Search Results for "parallel"
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Showing 69 open source projects for "parallel"
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parallel-ssh
Asynchronous parallel SSH client library.
parallel-ssh is an asynchronous parallel SSH library designed for large-scale automation. It differentiates itself from alternatives, other libraries and higher-level frameworks like Ansible or Chef in several ways. -
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nbmake
Pytest plugin for testing notebooks
Pytest plugin for testing and releasing notebook documentation. To raise the quality of scientific material through better automation. Research/Machine Learning Software Engineers who maintain packages/teaching materials with documentation written in notebooks. -
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CUDA Python
Performance meets Productivity
...The project is designed to simplify GPU programming by offering Pythonic abstractions while still exposing the full power of CUDA for advanced users. 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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Make It heavy
A Python framework that emulates Grok Heavy functionality
Make It heavy is a Python framework for producing deeper AI analysis through multi-agent orchestration. It is designed to emulate the style of Grok Heavy by splitting a user query into several specialized research angles. The system runs four agents in parallel so each one can explore the problem from a different perspective. It then combines their outputs into one unified answer through an intelligent synthesis step. The framework uses OpenRouter for model access and can also run in single-agent mode for simpler tasks. Overall, it is useful for users who want broader research coverage, richer reasoning diversity, and structured multi-agent responses from one command-line workflow. -
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Otter-Grader
A Python and R autograding solution
...It is designed to work with classes at any scale by abstracting away the autograding internals in a way that is compatible with any instructor's assignment distribution and collection pipeline. Otter supports local grading through parallel Docker containers, grading using the autograder platforms of 3rd party learning management systems (LMSs), the deployment of an Otter-managed grading virtual machine, and a client package that allows students to run public checks on their own machines. Otter is designed to grade Python scripts and Jupyter Notebooks, and is compatible with a few different LMSs, including Canvas and Gradescope. -
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GenAI Processors
GenAI Processors is a lightweight Python library
...Its central abstraction is the Processor, a unit of work that consumes an asynchronous stream of parts (text, images, audio, JSON) and produces another stream, making it natural to chain operations and keep everything streaming end-to-end. Processors can be composed sequentially (to build multi-step flows) or in parallel (to fan-out work and merge results), which makes sophisticated agent behaviors easy to express with simple operators. The library offers built-in processors for classic turn-based Gemini calls as well as Live API streaming, so you can mix “batch” and real-time interactions in the same graph. It leans on Python’s asyncio to coordinate concurrency, handle network I/O, and juggle background compute threads without blocking. -
7
jlens
Companion code for the global workspace interpretability paper
...The transformed vectors are decoded through the model’s own unembedding into ranked vocabulary predictions. The package can fit new lenses, load saved ones, apply them to prompts, and merge results from parallel fitting jobs. Interactive layer-by-position views reveal how token rankings evolve across the network and compare them with the model’s final output. It supports open-weight Hugging Face decoder models, with Qwen used in the included examples. The repository also provides synthetic evaluation data and an end-to-end notebook, but it is not maintained. -
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mosdepth
fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing
mosdepth is a fast BAM/CRAM depth calculation tool for genomic data, allowing efficient computation of sequencing coverage. -
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Kiro Crew
A persistent workspace for development work that self-improves
Kiro Crew is an open-source persistent development workspace that keeps AI-assisted work running beyond a single chat session. It can operate locally, in Docker, or on a remote machine controlled by the user. Sessions, memory, schedules, checkpoints, and learned preferences survive restarts and can influence later work. Long-running tasks can plan steps, execute them, validate results, retry failures, and resume from saved checkpoints. The system supports subagents, reusable skills, MCP... -
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nbdev
Create delightful software with Jupyter Notebooks
nbdev is a notebook-driven development platform (by fast.ai/AnswerDotAI) enabling you to write code, tests, documentation, and deploy software, all from Jupyter Notebooks. It provides a unified literate programming workflow where you can tag notebook cells for export to Python modules, auto-generate documentation via Quarto (and host it on GitHub Pages), run tests embedded in notebooks, manage clean notebooks with Git-friendly metadata hooks, and seamlessly publish packages to PyPI/conda,... -
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Blade Build System
Blade is a powerful build system from Tencent
An easy-to-use, fast and modern build system for trunk-based development in large-scale mono repo codebase. The code on the master branch is the development version and should be considered as alpha version. Please prefer using the version on the tags in your formal environment. We will release the verified version on the large-scale internal code base to the tag from time to time. -
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PyOpenCL
OpenCL integration for Python, plus shiny features
PyOpenCL is a Python wrapper for the OpenCL framework, providing seamless access to parallel computing on CPUs, GPUs, and other accelerators. It enables developers to harness the full power of heterogeneous computing directly from Python, combining Python’s ease of use with the performance benefits of OpenCL. PyOpenCL also includes convenient features for managing memory, compiling kernels, and interfacing with NumPy, making it a preferred choice in scientific computing, data analysis, and machine learning workflows that demand acceleration. -
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LangExtract
A Python library for extracting structured information
...LangExtract supports a wide range of models, including Google Gemini, OpenAI GPT, and local LLMs via Ollama, making it adaptable to different deployment environments and compliance needs. The system excels at handling long documents using optimized chunking, multi-pass extraction, and parallel processing to ensure both high recall and structured consistency. -
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julep
A new DSL and server for AI agents and multi-step tasks
Julep is a platform for creating AI agents that remember past interactions and can perform complex tasks. It offers long-term memory and manages multi-step processes. Julep enables the creation of multi-step tasks incorporating decision-making, loops, parallel processing, and integration with numerous external tools and APIs. While many AI applications are limited to simple, linear chains of prompts and API calls with minimal branching, Julep is built to handle more complex scenarios. -
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Buildbot
Python-based continuous integration testing framework
Buildbot is an open-source framework for automating software build, test, and release processes. At its core, Buildbot is a job scheduling system: it queues jobs, executes the jobs when the required resources are available, and reports the results. Your Buildbot installation has one or more masters and a collection of workers. The masters monitor source-code repositories for changes, coordinate the activities of the workers, and report results to users and developers. Workers run on a... -
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Triton
Development repository for the Triton language and compiler
...Triton enables users to write optimized kernels for machine learning workloads while maintaining readability and control over performance-critical aspects like memory access patterns and parallel execution. The project leverages LLVM and MLIR to compile code into efficient GPU instructions, supporting both NVIDIA and AMD hardware. It is widely used in research and production environments where custom tensor operations are required, offering both high performance and developer-friendly syntax. -
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Nevergrad
A Python toolbox for performing gradient-free optimization
Nevergrad is a Python library for derivative-free optimization, offering robust implementations of many algorithms suited for black-box functions (i.e. functions where gradients are unavailable or unreliable). It targets hyperparameter search, architecture search, control problems, and experimental tuning—domains in which gradient-based methods may fail or be inapplicable. The library provides an easy interface to define an optimization problem (parameter space, loss function, budget) and... -
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Stanza
Stanford NLP Python library for many human languages
...It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of speech and morphological features, to give a syntactic structure dependency parse, and to recognize named entities. The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism. Stanza is built with highly accurate neural network components that also enable efficient training and evaluation with your own annotated data. -
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AI Berkshire
AI-era Berkshire: a value investing research framework
...The project is meant to improve research depth, decision discipline, and analytical consistency compared with asking a general AI model for a one-off stock opinion. It uses parallel agent analysis, adversarial viewpoints, financial rigor checks, and repeatable report formats to reduce shallow or overly balanced conclusions. The framework covers company research, earnings review, industry screening, portfolio thinking, management analysis, and investment checklists. It is best understood as a research and decision-support system rather than a source of financial advice. -
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OptScale
FinOps and MLOps platform to run ML/AI and regular cloud workloads
Run ML/AI or any type of workload with optimal performance and infrastructure cost. OptScale allows ML teams to multiply the number of ML/AI experiments running in parallel while efficiently managing and minimizing costs associated with cloud and infrastructure resources. OptScale MLOps capabilities include ML model leaderboards, performance bottleneck identification and optimization, bulk run of ML/AI experiments, experiment tracking, and more. The solution enables ML/AI engineers to run automated experiments based on datasets and hyperparameter conditions within the defined infrastructure budget. ... -
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YAPF
A formatter for Python files
YAPF is a Python code formatter that automatically rewrites source to match a chosen style, using a clang-format–inspired algorithm to search for the “best” layout under your rules. Instead of relying on a fixed set of heuristics, it explores formatting decisions and chooses the lowest-cost result, aiming to produce code a human would write when following a style guide. You can run it as a command-line tool or call it as a library via FormatCode / FormatFile, making it easy to embed in... -
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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. ... -
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DeepEP
DeepEP: an efficient expert-parallel communication library
DeepEP is a communication library designed specifically to support Mixture-of-Experts (MoE) and expert parallelism (EP) deployments. Its core role is to implement high-throughput, low-latency all-to-all GPU communication kernels, which handle the dispatching of tokens to different experts (or shards) and then combining expert outputs back into the main data flow. Because MoE architectures require routing inputs to different experts, communication overhead can become a bottleneck — DeepEP... -
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Transcrypt
Python 3.7 to JavaScript compiler
...As the project caught on and the number of people contributing issues, ideas and code grew, the repo was transferred to the QQuick organization, to be able to form a developer team on GitHub. There's a simple parallel between the Python and the JavaScript code. In combination with the use of source maps, this enables efficient debugging. Also, code can be tested from the command prompt using stubs. Lightning-fast JavaScript 6 code: call caching, for-loop optimization, in-line JavaScript etc. Integrated static typechecking and minification at the tip of a command-line switch. ... -
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TextTest is an application-independent tool for text-based functional testing. This means running a batch-mode binary in lots of different ways, and using the text output produced as a means of controlling the behaviour of that application.
