Saravanan666/pypi-org huggingface dataset:
Package: federated_monsters
Federated Monsters is a game that seeks to follow the format of games like Pokemon, but to instead use a federated server format to store and trade creaturesFree software: GPLv3 licenseDocumentation:http://federated_monsters.readthedocs.org.FeaturesSupport for Oracle Berkeley DBStandardized protocol for easy extensibilityHistory0.4.0 (2014-07-15)Fix issue with database not working in testsNote: Do not run BerkeleyDB.close! It will make the database unreadable for some weird-ass reasonRan files throughisortFix loop infederated_monsters.box.Box.run()so that it exits cleanly onKeyboardInterruptAdd to theusagesectionAddfederated_monsters.box.Clientclass to make chat handling easierUsefederated_monsters.box.Clientthroughoutfederated_monsters.boxAdd code to implement chat, but not going to add it in until some implementation details are fixedAddfederated_monsters.cryptoso monsters can be encrypted before uploadPrevent server owners form jacking all the monstersAdd tests forfederated_monsters.cryptoFixsetup.pyso requirements aren’t just for Python 3Change return type offederated_monsters.crypto.gen_key()tocollections.namedtupleAdd support for encryption tofederated_monsters.box.BoxAdd tests forfederated_monsters.cryptoand encrypted uploads0.3.1 (2014-07-08)Update.travis.ymlto finally get it to buildHad to remove builds for all Python versions except 2.7 and 3.2thx ubuntuMove requirements fromrequirements.txttosetup.pyUpdateREADME.rstto fix badgesChange doc settings so building on Read The Docs works0.3.0 (2014-07-06)Fix Python 2 compatibility issuesBegin work on communication protocolUpdate tests for new protocolAdd support for storage in Oracle Berkeley DBAdd/uploadmonstercommandCreate simple clientFix weird import errors with testsRequired adding try-except clause to all importsAdd generic database classMove opening of database file to separate function, and put it inBox.run()Allows for a quick switcheroo of database types for testingInitial protocol documentationAdd/downloadmonstercommandMakeBoxstrip whitespace from sent textAdd hash authenticationAdd support for user response to serverUpdate HISTORY to use code instead of italic formatting0.2.0 (2014-07-04)Add tests forboxandmonsterCreateboxmodule to contain server codeAdd Sphinx-compatible docstrings to all codeCreate framework forboxto parse commandsAddexport_monstermethod toMonsterto make export easier0.1.0 (2014-07-02)Initial release
Package: federation
The aim offederationis to provide and abstract multiple social web protocols like
ActivityPub and Diaspora in one package, over an easy to use and understand Python API.
This way applications can be built to (almost) transparently support many protocols
without the app builder having to know everything about those protocols.StatusCurrently three protocols are being focused on.Diaspora is considered to be stable with most of the protocol implemented.ActivityPub support should be considered as beta - inbound payload are
handled by a jsonld processor (calamus)Matrix support cannot be considered usable as of yet.The code base is well tested and in use in several projects. Backward incompatible changes
will be clearly documented in changelog entries.Additional informationInstallation and requirementsSeeinstallation documentation.Usage and API documentationSeeusage documentation.Support and helpSeedevelopment and support documentation.LicenseBSD 3-clause license.AuthorJason Robinson /jasonrobinson.me/@jaywink:federator.dev/GitLab/GitHub
Package: fedex
Author:Greg Taylor, Radek WojcikMaintainer:Python FedEx DevelopersLicense:BSDStatus:StableWhat is it?A light wrapper around FedEx’s Webservice Soap API. We don’t do much of any
validation, but we’ll help you sort through the pile of SOAP objects FedEx
uses.InstallationThe easiest way is via pip or easy_install:pip install fedexQuick StartClone this repository.Edit theexample_config.pyfile in Seeexamples/with your fedex credentials and run any of the provided examples.DocumentationRefer to thedocumentationfor more details on the project. Latest doc builds
are found indocsand doc build scripts indoc_source. Sphinx documentation is indoc_src.There are also a lot of useful examples under the examples directory within
this directory.SupportIssues & Questions:https://github.com/gtaylor/python-fedex/issuesMost problems are going to require investigation or a submitted
pull request by someone from the Python FedEx Developers organization.
To contribute a new feature or service, feel free to create a pull request.
We are always looking for new contributors to help maintain the project.Fedex Support and DocumentationFedex Support Email:websupport@fedex.comDeveloper Portal:http://www.fedex.com/us/developer/Updates To Services:https://www.fedex.com/us/developer/web-services/process.html(FedEx Web Services Announcements)Related ProjectsFedEx Commercial Invoice Generation, seehttps://github.com/radzhome/fedex-commercial-invoiceTodosIncrease service specific request validationRemove deprecated services (package movement service)Pickup service unit testsLegalCopyright (C) 2008-2015 Greg TaylorCopyright (C) 2015-2016 Python FedEx DevelopersThis software is licensed under the BSD License.python-fedex is not authored by, endorsed by, or in any way affiliated with
FedEx.
Package: fedex-generator
Fedex GenaratorIntroductionFEDEx Genarator is a system that assists in the process of EDA (Exploratory Data Analysis) sessions. Based on FEDEx work (https://github.com/TAU-DB/FEDEx), it gives the user the option to generate NL explanations + Visualizations to their queries (Filter/GroupBy/Join) results.How it worksFEDEx generator is forked from on FEDEx system, and offer new process to get explanation:The user users query (filter/groupby/join), and pass to FEDEx the input dataframe, output dataframe and the query parameters.FEDEx calculates an Interestingness Measure (that works well with the specific operation, for example Exceptionality measure for Filter and Join operations) for every column in the output dataframe (the query result)FEDEx finds the most interesting columns and partition them to set of rows.Then it finds the set-of-rows that affects the Interesingness measure result the most (from [2]).Now FEDEx takes the top columns and set-of-rows and generates meaningful explanationsFor the full details, you can either view the code or read the FEDEx article which will be referenced here really soon:)ExampleIn FEDEx example they used the spotify dataset from Kaggle.
The first operation of our user wasSELECT * FROM Spotify WHERE popularity > 65;The raw output (Snip) -The generated explanation -The second operation of the user wasSELECT AVG(dancability), AVG(loudness) FROM [SELECT * FROM Spotify WHERE year >= 1990] GROUPBY year;The raw output (Snip) -The generated explanation -UsageNotice- This project was tested on python version 3.6-3.8.First, you have to install the requirements -py -3 -m pip install -r requirements.txtSecondly, you should install latex on your system (the explanations inside the graphs require that). Things will still work even without latex but the experince might be a bit inferior.This fork created to work on some adjusments for a API that will allow users to use pandas and generate explanations without effort and without using additional dedicated API.
Package: fedex-invoice
# Fedex Commercial Invoice #Fedex Commercial Invoice Generation using Python (using reportlab)### What is this repository for? ###* A way to generate a commercial fedex invoice* https://github.com/radzhome/fedex-commercial-invoice### How do I get set up? ###* download repo, install requirements.txt using pip* to install globally, use `[sudo] python setup.py install`### Usage ###Generate Commercial Invoice<pre><code># generate_invoice testfrom fedex_invoice.invoice import generate_commercial_invoice# Includes company info.export_data = {'company': 'Radtek Inc.', 'address': '123 Main St.','city': 'Toronto', 'state_code': 'ON', 'postal_code': 'L8J 1V4', 'country_code': 'CA','waybill_no': '9999 9999 9999', 'export_date': '12/20/2015', 'export_refs': '999432423, 14314321423','export_country': 'USA', 'manufacture_country': 'CHINA','destination_country': 'CANADA'}importer_data = {'first_name': 'Real First Name','last_name': 'Real Last Name','postal_code': 'M5A 3C6', 'country_code': 'CA', 'state_code': 'ON','city': 'North York', 'address': '123 Import St.'}exporter_data = {'first_name': 'Real First Name','last_name': 'Real Last Name','postal_code': 'M5A 3C6', 'country_code': 'CA', 'state_code': 'ON','city': 'North York', 'address': '123 Export St.'}cosignee_data = {'first_name': 'Real First Name','last_name': 'Real Last Name','postal_code': 'M5A 3C6', 'country_code': 'CA', 'state_code': 'ON','city': 'North York', 'address': '123 Cosignee St.'}product1 = {'marks_nos': '1234', 'no_packages': 1, 'package_type': 'BOX','description': 'a description of the goods in a full and up to date way', 'quantity': 1,'measure_unit': 'lbs', 'weight': 25, 'unit_value': 23.24, 'total_value': 23.24}product2 = {'marks_nos': '12', 'no_packages': 1, 'package_type': 'OWN_PACKAGING','description': 'a description of the goods, another product to describe', 'quantity': 2,'measure_unit': 'lbs', 'weight': 5, 'unit_value': 43.44, 'total_value': 43.44}flags = {'fob': True, 'caf': True, 'cif': False}products = [product1, product2, product1, product2, product1, product2]generate_commercial_invoice(export_data, exporter_data, cosignee_data, products, flags, importer_data)</code></pre>See Sample for output example.This repository can be used as a starting point for generating your own invoice. If you have a useful script, don'thesitate to add it to the project.
Package: fedexrates
Libreria para conseguir cotizaciones sobre envio de paquetes con la API de shipengine (FedEx)fromfedexrates.ratesimportRatequote_params={"address_from":{"zip":"64000","country":"MX"},"address_to":{"zip":"64000","country":"MX"},"parcel":{"length":25.0,"width":28.0,"height":46.0,"distance_unit":"cm","weight":6.5,"mass_unit":"kg"}}credentials="TEST_DR6z1zWA0vFKnL+Znjk3FpRlLBEKGpKDg7N/yF7AShY"rate=Rate(quote_params=quote_params,credentials=credentials)print(rate.get_rate())
Package: fedextrack
FedEx Tracking API Python ClientThis is a Python client for theFedEx Tracking API.It is not fully featured yet, but it is a good starting point. It requires you to have a FedEx developer account and an API key.InstallationpipinstallfedextrackUsagefromfedextrackimportFedExapi=FedEx("YOUR_API_KEY","YOUR_API_SECRET")# Realtime trackingtracking=api.tracking("YOUR_SHIPMENT_NUMBER")LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: fedflow
Fed FlowDescriptionauto-scheduler for parallel task.Installpip instal fedflow==0.2.0Usageimportosimporttorch.nnasnnimporttorch.optimasoptimimporttorch.nn.functionalasFfromtorchvision.datasetsimportmnistfromtorchvision.transformsimporttransformsfromfedflowimportTask,TaskGroup,FedFlowfromfedflow.configimportConfigfromfedflow.utils.trainer.supervised_trainerimportSupervisedTrainerConfig.set_property("debug",True)Config.set_property("scheduler.interval",2)datasets_path=os.path.join(os.path.abspath("."),"datasets")classNet(nn.Module):def__init__(self):super(Net,self).__init__()self.conv1=nn.Conv2d(1,20,5,1)self.conv2=nn.Conv2d(20,50,5,1)self.fc1=nn.Linear(4*4*50,500)self.fc2=nn.Linear(500,10)self.softmax=nn.Softmax(dim=1)defforward(self,x):x=F.relu(self.conv1(x))x=F.max_pool2d(x,2,2)x=F.relu(self.conv2(x))x=F.max_pool2d(x,2,2)x=x.view(-1,4*4*50)x=F.relu(self.fc1(x))x=self.fc2(x)returnxclassMnistTask(Task):def__init__(self,id,datasets_path):super(MnistTask,self).__init__(task_id=id,estimate_memory="2.5GB",estimate_cuda_memory="1200MB")self.datasets_path=datasets_pathdefload(self):self.mnist_dataset=mnist.MNIST(root=self.datasets_path,download=True,train=True,transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.13066062,),(0.30810776,))]))self.test_dataset=mnist.MNIST(root=self.datasets_path,download=True,train=False,transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.13066062,),(0.30810776,))]))self.mnist_model=Net()self.mnist_optim=optim.SGD(self.mnist_model.parameters(),lr=0.01)self.criterion=nn.CrossEntropyLoss()deftrain(self,device)->dict:self.mnist_model=self.mnist_model.to(self.device)trainer=SupervisedTrainer(self.mnist_model,self.mnist_optim,self.criterion,epoch=50,device=self.device,console_out="console.out")trainer.mount_dataset(self.mnist_dataset,self.test_dataset,batch_size=32)returntrainer.train()defprint_result(group:TaskGroup):print("%2s%9s%9s"%("ID","train acc"," val acc "))foriinrange(20):task=group.get_task(i)result=task.resultprint("%02d%6.2f%%%6.2f%%"%(i,result["train_acc"],result["val_acc"]))if__name__=="__main__":# Download mnist datasetsmnist.MNIST(root=datasets_path,download=True)group=TaskGroup("mnist")foriinrange(20):group.add_task(MnistTask(i,datasets_path))withFedFlow()asflow:flow.execute(group)print_result(group)Featuresadd subprocess trackeradd GPUs load balancingadd methods to kill specified subprocess/task
Package: fedgraph
Federated Graph LearningDocumentation|PaperFedGraph(Federated Graph)is a library built on top ofPyTorch Geometric (PyG),Ray, andPyTorchto easily train Graph Neural Networks
under federated or distributed settings.It supports various federated training methods of graph neural networks under simulated and real federated environments and supports communication between clients and the central server for model update and information aggregation.Main FocusFederated Node Classification with Cross-Client Edges: Our library supports communicating information stored in other clients without affecting the privacy of users.Federated Graph Classification: Our library supports federated graph classification with non-IID graphs.Cross Platform TrainingWe support federated training across Linux, macOS, and Windows operating systems.Library HighlightsWhether you are a federated learning researcher or a first-time user of federated learning toolkits, here are some reasons to try out FedGraph for federated learning on graph-structured data.Easy-to-use and unified API: All it takes is 10-20 lines of code to get started with training a federated GNN model. GNN models are PyTorch models provided by PyG and DGL. The federated training process is handled by Ray. We abstract away the complexity of federated graph training and provide a unified API for training and evaluating FedGraph models.Various FedGraph methods: Most of the state-of-the-art federated graph training methods have been implemented by library developers or authors of research papers and are ready to be applied.Great flexibility: Existing FedGraph models can easily be extended for conducting your research. Simply inherit the base class of trainers and implement your methods.Large-scale real-world FedGraph Training: We focus on the need for FedGraph applications in challenging real-world scenarios with privacy preservation, and support learning on large-scale graphs across multiple clients.InstallationpipinstallfedgraphCitePlease citeour paper(and the respective papers of the methods used) if you use this code in your own work:@article{yao2023fedgcn,
  title={FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks},
  author={Yao, Yuhang and Jin, Weizhao and Ravi, Srivatsan and Joe-Wong, Carlee},
  journal={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2023}
}Feel free toemail usif you wish your work to be listed in theexternal resources.
If you notice anything unexpected, please open anissueand let us know.
If you have any questions or are missing a specific feature, feel freeto discuss them with us.
We are motivated to constantly make FedGraph even better.
Package: fedhf
FedHFFedHFis a loosely coupled,Heterogeneous resource supported, andFlexible federated learning framework.Accelerate your researchFeaturesLosely coupledHeterogeneous resource supportedFlexible federated learning frameworkSupport for asynchronous aggregationSupport for multiple federated learning algorithmsAlgorithms SupportedSynchronous Aggregation[FedAvg]Communication-Efficient Learning of Deep Networks from Decentralized Data(AISTAT)[paper]Asynchronous Aggregation[FedAsync]Asynchronous Federated Optimization(OPT2020)[paper]Tiered Aggregation[TiFL]TiFL: A Tier-based Federated Learning System (HPDC 2020)[paper]Getting Startpipinstallfedhf# If you want to use wandb to view log, please login firstwandbloginYou can see theDocumentfor more details.ContributingFor more information, please see theContributingpage.CitationIn progressLicenceThis work is provided underApache License Version 2.0.AcknowledgementMany thanks toFedLabandFedMLfor their great work.
Package: fedinesia
*!!! BEWARE, THIS TOOL WILL DELETE SOME OF YOUR POSTS ON THE FEDIVERSE !!!*Fedinesia is a command line (CLI) tool to delete old statuses from Mastodon or Pleroma instances.
It respects rate limits imposed by servers.Install and run fromPyPiIt’s ease to install Fedinesia from Pypi using the following command:pip install fedinesiaOnce installed Fedinesia can be started by typingfedinesiainto the command line.Install and run fromSourceAlternatively you can run Fedinesia from source by cloning the repository using the following command line:git clone https://codeberg.org/MarvinsMastodonTools/fedinesia.gitFedinesia usesPDMfor dependency control, please install PDM before proceeding further.Before running, make sure you have all required python modules installed. With PDM this is as easy as:pdm sync --group :allRun Fedinesia with the commandpdm run fedinesiaConfiguration / First RunFedinesia will ask for all necessary parameters when run for the first time and store them in`config.jsonfile in the current directory.LicensingFedinesia is licensed under theGNU Affero General Public License v3.0Supporting FedinesiaThere are a number of ways you can support Fedinesia:Create an issue with problems or ideas you have with/for FedinesiaCreate a pull request if you are more of a hands on person.You canbuy me a coffee.You can send me small change in Monero to the address below:Monero donation address86ZnRsiFqiDaP2aE3MPHCEhFGTeipdQGJZ1FNnjCb7s9Gax6ZNgKTyUPmb21WmT1tk8FgM7cQSD5K7kRtSAt1y7G3Vp98nT
Package: feditools
feditoolsPython tools for the Fediverse.InstallationInstall the latest stable versionNo stable versions are yet available.Install the latest experimental version$pipinstall--prefeditoolsUsageNodeInfo$python-mfeditools.nodeinfomastodon.social{"version":"2.0","software":{"name":"mastodon","version":"4.0.2"},"protocols":["activitypub"],"services":{"outbound":[],"inbound":[]},"openRegistrations":false,"usage":{"users":{"total":879403,"activeMonth":245520,"activeHalfyear":422601},"localPosts":42641202},"metadata":{}}WebFinger$python-mfeditools.webfingersoundbyte.socialnigel{"subject":"acct:nigel@soundbyte.social","aliases":["https://soundbyte.social/@nigel","https://soundbyte.social/users/nigel"],"links":[{"rel":"http://webfinger.net/rel/profile-page","type":"text/html","href":"https://soundbyte.social/@nigel"},{"rel":"self","type":"application/activity+json","href":"https://soundbyte.social/users/nigel"},{"rel":"http://ostatus.org/schema/1.0/subscribe","template":"https://soundbyte.social/authorize_interaction?uri={uri}"}]}
Package: fediverse-pasture
Fediverse PastureThis python package contains tools to test Fediverse applications. This
package usesbovinefor a lot
of the Fediverse related logic. It should also be noted that the aim here
is to debug issues caused by federation, thus everything involves starting
a webserver and running requests against it.UsageFor usage information, see thedocumentation.DevelopmentInstall the necessary dependencies viapoetryinstall--withtest,dev--all-extrasTo lint and check code formatting runpoetryrunruffcheck.
poetryrunruffformat.To test the code runpoetryrunpytestReleasingBump version viapoetryversion$TAGgitcommit-a-m"new version"gitpushoriginmainCheck that the build was successfulgittag$TAGgitpushorigin$TAGFundingThis code was created as part ofFediverse Test Framework.A project funded through theNGI0 CoreFund,
a fund established byNLnetwith financial support from
the European Commission'sNext Generation Internetprogramme,
under the aegis of DG Communications Networks, Content and Technology
under grant agreement No 101092990.
Package: fediverse-pasture-inputs
fediverse-pasture-inputsVersioned inputs to generate interoperability data ...FundingThis code was created as part ofFediverse Test Framework.A project funded through theNGI0 CoreFund,
a fund established byNLnetwith financial support from
the European Commission'sNext Generation Internetprogramme,
under the aegis of DG Communications Networks, Content and Technology
under grant agreement No 101092990.
Package: fedjax
FedJAX: Federated learning simulation with JAXDocumentation|PaperNOTE: FedJAX is not an officially supported Google product. FedJAX is still in
the early stages and the API will likely continue to change.What is FedJAX?FedJAX is aJAX-based open source library forFederated Learningsimulations that emphasizes ease-of-use in research. With its simple primitives
for implementing federated learning algorithms, prepackaged datasets, models and
algorithms, and fast simulation speed, FedJAX aims to make developing and
evaluating federated algorithms faster and easier for researchers. FedJAX works
on accelerators (GPU and TPU) without much additional effort. Additional details
and benchmarks can be found in ourpaper.InstallationYou will need a moderately recent version of Python. Please checkthe PyPI pagefor the up to date version
requirement.First, install JAX. For a CPU-only version:pip install --upgrade pip
pip install --upgrade jax jaxlib  # CPU-only versionFor other devices (e.g. GPU), followthese instructions.Then, install FedJAX from PyPI:pip install fedjaxOr, to upgrade to the latest version of FedJAX:pip install --upgrade git+https://github.com/google/fedjax.gitGetting StartedBelow is a simple example to verify FedJAX is installed correctly.importfedjaximportjaximportjax.numpyasjnpimportnumpyasnp# {'client_id': client_dataset}.fd=fedjax.InMemoryFederatedData({'a':{'x':np.array([1.0,2.0,3.0]),'y':np.array([2.0,4.0,6.0]),},'b':{'x':np.array([4.0]),'y':np.array([12.0])}})# Initial model parameters.params=jnp.array(0.5)# Mean squared error.mse_loss=lambdaparams,batch:jnp.mean((jnp.dot(batch['x'],params)-batch['y'])**2)# Loss for clients 'a' and 'b'.print(f"client a loss ={mse_loss(params,fd.get_client('a').all_examples())}")print(f"client b loss ={mse_loss(params,fd.get_client('b').all_examples())}")The following tutorial notebooks provide an introduction to FedJAX:Federated datasetsWorking with models in FedJAXFederated learning algorithmsYou can also take a look at some of our working examples:Federated AveragingFull EMNIST exampleCiting FedJAXTo cite this repository:@article{fedjax2021,
  title={{F}ed{JAX}: Federated learning simulation with {JAX}},
  author={Jae Hun Ro and Ananda Theertha Suresh and Ke Wu},
  journal={arXiv preprint arXiv:2108.02117},
  year={2021}
}Useful pointershttps://jax.readthedocs.io/en/latest/index.htmlhttps://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.htmlhttps://jax.readthedocs.io/en/latest/notebooks/How_JAX_primitives_work.htmlhttps://dm-haiku.readthedocs.io/en/latest/