Saravanan666/pypi-org huggingface dataset:
Package: opentuner
Program autotuning has been demonstrated in many domains to achieve
better or more portable performance. However, autotuners themselves are
often not very portable between projects because using a domain informed
search space representation is critical to achieving good results and
because no single search technique performs best for all problems.OpenTuner is a new framework for building domain-specific
multi-objective program autotuners. OpenTuner supports fully
customizable configuration representations, an extensible technique
representation to allow for domain-specific techniques, and an easy to
use interface for communicating with the tuned program. A key capability
inside OpenTuner is the use of ensembles of disparate search techniques
simultaneously, techniques which perform well will receive larger
testing budgets and techniques which perform poorly will be disabled.InstallationOpenTuner requires python 3.7+ and sqlite3 (or yoursupporteddatabase backend of choice). Install with:sudo pip install opentunerorpip install --user opentunerDevelopment installationFor development or running examples out of a git checkout, we recommend
usingminiconda3.conda create --name=opentuner python=3.8
conda activate opentuner
pip install -r requirements.txt -r optional-requirements.txt
python setup.py developChecking InstallationTo check an installation you can run tests:pytest tests/*Or run an example program:./examples/rosenbrock/rosenbrock.pyTutorialsOptimizing Block Matrix
MultiplicationCreating OpenTuner
Techniques.PapersOpenTuner: An Extensible Framework for Program
Autotuning.
Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan
Ragan-Kelley, Jeffrey Bosboom, Una-May O’Reilly, Saman Amarasinghe.
International Conference on Parallel Architectures and Compilation
Techniques. Edmonton, Canada. August, 2014.Slides.Bibtex.Contributing CodeThe preferred way to contribute code to OpenTuner is to fork the project
on github andsubmit a pull
request.SupportOpenTuner is supported in part by the United States Department of EnergyX-Stackprogram as part ofD-TEC.
Package: opentypespec
opentypespecThis package currently provides theopentypespec.tagsmodule, which
exportsFEATURE_TAGS,SCRIPT_TAGS,LANGUAGE_TAGSandBASELINE_TAGSdictionaries, representing data from the
Layout Tag Registry (section 6.4 of the Open Font Format specification):>>>fromopentypespec.tagsimportSCRIPT_TAGS,LANGUAGE_TAGS>>>SCRIPT_TAGS["adlm"]{'name':'Adlam'}>>>LANGUAGE_TAGS["ECR "]{'name':'Eastern Cree','iso639':['crj','crl']}Data derived from the ISO/IEC 14496-22 “Open Font Format” specification.The Python code in this module is licensed under the Apache Software License 2.0History1.8.4 (2021-06-28)First release on PyPI.
Package: opentypesvg
Tools for making OpenType-SVG fontsaddsvgadds an SVG table to a font, using SVG files provided. The font's format can be either OpenType or TrueType.dumpsvgsaves the contents of a font's SVG table as individual SVG files. The font's format can be either OpenType, TrueType, WOFF, or WOFF2.fonts2svggenerates a set of SVG glyph files from one or more fonts and hex colors for each of them. The fonts' format can be either OpenType, TrueType, WOFF, or WOFF2.DependenciesPython 3.6 or higherFontTools3.1.0 or higherInstallation instructionsMake sure you have Python 3.6 (or higher) installed:python --versionSetup a virtual environment:python -m venv ot-svgActivate the environment:source ot-svg/bin/activateUpdatepip:pip install -U pipInstallopentypesvg:pip install opentypesvgHow to make OpenType-SVG fonts?Step 1Generate a set of SVG files from a series of fonts and color values.fonts2svg -c 99ccff,ff0066,cc0066 fonts/Zebrawood-Shadow.otf fonts/Zebrawood-Fill.otf fonts/Zebrawood-Dots.otfStep 2Add a set of SVG files to an existing OpenType (or TrueType) font.addsvg fonts/SVGs fonts/Zebrawood.otfYou can useStep 2without doingStep 1, but there are a few things you need to be aware of when using theaddsvgtool:After the SVG files are saved with the authoring application (e.g. Adobe Illustrator, CorelDRAW!, Inkscape) they should be put thru a process that optimizes and cleans up the SVG code; this will slim down the file size while keeping the resulting artwork the same. For this step you can use one of these tools:SVG Cleaner(GUI version)SVG Cleaner(command line version)SVG OptimizerScourpicosvgaddsvgrequires the SVG files to be named according to the glyphs which they are meant to be associated with. For example, if the glyph in the font is namedampersand, the SVG file must be namedampersand.svg.addsvgexpects the color artwork to have been designed at the same size as the glyphs in the font, usually 1000 or 2048 UPM. This means 1 point (pt) in the authoring app equals 1 unit in font coordinates. If the artwork's canvas size is not the same as the font's UPM value, use the-koption to prevent the tool from removing the SVG'sviewBoxparameter. Retaining theviewBoxparameter will enable the artwork to be scaled to the full extent of the viewport (i.e. the font's UPM).If the artwork's<svg>element containsheightand/orwidthparameters, remove them, otherwise the artwork will have a fixed size and won't be allowed to scale to the full extent of the viewport.
Package: openue
中文说明|EnglishOpenUE is a lightweight toolkit for knowledge graph extraction.OpenUE是一个轻量级知识图谱抽取工具。特点基于预训练语言模型的知识图谱抽取任务 (兼容BERT, Roberta等预训练模型.)实体关系抽取事件抽取槽位和意图抽取更多的任务训练和测试接口快速部署NLP模型环境python3.8requirements.txt框架图其中主要分为三个模块，models,lit_models和data模块。models 模块其存放了我们主要的三个模型，针对整句的关系识别模型，针对已知句中关系的命名实体识别模型，还有将前两者整合起来的推理验证模型。其主要源自transformers库中的已定义好的预训练模型。lit_models 模块其中的代码主要继承自pytorch_lightning.Trainer。其可以自动构建单卡，多卡，GPU,TPU等不同硬件下的模型训练。我们在其中定义了training_steps和validation_step即可自动构建训练逻辑进行训练。由于其硬件不敏感，所以我们可以使用多种不同环境下调用OpenUE训练模块。data 模块data中存放了针对不同数据集进行不同操作的代码。使用了transformers库中的tokenizer先对数据进行分词处理再根据不同需要将数据变成我们需要的features。快速开始安装Anaconda 环境conda create -n openue python=3.8
conda activate openue
pip install -r requirements.txt
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia # 视自己Nvidia驱动环境选择对应的cudatoolkit版本
python setup.py installpip安装pipinstallopenuepip本地开发pythonsetup.pydevelop使用方式数据格式为json文件，具体例子如下。{"text":"查尔斯·阿兰基斯（Charles Aránguiz），1989年4月17日出生于智利圣地亚哥，智利职业足球运动员，司职中场，效力于德国足球甲级联赛勒沃库森足球俱乐部","spo_list":[{"predicate":"出生地","object_type":"地点","subject_type":"人物","object":"圣地亚哥","subject":"查尔斯·阿兰基斯"},{"predicate":"出生日期","object_type":"Date","subject_type":"人物","object":"1989年4月17日","subject":"查尔斯·阿兰基斯"}]}训练模型将数据存放在./dataset/目录下之后进行训练。如目录为空，运行以下脚本，将自动下载数据集和预训练模型并开始训练，过程中请保持网络畅通以免模型和数据下载失败。# 训练NER命名实体识别模块./scripts/run_ner.sh# 训练SEQ句中关系分类模块./scripts/run_seq.sh下面使用一个小demo简要展示训练过程，其中仅训练一个batch来加速展示。验证模型由于我们使用pipeline模型，所以无法联合训练，需要分别训练后进行统一验证。 在运行了两个训练脚本后，在output路径下会得到两个模型权重output/ner/${dataset}以及output/seq/${dataset}根据不同数据集放在对应的目录中。将模型权重目录分别作为ner_model_name_or_path和seq_model_name_or_path输入到run_infer.yaml或者是run_infer.sh运行脚本中，即可进行验证。Notebook快速开始ske数据集训练notebook使用中文数据集作为例子具体介绍了如何使用openue中的lit_models,models和data。方便用户构建自己的训练逻辑。使用colab云端环境，无需配置环境。支持自动调参（wandb）# 在代码中将logger 部分替换成wandb logger即可支持wandblogger=pl.loggers.WandbLogger(project="openue")快速部署模型下载torchserve-dockerdocker下载创建模型对应的handler类我们已经在deploy文件夹下放置了对应的部署类handler_seq.py和handler_ner.py。# 使用torch-model-archiver 将模型文件进行打包，其中# extra-files需要加入以下文件# config.json, setup_config.json 针对模型和推理的配置config。# vocab.txt : 分词器tokenizer所使用的字典# model.py : 模型具体代码torch-model-archiver--model-nameBERTForNER_en\--version1.0--serialized-file./ner_en/pytorch_model.bin\--handler./deploy/handler.py\--extra-files"./ner_en/config.json,./ner_en/setup_config.json,./ner_en/vocab.txt,./deploy/model.py"-f# 将打包好的.mar文件加入到model-store文件夹下，并使用curl命令将打包的文件部署到docker中。sudocp./BERTForSEQ_en.mar/home/model-server/model-store/
curl-v-XPOST"http://localhost:3001/models?initial_workers=1&synchronous=false&url=BERTForSEQ_en.mar&batch_size=1&max_batch_delay=200"项目成员浙江大学：张宁豫、谢辛、毕祯、王泽元、陈想、余海阳、邓淑敏、叶宏彬、田玺、郑国轴、陈华钧达摩院：陈漠沙、谭传奇、黄非引用如果您使用或扩展我们的工作，请引用以下文章：@inproceedings{zhang-2020-opennue,
    title = "{O}pe{UE}: An Open Toolkit of Universal Extraction from Text",
    author = "Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen",
    year = "2020",
}
Package: open-usp-tukubai
Universal Shell Programming Laboratoryinfo@usp-lab.comWed Aug  1 14:10:11 JST 2012Open usp Tukubaiとは====================Open usp Tukubaiは短期間低コストで企業システムを構築するエンタープライズ向けコマンド群「usp Tukubai」のオープンソースソフトウェア版。ユニケージ開発手法の普及促進を狙い、usp Tukubaiから特に利用頻度の高いものを選定して提供されている。Open usp Tukubaiは基幹業務システム、情報分析システム、データバッチ処理、高速検索システム、勘定系システムなどにおけるシェルおよびシェルスクリプトの用途を広げるためのUNIX環境用コマンド群であり、The MIT Licenseのもとでオープンソースソフトウェアとして公開されている。テキストデータの集計や結合 (リレーション)、その他業務システムで利用するコマンドが用意されている。Open usp Tukubaiはターミナルから操作するインタラクティブなデータ処理作業から、シェルスクリプトによる業務システムの構築・業務用Webアプリケーションの構築まで、UNIXの基本機能を最大限に活用するプログラミング能力を提供する。Open usp Tukubaiのサポート(有償)、商用版Tukubaiに関するお問合せ先は下記へ。info@usp-lab.comダウンロード===========ダウンロードページhttps://uec.usp-lab.com/tukubai_downloadGitHubhttps://github.com/usp-engineers-community/Open-usp-TukubaiRead-Only git://github.com/usp-engineers-community/Open-usp-Tukubai.git利用方法========Open usp Tukubaiはスクリプト言語Pythonで開発されている。PythonのインストールされたUNIX系OSで利用できる。Open usp Tukubaiのインストールおよびセットアップに関してはINSTALLファイルを参照のこと。なお、業務システム向けに提供されている商用版のTukubaiはC言語で開発されている。商用版の提供や教育プログラムに興味がある場合にはinfo@usp-lab.comまでご連絡いただきたい。リファレンス============オンラインコマンドマニュアルhttps://uec.usp-lab.com/tukubai_man付属のマニュアルデフォルトインストール先は/usr/local/share/open-usp-tukubai/devel/pdf//usr/local/share/open-usp-tukubai/devel/html//usr/local/share/open-usp-tukubai/devel/man/文献====https://uec.usp-lab.com/UEC - usp engineers' community site- USP MAGAZINE- 技術評論社 Software Design- 日経LINUX3月号ライセンスについて==================Open up TukubaiはThe MIT Licenseに基づき、USP研究所が無償で配布している。The MIT Licenseの詳細についてはLICENSEファイルを参照のこと。※ユニケージはユニバーサル・シェル・プログラミング研究所の登録商標。
Package: openva-pipeline
OpenVA_PipelineAutomates the processing of verbal autopsy (VA) data from an ODK Aggregate
server, through the openVA Pipeline middleware, and ending with a DHIS2 server
(with theVA module). VA data are
analyzed using theopenVA
packageinR(seeThe openVA Toolkit articlefor
more details).The documentation for the openVA Pipeline can be found at:http://openva-pipeline.readthedocs.io/Getting StartedThe OpenVA Pipeline has been designed for (and tested on) Ubuntu LTS 20.04.
The steps to install and configure the pipeline can be found indocs/setup.md. To make the installation easier, there is also a bash shell
scriptubuntu_installation.shthat installs all necessary software (the
script requires thesudopassword).  The script also works for Ubuntu LTS 22.04.LicenseGNU General Public License v3.0Licenses for Dependencies (ODK Briefcase and SmartVA-Analyze)The openva-pipeline package makes use of two external programs:ODK-Briefcase-v1.18.0.jarandsmartva.ODK Briefcaseis a tool in the Open Data Kit (ODK) Software Suite, and is open-source software available
under the Apache 2.0 license (seeODK_Briefcase_LICENSE.smartvais an open-source command line interface application for linux (see theSmartVA-Analyze MIT License)
that is available for download from theihmeuw/SmartVA-Analyze repository.These applications are NOT included under the same license as the OpenVA Pipeline (GPL-3).
Package: open-variant
OpenVariant is a comprehensive Python package that provides different functionalities to read, parse and operate
different multiple input file formats (e. g.tsv,csv,vcf,maf,bed), being able to customize the output.Its aim is being able to manage a ton of data represented in multiple ways and be able to build an unified output with
a proper annotation file structure. This package was thought to work with any kind of data that can be represented
as a table.Documentation:https://openvariant.readthedocs.ioFeaturesOpenVariant offers a toolkit to transform and operate the parsed input data. We will be able to apply different
functionalities on our parsed result some of them are the following ones:Find filesRead and saveCatGroup byCountCommand-line interface (CLI)PluginsCheckUser guidein OpenVariant's documentation to find all the information about how it works
and how can be applied the different functionalities.InstallationIt requires Python 3 or higher and can be installed asPyPI packagewith:pipinstallopen-variantFor more details check ourInstallationsection.ExamplesWe offer a bunch ofExamplesto we be able to understand how OpenVariant can be applied. Also, checkExamplessection in OpenVariant's documentation.ContributingFeel free to contribute as much as you want to the code.SeeCONTRIBUTINGfor guidelines on contributing and respect your behaviour specified
atCODE OF CONDUCT.LicenseThe software is licensed underBSD-3-Clause.
Package: openvario-shell
Main Menu Shell for OpenvarioThis is a replacement for stock main menu shipped with officialOpenvarioimages. It is implemented in Python and
offers more extensibility and richer user interface than stock shell
script-based one.The goal of this project is to provide a user interface for managing Openvario
device that is:User friendly-- responsive, can be operated with remote stick or
minimal input controls.Feature rich-- allow to manage all aspects of device, including
connected devices, files, etc.Extensible-- allow to integrate third-party applications.Simple to develop and test-- written in Python, can be tested without
Openvario device at hand.FeaturesRun XCSoar, optionally automatically after configurable timeoutPick preferred XCSoar languageChose screen orientation, screen brightness, font size and moreCalibrate touchscreen and sensorsDownload flight logs to USB flash drive with responsive UICopy files to and from USB flash drive, backup and restore configurationSynchronize system time with GPS time (when GPS receiver is connected)Integrate with third party apps (likeCompetition Manager)InstallationThe easiest way to install is usingopkgpackage manager on
internet-connected Openvario device.wgethttps://raw.githubusercontent.com/kedder/openvario-shell/master/scripts/openvario-shell-install.sh-O-|shAfter executing this command,ovshellcommand should be available. It will
start automatically next time Openvario boots up.In case you didn't like it and would like to return to stock Openvario menu,
run this command:wgethttps://raw.githubusercontent.com/kedder/openvario-shell/master/scripts/openvario-shell-uninstall.sh-O-|shDevelopmentIt is not required to own or have access to Openvario device in order to
developovshell. The only requirements are Python 3.7 or higher and terminal
emulator, readily available on MacOS or Linux operating systems. There are lots
of free options for Windows as well.Setting up the development environmentovshellusespipenvfor managing dependencies and dev environment. If you
don't have it yet, install with:pipinstallpipenv# or pip3 if you don't have "pip"After checking out the sources,cdtoopenvario-shelldirectory and run:pipenvshell
pipenvsyncAfter that, your development environment is ready, you should be able to run
the app:ovshellIt is possible to adjust few options by providing them in.envfile, located
in project directory. You can copy the sample filesample.envto.envand
adjust values there.Development toolsovshelluses various tools to check the code quality. They are generally
available throughmakeprogram. Most useful are these:make test- runs the test suitemake coverage- runs the test suite and display test coverage statisticsmake mypy- checks the sources with static type checkermake black- reformats the source code to match the code style
Package: openvas_to_report
OpenVAS XML report to human-friendly converterCode:https://github.com/cr0hn/openvas2reportIssues:https://github.com/cr0hn/openvas2report/issuesDocumentation HTML [EN]:http://openvas2report.readthedocs.orgPython version:Python 3What’s OpenVAS2Report?The idea is very simple:# Take an OpenVAS report, in it horrible XML format.
# Convert it into an beautiful Excel, ready to give to your boss.Why?I’m security auditor and I really hate to pass OpenVAS XML report into to and Excel document. This is a work for a monkey, not for a human! (Yes: security auditors are humans too. I know, I know. It’s incredible)So I started to develop this project and I thought share it for help other auditors that also hate make a monkey’s work.OpenVAS to in two wordsThis package are composed by 2 tools:openvas_to_document: This is the main program. You can use it to generate the Excel file.openvas_cutter: This is a facility for filter and crop some information from OpenVAS XML report.as a library: Also, you can use the tool as a library and import them it in your own code. It has BSD license, Feel free to use!A picture is worth a 1000 words From XML. Using openvas_to_document you can obtain this Excel file:FutureI’m have not enough time, but in a future, I’ll write the module to export the results in a Word.Bugs and errorsIf you find some bugs, please, open a ticket using github issues. And, If you send me a patch I’ll be very happy :)And if you want to help me… A beer may be a great idea :)
Package: openvax_project_template
Template for a new OpenVax Python projectREADMEEvery README file should contain the following badges:Travis build statusCoverageCurrent library version on PyPITravis configurationEvery travis build should, in addition to a project’s unit test, also
run theopenvax-integration-tests.Deploying to PyPIProjects should auto-deploy to PyPI from Travis. Each project has to
re-encrypt theopenvaxPyPI using thetravis encryptcommand.
The text of the README must be converted from Markdown to RST in thesetup.pyfile usingpypandoc, which also requires thatpandocis installed on Travis.VersionThe project’s version should be stored in the top-most__init__.pyfile in a filed named__version__.
Package: openvds
OpenVDS is a specification and an open source reference implementation of a
storage format for fast random access to multi-dimensional (up to 6D)
volumetric data stored in an object storage cloud service (e.g.
Amazon S3, Azure Blob storage or Google Cloud Storage). The specification is
based on, but not similar to, the existing Volume Data Store (VDS) file format.
The VDS format is a Bluware Inc. proprietary format, which has seen extensive
industrial deployments over the last two decades. The design of the VDS format
is contributed by Bluware Inc. to the Open Group Open Subsurface Data Universe
Forum (OSDU) (The Open Group, u.d.).OpenVDS has been designed to handle extremely large volumes, up to petabytes in
size, with variable sized compressed bricks. The OpenVDS format is very
flexible and can store any kind data representable as arrays with
key/value-pair metadata. In particular, data commonly used in seismic
processing can be stored along with all necessary metadata. This makes it
possible to go from legacy formats to OpenVDS and back, while retaining all
metadata.OpenVDS may be used to store E&P data types such as regularized single-Z
horizons/height-maps (2D), seismic lines (2D), pre-stack volumes (3D-5D),
post-stack volumes (3D), geobody volumes (3D-5D), and attribute volumes of any
dimensionality up to 6D.
The format has been designed primarily to support random access and on-demand
fetching of data, this enables applications that are responsive and interactive
as well as efficient I/O for high-performance computing or machine learning
workloads.
Package: openvi
OpenVI - Open toolkit for Computer VisionThis project aims to create a toolkit for computer vision, with image processing and computer vision training with no code. The main features are:Image processing graph: Everyone can create image processing graphs with OpenCV. Try the result in real time.Training machine learning models for object detection, image classification, and other computer vision tasks.Deploy machine learning models to devices.EnvironmentPython 3.9pipinstallCythonnumpy==1.23.0
pipinstall-e.Runpython-mopenvi.mainOpenVI - ScreencastBuild and PublishBuild and publish to PyPI:pythonsetup.pybuildtwine
bashbuild_and_publish.shReferencesWe used some code and media from the following sources:Image processing graph:Image-Processing-Node-Editor- License: Apache 2.0.Video ofRobert Arango LopezfromPixabay
Package: openviduconnect
OpenVidu ConnectThe project aims to connect to the OpenVidu Server.
It tries to wrap all the headers and APIs into attributes and methods of a class.
OpenVidu Connect provides support for Sync and Async API calls.(Enabled with context manager for both sync and async runs.)What do we have in store?Future annotations are used to get the maximum output for support in IDEsBuilt on top of httpx (Get httpx: pip install httpx)Code ExamplesSync Class (OpenViduClient) ExamplefromopenviduconnectimportOpenViduClientov=OpenViduClient("<HOST>","<SECRET>")response=ov.get_sessions()print(response)####### OR #######withOpenViduClient("<HOST>","<SECRET>")asov:response=ov.get_sessions()print(response)Async Class (AsyncOpenViduClient) ExamplefromopenviduconnectimportAsyncOpenViduClientaov=AsyncOpenViduClient("<HOST>","<SECRET>")"""Async Stuff"""response=awaitaov.get_sessions()print(response)####### OR #######asyncwithAsyncOpenViduClient("<HOST>","<SECRET>")asaov:response=awaitaov.get_sessions()print(response)We have all forms of specific errors respective to the documentation of the REST APIsfromopenviduconnect.exceptionsimportSessionBodyParameterError"""SessionBodyParameterError is just an example there are many more"""print(SessionBodyParameterError.status)# Provides HTTP Response Status CodeSupportRuns with Python 3.7 and beyondAt this point it only supports OpenVidu 2.17.0Will try to maintain versions of openviduconnect for different OepnVidu versionsKnown issues?DocumentationOthers.You tell me
Package: openvino2onnx
OPENVINO2ONNXA simple tool to convert your IR XML to ONNX file.Supported OpenVINO IR VersionIRv10: openvino>=2020,<2023IRv11: openvino>=2023UsageInstall from PyPIpipinstallopenvino2onnxConvert IR using CLIopenvino2onnxmodel.xml
Package: openvino-colab
This package provides Intel’s OpenVINO Toolkit installation and easy usage in the Google Colab Notebooks.The OpenVINO Toolkit allows edge AI application optimization and deployment on Google Colab using Colab Notebooks. It is also possible to choose to download all 5 or individual supported model dependencies directly in the notebook along with the option to start a Visual Studio Code instance and work interactively in a VS Code environment on the web using the Google Colab Container and provided resources to build and test Edge AI applications.This package also supports some basic configuration of vs code server (i.e. To choose different port, password or mounting the drive options.
Package: openvino-kaggle
This package provides Intel’s OpenVINO Toolkit installation and easy usage in the Kaggle Notebooks.The OpenVINO Toolkit allows edge AI application optimization and deployment on Kaggle using Kaggle Notebooks. It is also possible to choose to download all 5 or individual supported model dependencies directly in the notebook.Github Link:https://www.github.com/alihussainia/openvino-kaggleLinkedin Link:https://www.linkedin.com/in/alihussainia