Saravanan666/pypi-org huggingface dataset:
Package: fastreid
Gitter:fast-reid/communityWechat:FastReID is a research platform that implements state-of-the-art re-identification algorithms. It is a ground-up rewrite of the previous version,reid strong baseline.What's New[Sep 2021]DG-ReIDis updated, you can check thepaper.[June 2021]Contiguous parametersis supported, now it can
accelerate ~20%.[May 2021] Vision Transformer backbone supported, seeconfigs/Market1501/bagtricks_vit.yml.[Apr 2021] Partial FC supported inFastFace![Jan 2021] TRT network definition APIs inFastRThas been released!
Thanks forDarren's contribution.[Jan 2021] NAIC20(reid track)1-st solutionbased on fastreid has been released！[Jan 2021] FastReID V1.0 has been released！🎉
Support many tasks beyond reid, such image retrieval and face recognition. Seerelease notes.[Oct 2020] Added theHyper-Parameter Optimizationbased on fastreid. Seeprojects/FastTune.[Sep 2020] Added theperson attribute recognitionbased on fastreid. Seeprojects/FastAttr.[Sep 2020] Automatic Mixed Precision training is supported withapex. Setcfg.SOLVER.FP16_ENABLED=Trueto switch it on.[Aug 2020]Model Distillationis supported, thanks forguan'an wang's contribution.[Aug 2020] ONNX/TensorRT converter is supported.[Jul 2020] Distributed training with multiple GPUs, it trains much faster.Includes more features such as circle loss, abundant visualization methods and evaluation metrics, SoTA results on conventional, cross-domain, partial and vehicle re-id, testing on multi-datasets simultaneously, etc.Can be used as a library to supportdifferent projectson top of it. We'll open source more research projects in this way.Removeignite(a high-level library) dependency and powered byPyTorch.We write afastreid introandfastreid v1.0about this toolbox.ChangelogPlease refer tochangelog.mdfor details and release history.InstallationSeeINSTALL.md.Quick StartThe designed architecture follows this guidePyTorch-Project-Template, you can check each folder's purpose by yourself.SeeGETTING_STARTED.md.Learn more at outdocumentation. And seeprojects/for some projects that are build on top of fastreid.Model Zoo and BaselinesWe provide a large set of baseline results and trained models available for download in theFastreid Model Zoo.DeploymentWe provide some examples and scripts to convert fastreid model to Caffe, ONNX and TensorRT format inFastreid deploy.LicenseFastreid is released under theApache 2.0 license.Citing FastReIDIf you use FastReID in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.@article{he2020fastreid,title={FastReID: A Pytorch Toolbox for General Instance Re-identification},author={He, Lingxiao and Liao, Xingyu and Liu, Wu and Liu, Xinchen and Cheng, Peng and Mei, Tao},journal={arXiv preprint arXiv:2006.02631},year={2020}}
Package: fastrepl
⚡♾️ FastREPLFast Run-Eval-Polish Loop for LLM Applications.This project is still in the early development stage. Have questions?Let's chat!QuickstartLet's say we have this existing system:importopenaicontext="""The first step is to decide what to work on. The work you choose needs to have three qualities: it has to be something you have a natural aptitude for, that you have a deep interest in, and that offers scope to do great work.In practice you don't have to worry much about the third criterion. Ambitious people are if anything already too conservative about it. So all you need to do is find something you have an aptitude for and great interest in."""defrun_qa(question:str)->str:returnopenai.ChatCompletion.create(model="gpt-3.5-turbo",messages=[{"role":"system","content":f"Answer in less than 30 words. Use the following context if needed:{context}",},{"role":"user","content":question},],)["choices"][0]["message"]["content"]We already have a fixed context. Now, let's ask some questions.local_runneris used here to run it locally with threads and progress tracking. We will haveremote_runnerto run the same in the cloud.contexts=[[context]]*len(questions)# https://huggingface.co/datasets/repllabs/questions_how_to_do_great_workquestions=["how to do great work?.","How can curiosity be nurtured and utilized to drive great work?","How does the author suggest finding something to work on?","How did Van Dyck's painting differ from Daniel Mytens' version and what message did it convey?",]runner=fastrepl.local_runner(fn=run_qa)ds=runner.run(args_list=[(q,)forqinquestions],output_feature="answer")ds=ds.add_column("question",questions)ds=ds.add_column("contexts",contexts)# fastrepl.Dataset({#     features: ['answer', 'question', 'contexts'],#     num_rows: 4# })Now, let's use one of our evaluators to evaluate the dataset. Note that we are running it 5 times to ensure we obtain consistent results.evaluator=fastrepl.RAGEvaluator(node=fastrepl.RAGAS(metric="Faithfulness"))ds=fastrepl.local_runner(evaluator=evaluator,dataset=ds).run(num=5)# ds["result"]# [[0.25, 0.0, 0.25, 0.25, 0.5],#  [0.5, 0.5, 0.5, 0.75, 0.875],#  [0.66, 0.66, 0.66, 0.66, 0.66],#  [1.0, 1.0, 1.0, 1.0, 1.0]]Seems like we are getting quite good results. If we increase the number of samples a bit, we can obtain a reliable evaluation of the entire system.We will keep working on bringing better evaluations.Detailed documentation ishere.ContributingAny kind of contribution is welcome.Development: Please readCONTRIBUTING.mdandtests.Bug reports: UseGithub Issues.Feature request and questions: UseGithub Discussions.
Package: fastrequest
fastrequestA Python C extension module for fast HTTP requests with SSL/TLS supportInstallationTo build and install the extension, you just need to run theinstall.shscript.
The script then installs required packages, builds the extension usingsetuptoolsand then installs it usingpip.chmod+xinstall.sh
./install.shExample UsageJust importfastrequestmodule. It's usage is as easy as ABC!importfastrequest.httpres=fastrequest.http.get("https://www.google.com")res.save_to("index.html")Also, JSON (application/json) responses can be handled easily too.importjsonimportfastrequest.httpres=fastrequest.http.get("https://jsonplaceholder.typicode.com/comments?postId=1")withopen("data.json","w")asfp:json.dump(fp,res.json())Module ReferenceHTTPResponseHTTPResponse is the generic HTTP/HTTPS response object returned from the GET/POST functions.Pythonic definition of HTTPResponse is as follows,classHTTPResponse(object):def__init__(self,data,size,url=""):self.data=dataself.size=sizeself.url=urldeftext(self):returnself.data.decode()defjson(self):returnjson.loads(self.text())http.gethttp.get(url: str[, headers:dict]) -> HTTPResponseHTTP/GET request function usinglibcurlas backend.API ReferenceHTTPResponseHTTPResponse is the generic HTTP/HTTPS response object returned from the GET/POST functions.The data structure of HTTPResponse object is as follows,typedefstruct{PyObject_HEADPyObject*response_size;PyObject*response_data;PyObject*request_url;}HTTPResponseObject;Whereresponse_sizeis byte-size of the response body (PyLong)response_bodyis the response body (PyBytes)request_urlis the requested URI (PyUnicode)StringBufferStringBuffer is the generic 'string' object implementation to be used in Fast Request API functions.The data structure of StringBuffer object is as follows,typedefstruct{char*buf;int_fast64_tindex;int_fast64_tsize;}StringBuffer;FastRequestAPI_LibcurlHttpGetStringBuffer *FastRequestAPI_LibcurlHttpGet(char *url, PyObject *headers)Send HTTP/GET request to machine with theurlspecified with optional customheaders. This function useslibcurlas the backend.
Package: fast-resource
fast-resourcefast-resourceis a data transformation layer that sits between the database and the application's users, enabling quick data retrieval. It further enhances performance by caching data using Redis and Memcached.Why Use fast-resource?fast-resource is useful in situations where there are numerous data retrieval queries, multiple data sources requiring data aggregation, and shared data between entities that need to be cached. Additionally, the type of database used is not important.RequirementsredispymemcacheInstall>pipinstallfast-resourceUsageQuick Startfromfast_resourceimportResourceclassUserResource(Resource):classMeta:fields=('id','name',)defname(self,input_data)->str:returnf'{input_data["name"]}{input_data["family"]}'UserResource({'id':1,'name':'bagher','family':'rokni'}).to_dict()Custom outputUserResource({'id':1,'name':'bagher','family':'rokni'}).to_dict(('id',))Use cache decoderfromredis.clientimportRedisfromfast_resourceimportResource,cachefromfast_resource.cacheimportRedisCachedefmy_key_builder(input_data,field):returnf'user.{input_data["id"]}.{field}'classUserResource(Resource):classMeta:fields=('id','name',)@cache(key=my_key_builder)defname(self,input_data)->str:returnf'{input_data["name"]}{input_data["family"]}'Resource.cache_init(driver=RedisCache(Redis()))UserResource({'id':1,'name':'bagher','family':'rokni'}).to_dict()Custom cache expire_time@cache(key=my_key_builder,expire_time=60)defname(self,input_data)->str:returnf'{input_data["name"]}{input_data["family"]}'Cache deleteUserResource({'id':1,'name':'bagher','family':'rokni'}).cache_delete()Cache delete & rebuildUserResource({'id':1,'name':'bagher','family':'rokni'}).cache_delete(rebuild=True)Cache delete by keysUserResource.cache_delete_by_keys(['user.1.name'])Create collectionuser_collection=UserResource.collection([{'id':1,'name':'bagher','family':'rokni'},{'id':2,'name':'sepehr','family':'rokni'},{'id':3,'name':'sama','family':'rokni'},])user_collection.to_dict()
Package: fastrl2
Fastrl2This is a temporary location for fastrl version 2.{% include warning.html content='Even before fastrl==2.0.0, all Models should converge reasonably fast, however HRL modelsDADSandDIAYNwill need ' %}re-balancing and some extra features that the respective authors used.OverviewHere is changeFastai for computer vision and tabular learning has been amazing. One would wish that this would be the same for RL. The purpose of this repo is to have a framework that is as easy as possible to start, but also designed for testing new agents.Documentation is being served  athttps://josiahls.github.io/fastrl/from documentation directly generated vianbdevin this repo.Current Issues of InterestData Issuesdata and async_data are still buggy. We need to verify that the order that the data being returned is the best it can be for our models. We need to make sure that "dones" are returned and that there are new duplicate (unless intended)Better data debugging. Do environments skips steps correctly? Do n_steps work correct?Whats new?As we have learned how to support as many RL agents as possible, we found thatfastrl==1.*was vastly limited in the models that it can support.fastrl==2.*will leverage thenbdevlibrary for better documentation and more relevant testing. We also will be building on the work of theptan1library as a close reference for pytorch based reinforcement learning APIs.1"Shmuma/Ptan". Github, 2020,https://github.com/Shmuma/ptan. Accessed 13 June 2020.InstallPyPI (Not implemented yet)Placeholder here, there is no pypi package yet. It is recommended to do traditional forking.(For future, currently there is no pypi persion)pip install fastrl==2.0.0 --preCondaconda env create -f environment.yamlsource activate fastrl && pip install ptan --no-dependencies && python setup.py developDocker (highly recommend)For cpu executiondockerbuild-ffastrl.Dockerfile-tfastrl:latest.
dockerrun--rm-it-p8888:8888--user"$(id-u):$(id-g)"-v$(pwd):/home/fastrl/fastrlfastrl:latest/bin/bash#docker run --rm -it -p 8888:8888 -p 4000:4000 --user "$(id -u):$(id -g)" -v $(pwd):/opt/project/fastrl fastrl:latest /bin/bashInstall:Nvidia-Dockerdockerbuild-ffastrl_cuda.Dockerfile-tfastrl_cuda:latest.
dockerrun--rm-it-p8888:8888-p4000:4000--gpusall--user"$(id-u):$(id-g)"-v$(pwd):/opt/project/fastrlfastrl_cuda:latest/bin/bashContributingAfter you clone this repository, please runnbdev_install_git_hooksin your terminal. This sets up git hooks, which clean up the notebooks to remove the extraneous stuff stored in the notebooks (e.g. which cells you ran) which causes unnecessary merge conflicts.Before submitting a PR, check that the local library and notebooks match. The scriptnbdev_diff_nbscan let you know if there is a difference between the local library and the notebooks.If you made a change to the notebooks in one of the exported cells, you can export it to the library withnbdev_build_libormake fastai2.If you made a change to the library, you can export it back to the notebooks withnbdev_update_lib.
Package: fastrlapi
Reinforcement Learning API made simple - FAST_RLAPIfastrlapi is a high level reinforcement learning api focusing on ease of use and simplicity.
Written in Python and running on top of established reinforcement learning libraries liketf-Agents,tensorforceorkeras-rl.Examplesfrom fastrlapi.agents import PpoAgent
from fastrlapi.callbacks import plot

ppoAgent = PpoAgent('CartPole-v0')
ppoAgent.train([plot.State(), plot.Loss(), plot.Rewards()])More Detailedfrom fastrlapi.agents import PpoAgent
from fastrlapi.callbacks import plot

ppoAgent = PpoAgent( 'Orso-v1',fc_layers=(500,500,500))
ppoAgent.train([plot.State(), plot.Loss(), plot.Rewards(), plot.Actions(), 
                plot.StepRewards(), plot.Steps(), plot.ToMovie()], 
                learning_rate = 0.0001, num_iterations = 500, max_steps_per_episode=50 )Available Algorithms and Backendsalgorithmtf-Agentstensorforcekeras-rl (suspended)fastrlapi class nameCEMnot availablenot availableyesCemAgentDqnyesyesyesDqnAgentDouble Dqnopennot availableyesDoubleDqnAgentDueling Dqnnot availableyesyesDuelingDqnAgentPpoyesyesnot availablePpoAgentRandomyesyesnot availableRandomAgentREINFORCEyesyesnot availableReinforceAgentSACpreviewnot availablenot availableSacAgentInstallationInstall from pypi using pip:pipinstallfastrlapi
Package: fastroc
fastrocfastroc is a module that is able to approximate the ROC AUC score over an axis of two numpy arrays much faster than current
alternatives. It does this at the expense of portability as it uses C to calculate the ROC AUC.UsageTo calculate the ROC AUC score, 2 arrays are needed:y_true- Whether that event actually happenedy_score- The model's score of that event happening. The lower, the more likely.y_scoreshould be in the range 0
to 1.The function also takes in 3 optional arguments:axis(default-1) - Which axis to compute the score overintegral_precision(default50) - The number of samples to use for the integrationthread_count(default1) - The number of threads to use. A value of 1 keeps the program single threaded.The array returned contains the roc scores.BuildingFirst run:gitclonehttps://github.com/brightlego/fastroc.gitcdfastrocThen run:Linux:To build and install with you default Python3 installation, runbashbuild.shOtherTo build and install with another Python3 installation or on MacOS/Windows, run<python-installation>-mbuild
<python-installation>-mpipinstall"$(lsdist/*.whl|sort-V|tail-n1)"--force-reinstallNotes for Windows:This code is not tested on Windows and is unlikely to be able to use multithreading.
Package: fastrometry
To install fastrometry, use:pip install fastrometryNote that you must have a Python version of 3.10 or higher.To use fastrometry, simply call fastrometry at the command line:fastrometry filename -option1 -option2 ...For a fastrometry video demo, please watch this:https://youtu.be/mFXDM3aa6lUFor more info, please visit the wiki:https://github.com/user29A/fastrometry/wikiThis project was undertaken with the financial support of the Canadian Space Agency.
Package: fastrpc
===========================pytodos===========================Command line lightweight todos... image:: https://travis-ci.org/chuanwu/PyTodos.svg?branch=master.. image:: https://badge.fury.io/py/pytodos.svg:target: https://badge.fury.io/py/pytodosInstall::pip install pytodosUsage::add: 增加任务list: 显示未完成的任务以及一个礼拜以内处理完的任务listall：显示未完成的任务以及处理完的所有任务got x: x表示未完成任务的编号，将其标记为完成.. image:: https://ooo.0o0.ooo/2017/04/19/58f729d498fa7.pngAttention::文件保存在/tmp/data.txt，请注意不要误删了，否则您的数据会丢失。
Package: fastruct
fastructStructural analysis and design made fast and simpleCommand line application (CLI) to analyse and design simple structures.Written in python.Current capabilitiesRigid foundation analysis: soil stress and lifting percentaje.Installationpipinstallfastructor using virtualenvpython-mpipinstallfastructUsageAdding new 1x1x1 foundation$fastructfadd123fundacion.id=1Adding loads (p vx, vy, mx, my) to foundation with ID=1$fastructladd111111user_load.id=1$fastructladd12.50.1-0.5-0.10.25
user_load.id=2Computing results for foundation with ID=1$fastructfanalize1F001:Lx=1.00Ly=2.00Lz=3.00Depth=3.00
┏━━━━┳━━━━━━┳━━━━━━┳━━━━━┳━━━━━━┳━━━━━━┳━━━━━┳━━━━━━━━━━━━┳━━━━━━┓
┃#  ┃ NAME ┃ P    ┃ Vx  ┃ Vy   ┃ Mx   ┃ My  ┃ σ (ton/m²) ┃ %    ┃┡━━━━╇━━━━━━╇━━━━━━╇━━━━━╇━━━━━━╇━━━━━━╇━━━━━╇━━━━━━━━━━━━╇━━━━━━┩
│01││16.0│1.0│1.0│4.0│4.0│39.00│83%│
├────┼──────┼──────┼─────┼──────┼──────┼─────┼────────────┼──────┤
│02││17.5│0.1│-0.5│-1.6│0.6│18.76│100%│
└────┴──────┴──────┴─────┴──────┴──────┴─────┴────────────┴──────┘
Package: fastrun
###快速启动-android稳定性测试前置条件：需安装adb环境、需安装adb环境、需安装adb环境，重要事情说三遍简介：
结合字节跳动提供的开源工具Fastbot_android, 进行封装了基础版的稳定性测试，
只需要数据线连接电脑，并确定在cmd中输入adb devices，看到了手机设备号，即可运行命令。命令参数必选参数:
  startproject              创建工程目录
  
  run                       运行
  
    package 包名，必须输入，不然会抛出异常
    
    -m 10 是设置分钟，置默认为5分钟
    
    -t 500 是设置点击事件，默认设置为500
    
    -log 是捕获异常日志，布尔值，默认是true
    
    -module 是获取自定义文件中的模块名称基础教程新建一个工程目录testProject，在Terminal中输入命令创建脚手架：PS E:\testProject> py -m fastrun startproject demo          
2022-02-23 11:27:43.369 | INFO     | fastrun.scaffold:create_folder:29 - 创建文件夹：demo
2022-02-23 11:27:43.371 | INFO     | fastrun.scaffold:create_file:38 - 创建文件：demo\__init__.py
2022-02-23 11:27:43.372 | INFO     | fastrun.scaffold:create_folder:29 - 创建文件夹：demo\modules
2022-02-23 11:27:43.373 | INFO     | fastrun.scaffold:create_file:38 - 创建文件：demo\modules\fastrun.json
2022-02-23 11:27:43.374 | INFO     | fastrun.scaffold:create_file:38 - 创建文件：demo\debugfeng.py在Terminal中输入运行命令默认是运行 5 分钟点击 500 下PS E:\testProject> py -m fastrun run "包名" -module "login"(login是fastrun.json文件中的模块名)默认是运行 10 分钟点击 1000 下PS E:\testProject> py -m fastrun run "包名" -m 10 -t 100 -module "login"(login是fastrun.json文件中的模块名)默认是运行 5 分钟点击 500 下，并不要记录日志PS E:\testProject> py -m fastrun run "包名" -module login -l flase设置自定义文件fastrun.json文件json文件固定格式：{"login":[{"prob":1,"activity":"xz.act.app_v4.account.LoginActivityV4","times":1,"actions":[{"xpath":"//*[@resource-id='xz.act:id/tv_account']","action":"CLICK","throttle":2000}]},{"prob":1,"activity":"xz.act.app_v4.account.AccountLoginActivityV4","times":1,"actions":[{"xpath":"//*[@resource-id='xz.act:id/et_select']","action":"CLICK","throttle":2000},{"xpath":"//*[@resource-id='xz.act:id/et_search']","action":"CLICK","text":"*测试酒店","clearText":false,"throttle":2000},{"xpath":"//*[@resource-id='xz.act:id/group_name']","action":"CLICK","throttle":2000}]}]}|后续会在增加黑白名单操作，敬请期待。。。###严正声明作者只提供工具使用，当用户使用其他用途时，对用户或者他人造成任何形式的损失和伤害，该工具不承担任何责任。本协议的一切解释权与修改权归本工具所有。
Package: fast-s3
Download/upload images from/to s3 very quicklySetuppoetry installUsageDownload image filesfromPILimportImagefromfast_s3importFetcher,Statuslarge_list_of_image_paths=[...]fetcher=Fetcher(paths=large_list_of_image_paths,endpoint_url="https://s3.my-path-to-s3",aws_access_key_id="my-key-id",aws_secret_access_key="my-secret-key",region_name="my-region",bucket_name="my-bucket",ordered=True,# returns files in the same order as pathsbuffer_size=1024,n_workers=32,)forfileinfetcher:iffile.status!=Status.error:Image.open(file.buffer).save(file.path)fetcher.close()Upload filesfromfast_s3importUploaderlarge_list_of_files=[...]large_list_of_paths=[...]uploader=Uploader(endpoint_url="https://s3.my-path-to-s3",aws_access_key_id="my-key-id",aws_secret_access_key="my-secret-key",region_name="my-region",bucket_name="my-bucket",)uploader.queue_upload(source=large_list_of_files,destination=large_list_of_paths,)uploader.await_upload()uploader.close()
Package: fastscape
A fast, versatile and user-friendly landscape evolution model.Fastscape is a Python package that provides a lot a small model
components (i.e., processes) to use with thexarray-simlabmodeling
framework. Those components can readily be combined together in order
to create custom Landscape Evolution Models (LEMs).Routines from thefastscapeliblibrary are used for fast model
execution.DocumentationDocumentation is hosted on ReadTheDocs:https://fastscape.readthedocs.ioLicense3-clause (“Modified” or “New”) BSD license. See the LICENSE file for details.AcknowledgmentFastscape is developed at theEarth Surface Process Modellinggroup of
the GFZ Helmholtz Centre Potsdam.Citing fastscapeIf you use xarray-simlab in a scientific publication, we would
appreciate acitation.
Package: fastscapelib
A C++/Python library of efficient and reusable algorithms for landscape
evolution modeling. It aims at providing a (bare bones) toolkit for building
your own Landscape Evolution Models (LEMs) or integrating it with other existing
codes or models.DocumentationDocumentation is hosted on ReadTheDocs:http://fastscapelib.readthedocs.ioLicenseThis library is licensed under theGNU General Public License
v3.0. See LICENSE file.Fastscapelib uses some code from other projects. Their licenses are
reproduced in the “licenses” directory.AcknowledgmentThis project is supported by theEarth Surface Process Modellinggroup of the GFZ Helmholtz Centre Potsdam.Citing fastscapelibIf you use Fastscapelib in a scientific publication, we would
appreciate acitation.
Package: fastscape-litho
fastscape_lithoFeeding fastscape with a 3D bloc of lithology with differential erodibility, diffusion and other. The input requires one 3D matrix of integer labels (0,1,2,3,4,...) and a list of label correspondence for each rock type specific parameters. The current version of the package includes basic functions to run erosion through a 3D bloc from a flat surface (see features). Future versions will include more feature, it highly depends how fast projects related to this components arises. Feel free to contact me if you are interested to see a specific feature getting developed!Free software: BSD licenseQuick StartYou need to installfastscape, with all dependencies and all. Then, you can simply:pip install fastscape-lithoNote that you need to be in the fastscape environment.conda-forgepackage to come!Features3D input of parameters3D K with the basic stream power law (implementation fromBraun and Willett, 2013)3D Kd for rocks and soil for hillslope linear diffusionFeatures to come:Initialization with existing topography ->soonAllow different geometry for the 3D bloc (e.g. different dx and dy) ->soon3D rock density for flexure and isostasy ->Kind Of Here But Deserves More Thoughts3D infiltration compatible with 2D precipitation ->WIPlandscape parametrization (template of geological 3D blocs) ->futureBridge betweengempyandfastscape-litho->futureCreditsBoris Gailleton -boris.gailleton@gfz-potsdam.deThis package was created with Cookiecutter_ and theaudreyr/cookiecutter-pypackage_ project template... _Cookiecutter:https://github.com/audreyr/cookiecutter.. _audreyr/cookiecutter-pypackage:https://github.com/audreyr/cookiecutter-pypackage=======
History0.0.1 (2021-02-22)First release on PyPI.