Saravanan666/pypi-org huggingface dataset:
Package: lightLabel
lightLabel一个自己使用的标注系统（后端）0. 声明本项目目前只计划开放当前版本源码，以后的代码应该会闭源1. 简介该标注系统主要用于简单的标注任务，数据的标注信息会实时同步到数据库中，目前已经基本实现了文本分类任务（样本类别数量不大）。2. 功能特性系统各功能层次耦合度较低，将标注任务抽象成资源类。将数据库访问功能也封装了抽象类，并且提供了MongoDB的默认实现，理论上可以自由添加其他数据库的实现。采用flask作为web服务框架，较严格采用restful设计风格，系统会为每个标注任务都提供相关restAPI接口。3. 使用示例示例代码fromlightlabelimportEngine,TextClassificationtext_cls=TextClassification('ttt_demo','des_demo')text_cls.add_classes(['唐朝人物','虚拟人物','三国人物'])text_cls.update_from_csv(r'C:\Users\Alienware\Desktop\text_classification_demo.csv',headers=['word'])engine=Engine()engine.add_plan(text_cls)engine.run()运行结果* Running on http://localhost:5000/ (Press CTRL+C to quit)
{'ttt_demo'}
 * Serving Flask app "lightlabel.web.engine" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: off
127.0.0.1 - - [21/Feb/2020 18:24:52] "GET /ttt_demo/items HTTP/1.1" 200 -
127.0.0.1 - - [21/Feb/2020 18:25:31] "GET /ttt_demo/data HTTP/1.1" 200 -
127.0.0.1 - - [21/Feb/2020 18:30:57] "GET /project_lists HTTP/1.1" 200 -
127.0.0.1 - - [21/Feb/2020 18:30:58] "GET /favicon.ico HTTP/1.1" 200 -text_classification_demo.csv文件中内容李白
曹操
夏侯惇
张飞
周瑜
陆逊
司马懿数据库中该文本分类任务的数据内容（数据略有不符，因为我标注了几例）/* 1 */{"_id":ObjectId("5e4d411fc827b0709d554149"),"check_status":false,"labeled_data":"唐朝人物","labeled_status":true,"labeled_user":null,"raw_data":{"word":"李白"},"updated_time":null}/* 2 */{"_id":ObjectId("5e4d411fc827b0709d55414b"),"check_status":false,"labeled_data":"三国人物","labeled_status":true,"labeled_user":null,"raw_data":{"word":"曹操"},"updated_time":null}/* 3 */{"_id":ObjectId("5e4d411fc827b0709d55414d"),"check_status":false,"labeled_data":"三国人物","labeled_status":true,"labeled_user":null,"raw_data":{"word":"夏侯惇"},"updated_time":null}/* 4 */{"_id":ObjectId("5e4d411fc827b0709d55414f"),"check_status":false,"labeled_data":null,"labeled_status":false,"labeled_user":null,"raw_data":{"word":"张飞"},"updated_time":null}/* 5 */{"_id":ObjectId("5e4d411fc827b0709d554151"),"check_status":false,"labeled_data":null,"labeled_status":false,"labeled_user":null,"raw_data":{"word":"周瑜"},"updated_time":null}/* 6 */{"_id":ObjectId("5e4d411fc827b0709d554153"),"check_status":false,"labeled_data":null,"labeled_status":false,"labeled_user":null,"raw_data":{"word":"陆逊"},"updated_time":null}/* 7 */{"_id":ObjectId("5e4d411fc827b0709d554155"),"check_status":false,"labeled_data":null,"labeled_status":false,"labeled_user":null,"raw_data":{"word":"司马懿"},"updated_time":null}数据库中该文本分类任务对应的任务信息/* 1 */{"_id":ObjectId("5e4d411fc827b0709d554143"),"description":"des_demo","label_status":true,"task_type":"TextClassification","title":"ttt_demo","data":{"classes":["唐朝人物","虚拟人物","三国人物"]},"data_path":["C:\\Users\\Alienware\\Desktop\\text_classification_demo.csv"]}rest接口http://localhost:5000/project_lists：返回所有任务信息http://localhost:5000/ttt_demo/items：该任务所有标注条目http://localhost:5000/ttt_demo/data：该任务相关数据，如在该例中为所有标签类别组成的列表具体如下截图：4. 参考使用mongodb增删改查深层嵌套文档_数据库_qq_42427109的博客-CSDN博客pycharm: 恢复(reset) 误删文件_开发工具_JNing-CSDN博客快速入门 — Flask-RESTful 0.3.1 documentation[译] 用 Flask 和 Vue.js 开发一个单页面应用 - 掘金
Package: light-labyrinth
Light LabyrinthLight labyrinth machine learning model.How to install?pip install light_labyrinthHow to build dllFor develop purposes run build_devel.py scriptHow to name PRPR name should start with one of the following prefixes:[MAJOR] - for major changes (A+1.0.0)[MINOR] - for minor changes (A.B+1.0)[PATCH] - for patch changes (A.B.C+1)[NOBUMP] - for changes that should not bump versionHow to build with CMakeMake sure You have the CMake tool of version 3.10 or greater.Make sure You have some build system installed (for example MinGW Makefiles on Windows/on Linux You can use simple Unix Makefile)Create directory for build (in next step it will be refereed as "build_dir", if You want to build python package DO NOT name it "build"!)In build dir runcmake backboneCommand above can accept parameters like:-DCMAKE_C_COMPILER=<path-to-gcc> (or simplygccif gcc is in PATH)-G  (likeMinGW Makefiles)-DCMAKE_VERBOSE_MAKEFILE:BOOL=ON (to generate extensive logging in Makefile)-DCMAKE_BUILD_TYPE=Release|Debug (to generate makefiles for given build type)How to list symbols in dll:Run developer command prompt for vs 20XX and executedumpbin /exports light_labyrinth.dllDocumentationDocumentation available athttps://lightlabyrinth.orgSource codeSource code available athttps://bitbucket.org/Enkar234/lightlabyrinth
Package: lightly-insights
Lightly InsightsGet quick insights about your ML dataset.Lightly Insights visualises basic statistics of an image dataset. You provide a folder with images and object
detection labels, and it generates a static HTML webpage with metrics and plots.FeaturesSupports all object detection label formats that can be read withLabelformatpackage. That includes YOLO, COCO, KITTI, PascalVOC, Lightly and Labelbox.Shows the image, object and class countsAnalyzes how many images have no labels, and provides their filenames.Shows image samplesShows an analysis of image and object sizesShows an analysis per class with object sizes, counts per image, location heatmap and other.TypedMIT licensedPreviewSee alive example reportfor a small dataset.ScreenshotsInstallationpip install lightly-insightsUsageLightly Insights report is generated by a python script. This is necessary to support different label formats.The example below usesPascalVOC 2007dataset.
You can follow the example by downloading it (~450MB):wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
tar -xvf VOCtrainval_06-Nov-2007.tarTo run Lightly Insights, we need to provide:Image folder. In our case that is./VOCdevkit/VOC2007/JPEGImages.Object detection labels. They are ingested asLabelformatObjectDetectionInput class. For PascalVOC the constructor needs the folder with annotations./VOCdevkit/VOC2007/Annotationsand the list of classes. For other format classes, please
refer toLabelformat formats.frompathlibimportPathfromlabelformat.formatsimportPascalVOCObjectDetectionInputfromlightly_insightsimportanalyze,present# Analyze an image folder.image_analysis=analyze.analyze_images(image_folder=Path("./VOCdevkit/VOC2007/JPEGImages"))# Analyze object detections.label_input=PascalVOCObjectDetectionInput(input_folder=Path("./VOCdevkit/VOC2007/Annotations"),category_names=("person,bird,cat,cow,dog,horse,sheep,aeroplane,bicycle,boat,bus,car,"+"motorbike,train,bottle,chair,diningtable,pottedplant,sofa,tvmonitor"))od_analysis=analyze.analyze_object_detections(label_input=label_input)# Create HTML report.present.create_html_report(output_folder=Path("./html_report"),image_analysis=image_analysis,od_analysis=od_analysis,)To view the report, open./html_report/index.html.DevelopmentThe library targets python 3.7 and higher. We use poetry to manage the development environment.Here is an example development workflow:# Create a virtual environment with development dependenciespoetryenvusepython3.7
poetryinstall# Make changes...# Autoformat the codepoetryrunmakeformat# Run testspoetryrunmakeall-checksMaintained ByLightlyis a spin-off from ETH Zurich that helps companies
build efficient active learning pipelines to select the most relevant data for their models.You can find out more about the company and it's services by following the links below:HomepageWeb-AppLightly Solution Documentation (Lightly Worker & API)
Package: lightmail
功能特性对 Python 内置模块 email 和 smtplib 的简单封装安装目前 lightmail 支持的 Python 环境有 2.7, 3.4, 3.5, 3.6, 3.7为了简化安装过程，推荐使用 pip 进行安装pipinstalllightmail升级 lightmail 到新版本:pip install -U lightmail如果需要安装 GitHub 上最新代码:pip install https://github.com/ni-ning/lightmail/archive/master.zip使用首先需要配置文件 lightmail.cfg, 文件模板可参考 sample.cfg, 配置文件查找路径为工程当前目录运行环境家目录配置目录 etc使用案例:from lightmail import send_email

params = {
    "to": "linda@gmail.com",
    "title": "TESTING",
    "content": "Hello Python",
    "attach": ["@/tmp/test.txt"]
}
send_email(**params)
Package: light-manager-air
Light Manager Air SDK (Python)This is an SDK for communication with the light manager air from the vendor jb media (https://www.jbmedia.eu/light-manager/).The loading of zones, actuators and scenes is supported as well as the execution of the respective commands.It is also possible to listen for radio bus signals.For getting the connection to the devices service discovery is also available.Installationpip install light-manager-airExamplesConnect to Light Manager Air by urllight_manager = LMAir("http://lmair")Or connect to Light Manager Air by service discovery (recommended)light_managers = LMAir.discover()
assert len(light_managers) > 0
light_manager = light_managers[0]Call a specific commandzones, scenes = light_manager.load_fixtures()
zones[0].actuators[0].commands[0].call()Turn a light on when a radio bus signal is receiveddef callback(data):
    if data == "12282E9A":
        zones[0].actuators[0].commands[0].call()
        light_manager.stop_radio_bus_listening()

light_manager.start_radio_bus_listening(callback)
Package: lightmd
markdownA learn-motivated markdown parser.Supports all standard md elements.Sample use case:Install:pip install lightmdParse md.importlightmdwithopen("your.md","r")asmd_file:## Parse contentparsed_content=lightmd.parse_md_to_ast(md_file.read())## Render to HTMLrendered_html=lightmd.render_html(parsed_content)## Render to HTML with custom css stylerendered_html=lightmd.render_html(parsed_content,"/path/to/css_style.css")Dev onboardingInstall Python 3.7For Mac OS:brew install python@3.7For Windows User:choco install python --version=3.7Create Virtual Enviromentrun following command to install pipenvpip install pipenvRestart your command-line console, run following command:cd [project dir]
pipenv shellInstall all dependenciespipenv install
Package: lightmgb
Real Python Feed ReaderThe Real Python Feed Reader is a basicweb feedreader that can download the latest Real Python tutorials from theReal Python feed.For more information see the tutorialHow to Publish an Open-Source Python Package to PyPIon Real Python.InstallationYou can install the Real Python Feed Reader fromPyPI:python -m pip install realpython-readerThe reader is supported on Python 3.7 and above. Older versions of Python, including Python 2.7, are supported by version 1.0.0 of the reader.How to useThe Real Python Feed Reader is a command line application, namedrealpython. To see a list of thelatest Real Python tutorials, call the program without any arguments:$ realpython
The latest tutorials from Real Python (https://realpython.com/)
 0 How to Publish an Open-Source Python Package to PyPI
 1 Python "while" Loops (Indefinite Iteration)
 2 Writing Comments in Python (Guide)
 3 Setting Up Python for Machine Learning on Windows
 4 Python Community Interview With Michael Kennedy
 5 Practical Text Classification With Python and Keras
 6 Getting Started With Testing in Python
 7 Python, Boto3, and AWS S3: Demystified
 8 Python's range() Function (Guide)
 9 Python Community Interview With Mike Grouchy
10 How to Round Numbers in Python
11 Building and Documenting Python REST APIs With Flask and Connexion â€“ Part 2
12 Splitting, Concatenating, and Joining Strings in Python
13 Image Segmentation Using Color Spaces in OpenCV + Python
14 Python Community Interview With Mahdi Yusuf
15 Absolute vs Relative Imports in Python
16 Top 10 Must-Watch PyCon Talks
17 Logging in Python
18 The Best Python Books
19 Conditional Statements in PythonTo read one particular tutorial, call the program with the numerical ID of the tutorial as a parameter:$ realpython 0
# How to Publish an Open-Source Python Package to PyPI

Python is famous for coming with batteries included. Sophisticated
capabilities are available in the standard library. You can find modules for
working with sockets, parsing CSV, JSON, and XML files, and working with
files and file paths.

However great the packages included with Python are, there are many
fantastic projects available outside the standard library. These are most
often hosted at the Python Packaging Index (PyPI), historically known as the
Cheese Shop. At PyPI, you can find everything from Hello World to advanced
deep learning libraries.

[... The full text of the article ...]You can also call the Real Python Feed Reader in your own Python code, by importing from thereaderpackage:>>> from reader import feed
>>> feed.get_titles()
['How to Publish an Open-Source Python Package to PyPI', ...]
Package: lightmlrestapi
lightmlrestapiIt implements a light machine learningREST APIbased onfalcon.
If I were to start again this project, I would
probably chooseFastAPI.
You can test a dummywsgiserver by running:start_mlrestapi --name=dummyAnd then query it with:import requests
import ujson
features = ujson.dumps({'X': [0.1, 0.2]})
r = requests.post('http://127.0.0.1:8081', data=features)
print(r)
print(r.json())It should return:{'Y': [[0.4994216179, 0.4514893599, 0.0490890222]]}The module was first tried with success in a hackathon in 2018.
Participants could upload their model and retrieve their predictions
through a REST API to check it was producing the same one as they had.
A simple way to put a model into production.GitHub/lightmlrestapidocumentationBlogHistorycurrent - 2021-01-01 - 0.00Mb23: Issue with ujson>=3.0, reject_bytes must be set to False (2020-06-06)21: Fix issue with non-serializable numpy array (ujson does not do it anymore) (2020-03-29)0.2.151 - 2019-05-17 - 0.12Mb20: fix issue with falcon 2.0 (2019-05-08)17: reload module mapped to subfolder (2019-01-11)16: add full example to start a rest api (2018-12-01)9: add version number to REST API (2018-11-13)6: allow a zip file which contains data and code (2018-11-13)4: add authentification to the rest api (2018-11-13)8: allow clear logs (2018-11-02)5: add a load function (2018-11-02)3: add ip address in the logging (2018-04-15)2: add encrypted logging (2018-04-15)1: fix gallery of examples (style) (2018-01-05)
Package: lightnet
Building blocks to recreate Darknet networks in PytorchWhy another frameworkpytorch-yolo2is working perfectly fine,
but does not easily allow a user to modify an existing network.This is why I decided to create a library,
that gives the user all the necessary building blocks, to recreate any darknet network.
This library has everything you need to control your network,
weight loading & saving, datasets, dataloaders and data augmentation.Where it started as library to recreate the darknet networks in PyTorch,
it has since grown into a more general purpose single-shot object detection library.InstallingFirst installPyTorch and Torchvision.Then clone this repository and run one of the following commands:# If you just want to use Lightnetpipinstallbrambox# Optional (needed for training)pipinstall.# If you want to develop Lightnetpipinstall-rdevelop.txtThis project is python 3.6 and higher so on some systems you might want to use 'pip3.6' instead of 'pip'How to useClick Herefor the API documentation and guides on how to use this library.Theexamplesfolder contains code snippets to train and test networks with lightnet. For examples on how to implement your own networks, you can take a look at the files inlightnet/models.If you are using a different version than the latest,
you can generate the documentation yourself by runningmake clean htmlin thedocsfolder.
This does require some dependencies, like Sphinx.
The easiest way to install them is by using the-r develop.txtoption when installing lightnet.CiteIf you use Lightnet in your research, please cite it.@misc{lightnet18,
  author = {Tanguy Ophoff},
  title = {Lightnet: Building Blocks to Recreate Darknet Networks in Pytorch},
  howpublished = {\url{https://gitlab.com/EAVISE/lightnet}},
  year = {2018}
}Main ContributorsHere is a list of people that made noteworthy contributions and helped to get this project where it stands today!Tanguy OphoffJon CrallCedric Gullentops
Package: lightningaddon
Lightning AddOnsAdditional functionality forPytorch LightningPip!!Now you can directly do a pip installpip install lightningaddonUseful functionsPlease checkDOCUMENTATIONFor some useful functions checkuseFeatures (in progress)The documentation is in progress and of course, many more features are on the way.Generalcompose multiple functions on datatime any functionparallelly run any function on a listdownload from urlTorch relatedmodel vis using hiddenlayerclear gpu memory easilyflattenget a histogramfind modules with a conditioncheck if linear layerseed everythingfreeze/unfreeze parts of a modelcount paramemeters and display tablescount paramemeter state (frozen/unfrozen)easy pil<->tensoropen image -> resize -> convert to tensorModelsxresnet (all variants)RequirementsPyTorchLightning. (This will install pretty much everything else)einopsFAQWhy?
- Well, lightning is an awesome library but there are some things I wanted globally so I decided to make an "Add Ons" package.Why not contribute instead?
- I might once this package does significant extra stuffThank youA huge thank you to Jeremy Howard andfastai. Many of these tricks are inspired by the amazing work done there.
Package: lightning-bagua
Lightning ⚡ BaguaDeep Learning Training Acceleration withBaguaandLightning AIBaguais a deep learning training acceleration framework which supports multiple advanced distributed
training algorithms including:Gradient AllReducefor centralized synchronous communication, where gradients are averaged among all workers.Decentralized SGDfor decentralized synchronous communication, where each worker exchanges data with one or a few specific workers.ByteGradandQAdamfor low precision communication, where data is compressed into low precision  before communication.Asynchronous Model Averagefor asynchronous communication, where workers are not required to be  synchronized in the same iteration in a lock-step style.By default, Bagua usesGradient AllReducealgorithm, which is also the algorithm implemented in DDP, but Bagua can usually produce a higher training throughput due to its backend written in Rust.Installationpipinstall-Ulightning-baguaUsageSimply set the strategy argument in the Trainer:fromlightningimportTrainer# train on 4 GPUs (using Bagua mode)trainer=Trainer(strategy="bagua",accelerator="gpu",devices=4)SeeBagua Tutorialsfor more details on installation and advanced features.
Package: lightning-bolts
Deep Learning components for extending PyTorch LightningInstallation•Latest Docs•Stable Docs•About•Community•Website•LicenseGetting StartedPip / Condapipinstalllightning-boltsOther installationsInstall bleeding-edge (no guarantees)pipinstallhttps://github.com/Lightning-Universe/lightning-bolts/archive/refs/heads/master.zipTo install all optional dependenciespipinstalllightning-bolts["extra"]What is Bolts?Bolts package provides a variety of components to extend PyTorch Lightning, such as callbacks & datasets, for applied research and production.Example 1: Accelerate Lightning Training with the Torch ORT CallbackTorch ORT converts your model into an optimized ONNX graph, speeding up training & inference when using NVIDIA or AMD GPUs. See thedocumentationfor more details.frompytorch_lightningimportLightningModule,Trainerimporttorchvision.modelsasmodelsfrompl_bolts.callbacksimportORTCallbackclassVisionModel(LightningModule):def__init__(self):super().__init__()self.model=models.vgg19_bn(pretrained=True)...model=VisionModel()trainer=Trainer(gpus=1,callbacks=ORTCallback())trainer.fit(model)Example 2: Introduce Sparsity with the SparseMLCallback to Accelerate InferenceWe can introduce sparsity during fine-tuning withSparseML, which ultimately allows us to leverage theDeepSparseengine to see performance improvements at inference time.frompytorch_lightningimportLightningModule,Trainerimporttorchvision.modelsasmodelsfrompl_bolts.callbacksimportSparseMLCallbackclassVisionModel(LightningModule):def__init__(self):super().__init__()self.model=models.vgg19_bn(pretrained=True)...model=VisionModel()trainer=Trainer(gpus=1,callbacks=SparseMLCallback(recipe_path="recipe.yaml"))trainer.fit(model)Are specific research implementations supported?We'd like to encourage users to contribute general components that will help a broad range of problems; however, components that help specific domains will also be welcomed!For example, a callback to help train SSL models would be a great contribution; however, the next greatest SSL model from your latest paper would be a good contribution toLightning Flash.UseLightning Flashto train, predict and serve state-of-the-art models for applied research. We suggest looking at ourVISSLFlash integration for SSL-based tasks.Contribute!Bolts is supported by the PyTorch Lightning team and the PyTorch Lightning community!Join our Slack and/or read ourCONTRIBUTINGguidelines to get help becoming a contributor!LicensePlease observe the Apache 2.0 license that is listed in this repository.
In addition, the Lightning framework is Patent Pending.
Package: lightning-boost
Lightning-BoostExtension of thePyTorch Lightningframework to develop deep learning models inPyTorcheven faster.PyTorch Lightning already saves its versed users a lot of time, as large chunks of the PyTorch code being necessary to train deep neural networks are actually boilerplate.
However, with parts of the structure and code still being shared by most not too exotic projects, there is potential for further optimization.In essence, Lightning-Boost was born out of the need to not create a codebase from scratch for every deep learning project.
It provides three key features that help users to develop their models not only faster, but also in a more structured way:Command Line Interface and Configuration FilesPowered by theLightning CLI, Lightning-Boost unifies the configuration of deep learning models and their training process. This is accompanied by YAML-based configuration files that can be generated automatically and be used instead of mile-long parametrizations in run-scripts calls.
And the best: A single line of code in the run-script is sufficient, extensiveArgumentParserdefinitions are now a thing of the past.Standardized Project StructureLightning-Boost also unifies the structure of a deep learning project in a highly modularized fashion. It provides a clear logical separation between amodel, which takes a well-defined input and produces -- as a function -- an equally well-defined output, and asystem, which operates on one or more models, given data, and manages the whole training process,as already recommended by PyTorch Lightning. Moreover, Lightning-Boost suggests a directory structure that does not necessarily have to be used, but perfectly fits into this logical framework and helps to stay on top of things.Base Classes for Common FunctionalityAs both the management of the training process and datasets share some common functionality across projects, respectively, Lightning-Boost comes with two base classes for the corresponding concepts of a system and a datemodule. Analogously, further base classes are provided for models, datasets and many other components. When developing for a new project, users need to implement only a small number of methods that contain the exact functionality specific to their tasks.InstallationSimply install via pip:pip install lightning-boostGetting startedFor newcomers, it is recommended to work through thetutorials. Basic knowledge of PyTorch and PyTorch Lightning is assumed.
Package: lightning-colossalai
Lightning ⚡ Colossal-AIEfficient Large-Scale Distributed Training withColossal-AIandLightning AIInstallationpipinstall-Ulightning-colossalaiUsageSimply set the strategy argument in the Trainer:importlightningasLtrainer=L.Trainer(strategy="colossalai",precision="16-mixed",devices=...)For more fine-grained tuning of Colossal-AI's parameters, pass the strategy object to the Trainer:importlightningasLfromlightning_colossalaiimportColossalAIStrategystrategy=ColossalAIStrategy(...)trainer=L.Trainer(strategy=strategy,precision="16-mixed",devices=...)Find all configuration optionsin the docs!