aggregates signals from observability tools such as logs, metrics, alerts, and distributed traces, then analyzes them using large language models to identify potential root causes. Rather than requiring engineers to manually correlate large volumes of monitoring data, HolmesGPT automatically synthesizes evidence and presents explanations in natural language. The project is developed by Robusta and has been accepted as a Cloud Native Computing Foundation Sandbox project, highlighting its relevance to the cloud-native ecosystem. ... -
11
mlforecast
Scalable machine learning for time series forecasting
...It supports multi-series forecasting, meaning you can train one model that forecasts many time series at once (common in retail, demand forecasting, etc.), rather than one model per series. The library is built to scale: behind the scenes, it can leverage distributed computing frameworks (Spark, Dask, Ray) when datasets or the number of series grow large. -
12
Matrix
Multi-Agent daTa geneRation Infra and eXperimentation framework
Matrix is a distributed, large-scale engine for multi-agent synthetic data generation and experiments: it provides the infrastructure to run thousands of “agentic” workflows concurrently (e.g. multiple LLMs interacting, reasoning, generating content, data-processing pipelines) by leveraging distributed computing (like Ray + cluster management). The idea is to treat data generation as a “data-to-data” transformation: each input item defines a task, and the runtime orchestrates asynchronous, peer-to-peer agent workflows, avoiding global synchronization bottlenecks. ... -
13
Chitu
High-performance inference framework for large language models
...The framework focuses on improving efficiency, flexibility, and scalability for organizations that need to run LLM inference workloads across different hardware platforms. It supports heterogeneous computing environments, including CPUs, GPUs, and various specialized AI accelerators, allowing models to run across a wide range of infrastructure configurations. Chitu is designed to scale from small single-machine deployments to large distributed clusters that handle high volumes of concurrent inference requests. The system also includes performance optimizations for large models, including support for quantized formats and efficient computation operators that reduce memory usage and latency. ... -
14
Apache Hamilton
Helps data scientists define testable self-documenting dataflows
Apache Hamilton is an open-source Python framework designed to simplify the creation and management of dataflows used in analytics, machine learning pipelines, and data engineering workflows. The framework enables developers to define data transformations as simple Python functions, where each function represents a node in a dataflow graph and its parameters define dependencies on other nodes. Hamilton automatically analyzes these functions and constructs a directed acyclic graph... -
15
EvoTorch
Advanced evolutionary computation library built on top of PyTorch
EvoTorch is an evolutionary optimization framework built on top of PyTorch, developed by NNAISENSE. It is designed for large-scale optimization problems, particularly those that require evolutionary algorithms rather than gradient-based methods. -
16
DGL
Python package built to ease deep learning on graph
...DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for a better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks. -
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UNICORE
UNiform Interface to COmputing and data REsources
UNICORE is a software suite for building federated systems, providing secure and seamless access to heterogeneous resource such as compute clusters and file systems. UNICORE deals with authentication, user mapping and authorization, and provides a comprehensive set of RESTful APIs for HPC access and workflows. Contributors: visit https://github.com/UNICORE-EU -
18
FRODO 2
Open-Source Framework for Distributed Constraint Optimization (DCOP)
FRODO is a Java platform to solve Distributed Constraint Satisfaction Problems (DisCSPs) and Optimization Problems (DCOPs). It provides implementations for a variety of algorithms, including DPOP (and its variants), ADOPT, SynchBB, DSA... -
19
migrid
A grid middleware with minimal user and resource requirements
[This project moved to Github and is no longer maintained here] Minimum intrusion Grid (MiG) is an attempt to design a new platform for Grid computing which is driven by a stand-alone approach to Grid, rather than integration with existing systems. The goal of the MiG project is to provide Grid infrastructure where the requirements on users and resources alike is as small as possible (minimum intrusion). MiG strives for minimum intrusion but will seek to provide a feature rich and... -
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Render Farm Manager, Project Tracker.
CGRU: Afanasy render farm manager and RULES project tracker.
CGRU is an open source CG tools pack, includes Afanasy render farm manager and RULES project tracker. -
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lixa
LIXA, LIbre XA, is a free and open source XA transaction manager
LIXA (LIbre XA) is an open source and free Transaction Manager implementing the distributed transaction processing "XA specification" and "TX (transaction demarcation) specification" according to the X/Open CAE Specification. LIXA implements even XTA: XA Transaction API, an innovative API that implements XA transactional context passing among different applications. LIXA is a Transaction Manager but it's not a Transaction Monitor: this is the distinguishing feature of the project. LIXA... -
22
<<Hack|Track GNU/Linux
Distro Penetrasing Live System Burn to USB Flash Disk & Run.
<<Hack|Track GNU/Linux is an open source operating system developed by the HTGL Project from Indonesia which provides penetration testing. -
23
build nanoGPT
Video+code lecture on building nanoGPT from scratch
...The accompanying video lecture explains how the code develops into a reproduction of the 124-million-parameter GPT-2 model. The project covers tokenization, transformer architecture, optimization, distributed training, data loading, and performance improvements. It includes FineWeb data preparation and HellaSwag evaluation utilities. With sufficient computing resources, the same general code can scale toward larger GPT-3-style configurations. The repository focuses on pretraining rather than instruction tuning or conversational fine-tuning. -
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libptpmgmt
PTP Management library to communicate with linuxptp using IEEE 1558.
The libptpmgmt Project provides a library to communicate with LinuxPTP using IEEE 1558 management messages over a network. -
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TAME LLM
Traditional Mandarin LLMs for Taiwan
TAME LLM is an open-source initiative focused on building and releasing large language models optimized for Traditional Mandarin and the linguistic context of Taiwan. The project includes models such as Llama-3-Taiwan-70B, which are fine-tuned versions of large transformer architectures trained on extensive corpora containing both Traditional Mandarin and English text. These models are designed to support applications such as conversational AI, knowledge retrieval, and domain-specific...
