Search Results for "python-gtksourceview2" - Page 54
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Showing 19324 open source projects for "python-gtksourceview2"
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Anthropic's Original Performance
Anthropic's original performance take-home, now open for you to try
...This take-home includes starter code, tests, and tools to debug performance, aiming to measure how effectively one can apply algorithmic improvements and optimizations. Because it’s framed around beating baseline scores — and even outperforming previous automated systems — it encourages both deep knowledge of Python and creative problem-solving. -
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deep-q-learning
Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
The deep-q-learning repository authored by keon provides a Python-based implementation of the Deep Q-Learning algorithm — a cornerstone method in reinforcement learning. It implements the core logic needed to train an agent using Q-learning with neural networks (i.e. approximating Q-values via deep nets), setting up environment interaction loops, experience replay, network updates, and policy behavior. -
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MegaTTS 3
Official PyTorch Implementation
MegaTTS3 is an open-source text-to-speech (TTS) and voice-cloning system from ByteDance that aims to deliver high-quality, expressive speech synthesis, including zero-shot voice cloning of previously unseen speakers. Its backbone is a lightweight diffusion-transformer (on the order of ~0.45 B parameters), which enables efficient inference while still producing high-fidelity audio. Given a reference audio sample (and corresponding latent representation), MegaTTS3 can generate speech in the... -
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Code-Mode
Plug-and-play library to enable agents to call MCP and UTCP tools
...The repository contains both TypeScript and Python libraries, plus a code-mode-mcp component for integrating with MCP and UTCP ecosystems. Benchmarks in the README highlight improvements in latency and token cost for scenarios involving multiple tools, showing that code execution often outperforms traditional JSON-based function calling. -
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plexe
Build a machine learning model from a prompt
...It aims to be production-minded: models can be exported, versioned, and deployed, with reports to explain performance and limitations. The project supports both a Python library and a managed cloud option, meeting teams wherever they prefer to run workloads. The overall goal is to compress the path from idea to usable model while keeping humans in the loop for review and adjustment. -
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Tilf
Tilf (Tiny Elf) is a free, simple yet powerful pixel art editor
Tilf (Tiny Elf) is a lightweight, cross-platform pixel art editor developed in Python with PySide6, designed for simplicity, speed, and freedom from account systems or installation overhead. It focuses on enabling artists to create sprites, icons, and small 2D assets quickly, without requiring setup, dependencies, or internet connectivity. Tilf provides a familiar drawing environment with essential tools—such as pencil, eraser, fill, eyedropper, rectangle, and ellipse—along with zoom, grid display, real-time preview, and undo/redo capabilities. ... -
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Gemma
Gemma open-weight LLM library, from Google DeepMind
Gemma, developed by Google DeepMind, is a family of open-weights large language models (LLMs) built upon the research and technology behind Gemini. This repository provides the official implementation of the Gemma PyPI package, a JAX-based library that enables users to load, interact with, and fine-tune Gemma models. The framework supports both text and multi-modal input, allowing natural language conversations that incorporate visual content such as images. It includes APIs for... -
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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. -
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cheat.sh
The only cheat sheet you need
...You can query it from the terminal (for example curl cht.sh/rsync or curl cheat.sh/ls) or browse the web front page; it also supports a shorthand hostname (cht.sh) and provides both online and standalone/local installation modes. The repository contains the server and client code, instructions to run a local standalone instance (including Python virtualenv setup), and tooling to fetch or maintain the upstream cheat-sheet data; installation documentation explains disk-space needs and dependency setup for offline use. Cheat.sh is intentionally minimal and scriptable, so it fits naturally into shells, CI scripts, editors, and quick lookups without leaving the terminal, while also offering ways to extend or host personal cheat sheets. -
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Posting
The modern API client that lives in your terminal
...Posting supports saving requests in a readable, version-control-friendly format, making it ideal for collaboration and reproducibility. It also includes scripting capabilities, enabling users to run Python code before and after requests to automate workflows. Overall, Posting brings a modern, customizable, and developer-centric API testing experience to the terminal. -
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NeMo Curator
Scalable data pre processing and curation toolkit for LLMs
...At the core of the NeMo Curator is the DocumentDataset which serves as the the main dataset class. It acts as a straightforward wrapper around a Dask DataFrame. The Python library offers easy-to-use methods for expanding the functionality of your curation pipeline while eliminating scalability concerns. -
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Taipy
Turns Data and AI algorithms into production-ready web applications
From simple pilots to production-ready web applications in no time. No more compromise on performance, customization, and scalability. Taipy enhances performance with caching control of graphical events, optimizing rendering by selectively updating graphical components only upon interaction. Effortlessly manage massive datasets with Taipy's built-in decimator for charts, intelligently reducing the number of data points to save time and memory without losing the essence of your data's shape.... -
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segment-geospatial
A Python package for segmenting geospatial data with the SAM
The segment-geospatial package draws its inspiration from segment-anything-eo repository authored by Aliaksandr Hancharenka. To facilitate the use of the Segment Anything Model (SAM) for geospatial data, I have developed the segment-anything-py and segment-geospatial Python packages, which are now available on PyPI and conda-forge. My primary objective is to simplify the process of leveraging SAM for geospatial data analysis by enabling users to achieve this with minimal coding effort. I have adapted the source code of segment-geospatial from the segment-anything-eo repository, and credit for its original version goes to Aliaksandr Hancharenka. -
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MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameter tuning to find the best model. ... -
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Spack
A flexible package manager that supports multiple versions
...Spack is a package manager for supercomputers, Linux, and macOS. It makes installing scientific software easy. Spack isn’t tied to a particular language; you can build a software stack in Python or R, link to libraries written in C, C++, or Fortran, and easily swap compilers or target specific microarchitectures. Spack offers a simple "spec" syntax that allows users to specify versions and configuration options. Package files are written in pure Python, and specs allow package authors to write a single script for many different builds of the same package. ... -
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nbstripout
strip output from Jupyter and IPython notebooks
...Useful mainly as a git filter or pre-commit hook for users who don't want to track output in VCS. This does mostly the same thing as the Clear All Output command in the notebook UI. You can download and install the latest version of nbstripout from PyPI, the Python package index. Strip output from IPython / Jupyter / Zeppelin notebook (modifies the file in-place). Usually, nbstripout is installed per repository so you can choose where to use it or not. You can choose to set the attributes in .gitattributes and commit this file to your repository, however there is no way to have git set up the filters automatically when someone clones a repository. ... -
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Django Lifecycle Hooks
Declarative model lifecycle hooks, an alternative to Signals
...Django's built-in approach to offering lifecycle hooks is Signals. However, my team often finds that Signals introduce unnecessary indirection and are at odds with Django's "fat models" approach. Django Lifecycle Hooks supports Python 3.7, 3.8 and 3.9, Django 2.0.x, 2.1.x, 2.2.x, 3.0.x, 3.1.x, and 3.2.x. For simple cases, you might always want something to happen at a certain point, such as after saving or before deleting a model instance. When a user is first created, you could process a thumbnail image in the background and send the user an email. -
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Strawberry GraphQL
A GraphQL library for Python that leverages type annotations
Python GraphQL library based on dataclasses. Strawberry's friendly API allows to create GraphQL API rather quickly, the debug server makes it easy to quickly test and debug. Django and ASGI support allow having your API deployed in production in a matter of minutes. The quick start method provides a server and CLI to get going quickly. -
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Ray
A unified framework for scalable computing
...Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms. Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework. Scale reinforcement learning (RL) with RLlib, a framework-agnostic RL library that ships with 30+ cutting-edge RL algorithms including A3C, DQN, and PPO. Easily build out scalable, distributed systems in Python with simple and composable primitives in Ray Core. -
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GRR
GRR Rapid Response, remote live forensics for incident response
GRR Rapid Response is an incident response framework focused on remote live forensics. It consists of a python client (agent) that is installed on target systems, and python server infrastructure that can manage and talk to clients. The goal of GRR is to support forensics and investigations in a fast, scalable manner to allow analysts to quickly triage attacks and perform analysis remotely. GRR client is deployed on systems that one might want to investigate. -
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Full Stack FastAPI and PostgreSQL
Full stack, modern web application generator
Generate a backend and frontend stack using Python, including interactive API documentation. Production ready Python web server using Uvicorn and Gunicorn. Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). Great editor support. Completion everywhere. Less time debugging. Designed to be easy to use and learn. Less time reading docs. Minimize code duplication. -
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fastai
Deep learning library
...This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai is organized around two main design goals: to be approachable and rapidly productive, while also being deeply hackable and configurable. It is built on top of a hierarchy of lower-level APIs which provide composable building blocks. -
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Pipenv
Python Development Workflow for Humans
Pipenv is a package manager that brings all the best of the packaging world together to the Python world. It's got everything you could need: bundler, composer, npm, cargo, yarn and more all in one convenient package so you can easily set up a working environment. Pipenv creates and manages a virtualenv automatically, and can add or remove packages from your Pipfile as you install/uninstall packages. It also produces the Pipfile.lock, which is essential for deterministic builds. ... -
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Saleor
Customer-centric e-commerce on a modern stack
Saleor is one of the fastest growing open source e-commerce platforms, built to deliver ultra-fast, dynamic and personalized shopping experiences. Built with Python, Django, GraphQL, and ReactJS, Saleor is modular and highly performant. With a GraphQL API and headless commerce, you can build beautiful, customized online stores anywhere on any device using the latest technology. Saleor gives you great flexibility, with options for building your front-end how you want, and seamless integrations with accounting and inventory systems. ... -
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GraphEmbedding
Implementation and experiments of graph embedding algorithms
GraphEmbedding is an open-source Python project for implementing and experimenting with graph embedding algorithms. It follows a simple “graph in, embedding out” design. The library uses NetworkX graphs as input and produces vector representations for graph nodes. It includes implementations of DeepWalk, LINE, Node2Vec, SDNE, and Struc2Vec. Users can configure walks, embedding dimensions, training windows, epochs, and other model-specific parameters.
