Open Source Python Software - Page 66
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Python Software
Browse free open source Python Software and projects below. Use the toggles on the left to filter open source Python Software by OS, license, language, programming language, and project status.
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drf-yasg
Automated generation of real Swagger/OpenAPI 2.0 schemas
Only the latest version of drf-yasg is supported. Support of old versions is dropped immediately with the release of a new version. Please do not create issues before upgrading to the latest release available at the time. Regression reports are accepted and will be resolved with a new release as quickly as possible. Removed features will usually go through a deprecation cycle of a few minor releases. If you are looking to add Swagger/OpenAPI support to a new project you might want to take a look at drf-spectacular, which is an actively maintained new library that shares most of the goals of this project, while working with OpenAPI 3.0 schemas. OpenAPI 3.0 provides a lot more flexibility than 2.0 in the types of API that can be described. drf-yasg is unlikely to soon, if ever, get support for OpenAPI 3.0. -
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fairseq2
FAIR Sequence Modeling Toolkit 2
fairseq2 is a modern, modular sequence modeling framework developed by Meta AI Research as a complete redesign of the original fairseq library. Built from the ground up for scalability, composability, and research flexibility, fairseq2 supports a broad range of language, speech, and multimodal content generation tasks, including instruction fine-tuning, reinforcement learning from human feedback (RLHF), and large-scale multilingual modeling. Unlike the original fairseq—which evolved into a large, monolithic codebase—fairseq2 introduces a clean, plugin-oriented architecture designed for long-term maintainability and rapid experimentation. It supports multi-GPU and multi-node distributed training using DDP, FSDP, and tensor parallelism, capable of scaling up to 70B+ parameter models. The framework integrates seamlessly with PyTorch 2.x features such as torch.compile, Fully Sharded Data Parallel (FSDP), and modern configuration management. -
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fastai
Deep learning library
fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. 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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fastdup
An unsupervised and free tool for image and video dataset analysis
fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale. -
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git-cola
git-cola: The highly caffeinated Git GUI
Git Cola is a sleek and powerful graphical user interface for Git. Git Cola is free software and written in Python (v2 + v3). Git Cola uses QtPy, so you can choose between PyQt6, PyQt5 and PySide2 by setting the QT_API environment variable to pyqt6, pyqt5 or pyside2 as desired. qtpy defaults to pyqt6 and falls back to pyqt6 and pyside2 if pyqt5 is not installed. Git Cola enables additional features when the following Python modules are installed. send2trash enables cross-platform "Send to Trash" functionality. Never run pip install or make install as root or outside of a Python virtualenv! If you don't have PyQt installed then the easiest way to get it is to use a Python virtualenv and install Git Cola into it in "editable" mode. This install method lets you upgrade Git Cola by running git pull. -
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git-delete-merged-branches
Command-line tool to delete merged Git branches
A convenient command-line tool helping you keep repositories clean. Supports deletion of both local and remote branches. Detects multiple forms of de-facto merges (rebase merges, squash merges (needs --effort=3), single or range cherry-picks… leveraging git cherry). Supports workflows with multiple release branches, e.g. only delete branches that have been merged to all of master, dev and staging. Quick interactive configuration. Provider agnostic: Works with GitHub, GitLab, Gitea and any other Git hosting. Takes safety seriously. Deletion is a sharp knife that requires care. While git reflog would have your back in most cases, git-delete-merged-branches takes safety seriously. git push is used with --force-with-lease so if the server and you have a different understanding of that branch, it is not deleted. There is no use of os.system or shell code to go wrong. -
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gitfs
Version controlled file system
gitfs is a FUSE file system that fully integrates with git. You can mount a remote repository's branch locally, and any subsequent changes made to the files will be automatically committed to the remote. gitfs was developed by the awesome engineering team at Presslabs, a Managed WordPress Hosting provider. gitfs was designed to bring the full powers of git to everyone, no matter how little they know about versioning. A user can mount any repository and all their changes will be automatically converted into commits. gitfs will also expose the history of the branch you're currently working on by simulating snapshots of every commit. gitfs is useful in places where you want to keep track of all your files, but at the same time you don't have the possibility of organizing everything into commits yourself. A FUSE filesystem for git repositories, with local cache. We provide packages for the major Ubuntu releases and MacOS. -
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gpt-j-api
API for the GPT-J language mode. Including a FastAPI backend
An API to interact with the GPT-J language model and variants! You can use and test the model in two different ways. These are the endpoints of the public API and require no authentication. Just SSH into a TPU VM. This code was tested on both the v2-8 and v3-8 variants. -
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ipyvizzu
Build animated charts in Jupyter Notebook and similar environments
ipyvizzu - Build animated charts in Jupyter Notebook and similar environments with a simple Python syntax ipyvizzu is an animated charting tool for Jupyter, Google Colab, Databricks, Kaggle and Deepnote notebooks among other platforms. ipyvizzu enables data scientists and analysts to utilize animation for storytelling with data using Python. It's built on the open-source JavaScript/C++ charting library Vizzu. There is a new extension of ipyvizzu, ipyvizzu-story with which the animated charts can be presented right from the notebooks. Since ipyvizzu-story's syntax is a bit different to ipyvizzu's, we suggest you to start from the ipyvizzu-story repo if you're interested in using animated charts to present your findings live or to share your presentation as an HTML file. -
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jello
CLI tool to filter JSON and JSON Lines data with Python syntax
Filter JSON and JSON Lines data with Python syntax. jello is similar to jq in that it processes JSON and JSON Lines data except jello uses standard python dict and list syntax. JSON or JSON Lines can be piped into jello via STDIN or can be loaded from a JSON file or JSON Lines files (JSON Lines are automatically slurped into a list of dictionaries). Once loaded, the data is available as a python list or dictionary object named '_'. Processed data can be output as JSON, JSON Lines, bash array lines, or a grep-able schema. -
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kamiFaka
kamiFaka
Applicable to all kinds of e-commerce, coupons, forum invitation codes, recharge cards, activation codes, registration codes, Tencent iQiyi points CDK, etc., support manual and automatic delivery, and a tiered wholesale model similar to 1688. Stisla UI: The web interface is beautiful Front-end uses VUE3.0, millisecond-level response. Has integrated Alipay face-to-face payment, WeChat official, Payjs, Hupijiao, YunGouOS, Yipay, Mugglepay, V visa-free and other more than a dozen payment interfaces. Ordinary users support email and SMS to receive messages. Administrators support email, SMS, TG, WeChat, QQ notifications. Support 2~4 layer wholesale mode. Long card password can be exported as txt text. A variety of theme modes [list, card, grid]. Support custom background, logo, contact information, etc. Support hot backup, one-click cloud backup, one-click export backup file to local. Detachable database, compatible with Mysql, PostgreSQL and Sqlite. -
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kapture
Tools for manipulating datasets
Kapture is a pivot file format, based on text and binary files, used to describe SfM (Structure From Motion) and more generally sensor-acquired data. -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines of code, ktrain allows you to easily and quickly. ktrain purposely pins to a lower version of transformers to include support for older versions of TensorFlow. If you need a newer version of transformers, it is usually safe for you to upgrade transformers, as long as you do it after installing ktrain. As of v0.30.x, TensorFlow installation is optional and only required if training neural networks. -
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mac-cleanup-py
Python cleanup script for macOS
mac-cleanup-py is a powerful cleanup script for macOS. This project is a rewrite of the original mac-cleanup-sh rewritten in Python. -
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macOS Security Compliance
macOS Security Compliance Project
The macOS Security Compliance Project is an open source effort to provide a programmatic approach to generating security guidance. The configuration settings in this document were derived from National Institute of Standards and Technology (NIST) Special Publication (SP) 800-53, Security and Privacy Controls for Information Systems and Organizations, Revision 5. This is a joint project of federal operational IT Security staff from the National Institute of Standards and Technology (NIST), National Aeronautics and Space Administration (NASA), Defense Information Systems Agency (DISA), and Los Alamos National Laboratory (LANL). -
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manage-fastapi
CLI tool for FastAPI. Generating new FastAPI projects & boilerplates
manage-fastapi Project generator and manager for FastAPI. -
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mediapy
This Python library makes it easy to display images and videos
Read/write/show images and videos in an IPython/Jupyter notebook. -
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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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nb2mail
Send a notebook as an email
This repo contains a jupyter nbconvert exporter to convert notebooks to multipart MIME, and a postprocessor to send it via smtp. nb2mail does not do anything by itself. It provides an export format ("mail") and postprocessor ("SendMailPostProcessor"). Please see the nbconvert documentation and example configuration for more information. -
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nbcelltests
Cell-by-cell testing for production Jupyter notebooks in JupyterLab
nbcelltests is designed for writing tests for linearly executed notebooks. Its primary use is for unit testing reports. Cell-by-cell testing for production Jupyter notebooks in JupyterLab. To use in JupyterLab, you will also need the lab and server extensions. Typically, these are automatically installed alongside nbcelltests, so you should not need to do anything special to use them. The lab extension will require a rebuild of JupyterLab, which you'll be prompted to do on starting JupyterLab the first time after installing celltests (or you can do it manually with jupyter lab build). Note that you must have node.js installed (as for any lab extension). -
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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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npm-clang-format-node
Node repackaging(wrapping) of the LLVM Clang's clang-format
A Node.js package that integrates the clang-format tool for formatting C, C++, and JavaScript code, enhancing code quality and consistency. -
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openTSNE
Extensible, parallel implementations of t-SNE
openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE) [1], a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings [2], massive speed improvements [3] [4] [5], enabling t-SNE to scale to millions of data points, and various tricks to improve the global alignment of the resulting visualizations. -
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orjson
Fast, correct Python JSON library supporting dataclasses, datetimes
orjson is a fast, correct JSON library for Python. It benchmarks as the fastest Python library for JSON and is more correct than the standard json library or other third-party libraries. It serializes dataclass, datetime, numpy, and UUID instances natively. orjson supports CPython 3.8, 3.9, 3.10, 3.11, and 3.12. It distributes amd64/x86_64, aarch64/armv8, arm7, POWER/ppc64le, and s390x wheels for Linux, amd64 and aarch64 wheels for macOS, and amd64 and i686/x86 wheels for Windows. orjson does not support PyPy. Releases follow semantic versioning and serializing a new object type without an opt-in flag is considered a breaking change. -
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oscar
Domain-driven eCommerce for Django
Oscar is an open-source eCommerce framework for Django. Oscar makes few assumptions about your project, allowing virtually any part of the framework to be extended and customized. In this way, complex business rules can be captured in an elegant and cohesive way. Any class can be overridden, replaced and extended. This is the key feature that allows any project to be captured accurately in the codebase, no workarounds! A well-designed set of models built on the experience of many e-commerce projects, both large and small. Comprehensive documentation including recipes for solving common problems. Handling of a catalogue of around 15 million products, with stock provided by a range of international partners. Stock and biblio updates happening continuous using a Celery-driven backend. Integration with a series of SAP webservices to provide catalogue and inventory updates.