Saravanan666/pypi-org huggingface dataset:
Package: fedlearner
Fedlearner™Fedlearner is collaborative machine learning framework that enables joint modeling of data distributed between institutions.Trademark Usage PolicyFedlearner welcomes everyone to build on or modify Fedlearner open source software for your own project.
The license of the software doesn’t grant permission to use trademarks or product names in respect to the licensor.
However, you may use the trademark or product name if,You use a wordmark to refer to Fedlearner program, product or technology;You use a wordmark in text to indicate the compatibility of your project with Fedlearner project;You use a wordmark in text to indicate your project is built based on Fedlearner technology.If you would like to use the Fedlearner trademark,to combine a trademark with your own brand, trademark, product, project, service or domain name;for any other commercial use;as a verb or noun, rather than only as an adjective followed by the generic name/noun;in a modified, abbreviated or altered form, or in the plural or possessive form;
please feel free to contact us for an express permission.If you use a trademark in a way not set forth above, or for any illegal purpose with the program, the licensor reserves the right in its sole discretion to terminate or modify your permission to display or use a trademark and to take action against any use that does not conform to these terms and conditions, or violates applicable law.
Package: fedlearner-manylinux
Fedlearner™Fedlearner is collaborative machine learning framework that enables joint modeling of data distributed between institutions.Trademark Usage PolicyFedlearner welcomes everyone to build on or modify Fedlearner open source software for your own project.
The license of the software doesn’t grant permission to use trademarks or product names in respect to the licensor.
However, you may use the trademark or product name if,You use a wordmark to refer to Fedlearner program, product or technology;You use a wordmark in text to indicate the compatibility of your project with Fedlearner project;You use a wordmark in text to indicate your project is built based on Fedlearner technology.If you would like to use the Fedlearner trademark,to combine a trademark with your own brand, trademark, product, project, service or domain name;for any other commercial use;as a verb or noun, rather than only as an adjective followed by the generic name/noun;in a modified, abbreviated or altered form, or in the plural or possessive form;
please feel free to contact us for an express permission.If you use a trademark in a way not set forth above, or for any illegal purpose with the program, the licensor reserves the right in its sole discretion to terminate or modify your permission to display or use a trademark and to take action against any use that does not conform to these terms and conditions, or violates applicable law.
Package: fedlib
Installationgitclonehttps://github.com/lishenghui/bladescdbladespipinstall-v-e.# "-v" means verbose, or more output
# "-e" means installing a project in editable mode,
# thus any local modifications made to the code will take effect without reinstallation.cdblades/bladespythontrain.pyfile./tuned_examples/fedsgd_cnn_fashion_mnist.yamlBladesinternally callsray.tune; therefore, the experimental results are output to its default directory:~/ray_results.Experiment ResultsCluster DeploymentTo runbladeson a cluster, you only need to deployRay clusteraccording to theofficial guide.Built-in ImplementationsIn detail, the following strategies are currently implemented:Data Partitioners:Dirichlet PartitionerSharding PartitionerCitationPlease cite ourpaper(and the respective papers of the methods used) if you use this code in your own work:@article{li2023blades,
  title={Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning},
  author= {Li, Shenghui and Ju, Li and Zhang, Tianru and Ngai, Edith and Voigt, Thiemo},
  journal={arXiv preprint arXiv:2206.05359},
  year={2023}
}
Package: fedml
FedML - The community building and connecting AI anywhere at any scalehttps://doc.fedml.aiMissionFedML builds simple and versatile APIs for machine learning running anywhere at any scale.
In other words, FedML supports both federated learning for data silos and distributed training for acceleration with MLOps and Open Source support, covering industrial grade use cases and cutting-edge academia research.Distributed Training: Accelerate Model Training with Lightweight CheetahSimulator: (1) simulate FL using a single process (2) MPI-based FL Simulator (3) NCCL-based FL Simulator (fastest)Cross-silo Federated Learning for cross-organization/account training, including Python-based edge SDKCross-device Federated Learning for Smartphones and IoTs, including edge SDK for Android/iOS and embedded Linux.Model Serving: we focus on providing a better user experience for edge AI.MLOps: FedML's machine learning operation pipeline for AI running anywhere at any scale.Source Code StructureThe functionality of each package is as follows:core: The FedML low-level API package. This package implements distributed computing by communication backend like MPI, NCCL, MQTT, gRPC, PyTorch RPC, and also supports topology management.
Other low-level APIs related to security and privacy are also supported. All algorithms and Scenarios are built based on the "core" package.data: FedML will provide some default datasets for users to get started. Customization templates are also provided.model: FedML model zoo.device: FedML computing resource management.simulation: FedML parrot can support: (1) simulate FL using a single process (2) MPI-based FL Simulator (3) NCCL-based FL Simulator (fastest)cross-silo: Cross-silo Federated Learning for cross-organization/account trainingcross-device: Cross-device Federated Learning for Smartphones and IoTsdistributed: Distributed Training: Accelerate Model Training with Lightweight Cheetahserve: Model serving, tailored for edge inferencemlops: APIs related to machine learning operation platform (open.fedml.ai)centralized: Some centralized trainer code examples for benchmarking purposes.utils: Common utilities shared by other modules.About FedML, Inc.https://FedML.ai
Package: fedml-afaf
FedML - The community building and connecting AI anywhere at any scalehttps://doc.fedml.aiMissionFedML builds simple and versatile APIs for machine learning running anywhere at any scale.
In other words, FedML supports both federated learning for data silos and distributed training for acceleration with MLOps and Open Source support, covering industrial grade use cases and cutting-edge academia research.Distributed Training: Accelerate Model Training with Lightweight CheetahSimulator: (1) simulate FL using a single process (2) MPI-based FL Simulator (3) NCCL-based FL Simulator (fastest)Cross-silo Federated Learning for cross-organization/account training, including Python-based edge SDKCross-device Federated Learning for Smartphones and IoTs, including edge SDK for Android/iOS and embedded Linux.Model Serving: we focus on providing a better user experience for edge AI.MLOps: FedML's machine learning operation pipeline for AI running anywhere at any scale.Source Code StructureThe functionality of each package is as follows:core: The FedML low-level API package. This package implements distributed computing by communication backend like MPI, NCCL, MQTT, gRPC, PyTorch RPC, and also supports topology management.
Other low-level APIs related to security and privacy are also supported. All algorithms and Scenarios are built based on the "core" package.data: FedML will provide some default datasets for users to get started. Customization templates are also provided.model: FedML model zoo.device: FedML computing resource management.simulation: FedML parrot can support: (1) simulate FL using a single process (2) MPI-based FL Simulator (3) NCCL-based FL Simulator (fastest)cross-silo: Cross-silo Federated Learning for cross-organization/account trainingcross-device: Cross-device Federated Learning for Smartphones and IoTsdistributed: Distributed Training: Accelerate Model Training with Lightweight Cheetahserve: Model serving, tailored for edge inferencemlops: APIs related to machine learning operation platform (open.fedml.ai)centralized: Some centralized trainer code examples for benchmarking purposes.utils: Common utilities shared by other modules.About FedML, Inc.https://FedML.ai
Package: fedml-aws
IntroductionThe SAP Federated ML Python libraries (FedML) applies the Data Federation architecture of SAP DataWarehouse Cloud for intelligently sourcing data for Machine Learning experiments done at the Hyperscalers thereby removing the need for replicating or moving data.
By abstracting the Data Connection, Data load, and Model training (with flexibility and provision for user provided training scripts) for Hyperscaler Machine learning processes, the FedML library  provides end to end integration with few lines of code.InstallationInstall the SAP FedML AWS library using pip as follows:pip install fedml-awsDocumentation and getting startedFor getting started with the SAP FedML AWS Library and for documentation and sample notebooks, please refer the SAP FedML AWS github pagelink
Package: fedml-azure
IntroductionThe SAP Federated ML Python libraries (FedML) applies the Data Federation architecture of SAP Datasphere for intelligently sourcing data for Machine Learning experiments done at the Hyperscalers thereby removing the need for replicating or moving data.
By abstracting the Data Connection, Data load, and Model training (with flexibility and provision for user provided training scripts) for Hyperscaler Machine learning processes, the FedML library  provides end to end integration with few lines of code.InstallationInstall the SAP FedML Azure library using pip as follows:pip install fedml-azureDocumentation and getting startedFor getting started with the SAP FedML Azure Library and for documentation and sample notebooks, please refer the SAP FedML Azure github pagelink
Package: fedml-databricks
IntroductionThe SAP Federated ML Python library for Databricks applies the Data Federation architecture of SAP Datasphere for intelligently sourcing SAP as well as non-SAP data for Machine Learning experiments done in Databricks, thereby removing the need for replicating or moving the data. By abstracting the Data Connection, Data load, Model Deployment in SAP environment, and Inferencing for Machine learning processes , the FedML Databricks library provides end to end integration with few lines of code.InstallationInstall the SAP FedML Databricks library using pip as follows:pip install fedml-databricksDocumentation and getting startedFor getting started with the SAP FedML Databricks Library and for documentation and sample notebooks, please refer the SAP FedML Databricks github pagelink
Package: fedml-gcp
IntroductionThe SAP Federated ML Python libraries (FedML) applies the Data Federation architecture of SAP DataWarehouse Cloud for intelligently sourcing data for Machine Learning experiments done at the Hyperscalers thereby removing the need for replicating or moving data.
By abstracting the Data Connection, Data load, and Model training (with flexibility and provision for user provided training scripts) for Hyperscaler Machine learning processes, the FedML library  provides end to end integration with few lines of code.InstallationInstall the SAP FedML GCP library using pip as follows:pip install fedml-gcpDocumentation and getting startedFor getting started with the SAP FedML GCP Library and for documentation and sample notebooks, please refer the SAP FedML GCP github pagelink
Package: fedmsg_fasclient
An example of usingfedmsgto monitorFASfor messages, but delaying
action for a few seconds to accumulate messages and avoid pile-up.Running# Install virtualenvwrapper and restart you terminalsudoyuminstallpython-virtualenvwrapper# In a new terminalmkvirtualenvfedmsg-fasclientpythonsetup.pydevelopfedmsg-hubYou can tweak the settings in thefedmsg.d/directory.
Package: fedmsg-meta-fedora-infrastructure
fedmsg metadata providers for Fedora Infrastructure’s deploymentfedmsgis a set of tools for knitting together services
and webapps into a realtime messaging net.  This package contains metadata
provider plugins for the primary deployment of that system:Fedora
Infrastructure.If you were to deploy fedmsg at another site, you would like want to write your
own module like this one that could provide textual representations ofyourmessages.Pop into#fedora-appson freenode if you have questions or comments.Build StatusBranchStatusmasterdevelopRunning the Tests# Create a virtualenv and install deps
$ sudo dnf install python-virtualenv libffi-devel
$ virtualenv my-env
$ source my-env/bin/activate

# Install the dependencies
$ python setup.py develop

# Run the tests
$ pip install nose
$ $(which nosetests)Building the Docs# Install additional dependencies
$ pip install -r doc/requirements.txt
$ sphinx-build doc/ htmldocs/
Package: fedmsg_middleware
InstallationYou could install it yourself withpip:$ pip install fedmsg_middlewareOr you could addfedmsg_middlewareto the list of required packages in thesetup.pyfile of your project.Usage in TurboGears 2Simply editmyapp/config/middleware.pyand add the following tomake_app(...):# Wrap your base TurboGears 2 application with custom middleware here
import fedmsg_middleware
app = fedmsg_middleware.make_middleware(app)Usage in PyramidEditmyapp/__init__.pyand replace thereturn config.make_wsgi_app()line with the following:import fedmsg_middleware
app = config.make_wsgi_app()
app = fedmsg_middleware.make_middleware(app)
return appUsage in a PasteDeploy pipelineYou can enable it in your PasteDeploy pipeline like so:[pipeline:main]
pipeline =
    fedmsg_middleware
    my-app

[filter:fedmsg_middleware]
use = egg:fedmsg_middleware
topic = org.fedoraproject.prod.koji.*

[app:myapp]
...Get the sourceThe code and bug tracker live over athttp://github.com/ralphbean/fedmsg_middleware.
Please fork and improve!
Package: fedoo
fedooFinite element libraryAboutFEDOO is an open source Finite Element library developed in Python. It has originally been developed to allow easy creation and resolution of problems with the Proper Orthogonal Decomposition algorithm (based on a separated decomposition) and is now also actively developed for standard finite element features.Here are the main features:FEDOO is entirely written in Python 3Resolution of problems based on a separated decomposition (PGD, POD, Reduced bases)Static and Dynamics poblemsMesh import/export from msh (GMSH) and vtk formatExport results in vtk file for easy visualisation with Paraview (https://www.paraview.org/)Constitutive equation library including elasto-plastic law, composites law, ...Include many type of elements like cohesive elements, 2D, 3D, beam, ...Geometrical non linearitiesAnd many other....PyPI packageConda packageDocumentationLicenseWebsiteDocumentationThedocumentationis provided by readthedocs athttps://fedoo.readthedocs.io.InstallationInstallation with pip including recommanded dependencies excepted simcoon:pip install fedoo[all]Minimal installation with pip:pip install fedooInstallation with conda with recommanded dependencices (including simcoon):conda install -c conda-forge -c set3mah fedoo
Package: fedops
FedOps: Federated Learning Lifecycle Operations Management PlatformFedOps|Slack|LinkedIn|CCL Site|YoutubeFedOps (fedops) is a platform that helps organizations effectively manage and coordinate their federated learning operations:FLScalize: It simplifies the application of data and models in a FL environment by leveraging Flower's Client and Server.Manager: The manager oversees and manages the real-time FL progress of both clients and serverCE/CS: Contribution Evaluation and Client Selection processes based on their performance.CI/CD/CFL: the CI/CD/CFL system seamlessly integrates with a Code Repo,
enabling code deployment to multiple clients and servers for continuous or periodic federated learning.Monitoring: The FL dashboard is available for monitoring and observing the lifecycle of FL clients and serverFedOps TutorialFedOps has developed a web service to manage the lifecycle operations of federated learning on real devices.Install FedOps Library$pipinstallfedopsReal DevicesStart FedOps SiloStart FedOps MobileSingle MachineStart FedOps SimualtionCommunityPaperFLScalize: Federated Learning Lifecycle Management@article{CognitiveComputingLab,title={FLScalize:FederatedLearningLifecycleManagement},author={Semo Yang; Jihwan Moon; Jinsoo Kim; Kwangkee Lee; Kangyoon Lee},journal={IEEE Access},Page(s)={47212 - 47222}DOI={10.1109/ACCESS.2023.3275439}year={2023}}SupportFor any questions or issues, please contact the FedOps support team atgyom1204@gachon.ac.kr
Package: fedora
fedoraon PyPIThis dummy project is not installable.
You probably wantpython-fedorainstead.pyproject.toml tool.fedora namespaceThePEP 518declares
that a project can use the subtabletool.$NAMEifpyproject.tomlif,
and only if, they own the entry for$NAMEin the Cheeseshop/PyPI.That's what this project is for.
We own the entry forfedora, so we could usetool.fedorain .python-fedoraThe Fedora Infra'spython-fedoraproject provides an importable module namedfedora.This goes against the convention that PyPI distribution names should
match the module names.
But, python-fedora pre-dates wide use of that convention, and the issue
is hard to fix now.Please installpython-fedorato get the Fedora Infra's package.Please: Don't install packages blindlyWhen you see the exception:ModuleNotFoundError: No module named 'foo'… please research the actual requirements instead of going directly forpip install foo.
The project (distribution) name may differ from the module(s) it
provides.
Package: fedora-checksum-tester
Fedora Checksum TesterFedora Checksum Testeris a little script that can automatically run checksum
tests as defined inhttps://fedoraproject.org/wiki/QA:Testcase_Mediakit_Checksums.InstallationThe program is packed for PyPi, so you can install it easily using thepipcommand.pip install --user fedora_checksum_testerUsageNavigate into the directory where you have downloaded the images to check and
runfedora_checksum_testerwith those options. If you have not downloaded any
images to check, the program downloads the images automatically from Koji.Options--release: The release number of the Fedora compose, for which you want
to download the images.Rawhideis used by default.--compose: The compose identification. Mostly, this is a date in the form of
YYYYMMDD except for the release candidates. If left out, the program connects to
the release validation matrix and gets the number of the current compose. This is
probably how this program will be used mostly.--arch: Architecture for which to download the images.x86_64used by default.--variant: One possible variant, such asCloud,Everything,Server,Spins,Workstation. If not chosen, theEverythingwill be used as default.--subvariant: Can be used withSpins variant, to distinguish between
various spins, such asKDE,LXCD,Xfce. If left out, all spins will be
tested.--type: Can be used withServerto distinguish betweenbootanddvd. If
not selected, both images will be checked.--purge: If set toTrue, all downloaded images will be deleted after the test
has finished, independently of the result.--forcedownload: Not yet implemented. Normally, if the script finds out
that the requested image has already been downloaded, it will not download it again
to save time and bandwidth. If you explicitely want to download it anyway, useTrueto download the image.--report: If this is set toTrue, the script will report the results to the
Wiki matrix automatically. Also see--user.--user: The name of the user who reports the results. If left out, userdonkeywill be used.BugsIf you find a bug, report an issue athttps://github.com/lruzicka/checksum-tester.
Package: fedora-framework
The Fedora FrameworkThe Fedora Framework is an evolutionary feature engineering framework designed to streamline the process of creating and optimizing features for machine learning tasks. This project offers a flexible and extensible set of tools for feature engineering to help data scientists and machine learning engineers efficiently prepare their data for modelling.FeaturesModular Design:Fedora Framework is built around a modular architecture that allows you to extend and customize feature engineering components easily. You can mix and match different modules to suit your specific needs, using Context-Free Grammars.Automated Feature Generation:Fedora Framework provides built-in tools for automatic feature generation, reducing the manual effort required to create features. You can define feature operators and let the framework generate features based on your specifications.Feature Selection and Construction:Identify and select the most important features for your models using various feature engineering techniques.InstallationYou can install Fedora Framework fromPyPIusing pip:pip3installfedora-frameworkGetting StartedAfter installing the Fedora framework, check our examples in classical machine learning datasets in theexamplesfolder. Once inside this directory, to run the MNIST dataset example:cdmnist
python3main.pyDocumentationFor more in-depth documentation, please visit ourGitBook documentation.ContributingWe welcome contributions to Fedora Framework. Whether you want to add new features, fix bugs, improve documentation, or suggest enhancements, your contributions are valuable.Please reach out to us through the availablecommunication channels.LicenseFedora Framework is open-source and distributed under the MIT License. SeeLICENSEfor details.ContactIf you have questions, suggestions, or need support, feel free to reach out to us:GitHub Issues:https://github.com/miguelrabuge/fedora/issuesEmail:rabuge@dei.uc.pt/naml@dei.uc.ptCitationsIf you find this project useful or if you use any code, ideas, or resources from it, please consider citing the following sources:Rabuge, M., & Lourenço, N. (2023). The Fedora Framework (Version 1.0.1) [Computer software]. https://doi.org/10.5281/zenodo.10210815@software{Rabuge_The_Fedora_Framework_2023,author={Rabuge, Miguel and Lourenço, Nuno},doi={10.5281/zenodo.10210815},month=dec,title={{The Fedora Framework}},url={https://github.com/miguelrabuge/fedora},version={1.0.1},year={2023}}Publications🚧 Work in progress 🚧
Package: fedorainfra-ansible-messages
# fedorainfra-ansible messagesA schema package for [the fedora infrastructure ansible](https://pagure.io/fedora-infra/ansible). It defines messages for some scripts that are only kept in ansible, as well as messages ansible itself sends (like when a playbook starts)See the [detailed documentation](https://fedora-messaging.readthedocs.io/en/latest/messages.html) on packaging your schemas.
Package: fedora-messaging
This package provides tools and APIs to make using Fedora’s messaging
infrastructure easier. These include a framework for declaring message schemas,
a set of synchronous APIs to publish messages to AMQP brokers, a set of
asynchronous APIs to consume messages, and services to easily run consumers.This library is designed to be a replacement for thePyZMQ-backedfedmsglibrary in Fedora Infrastructure.To get started, check out ouruser guide.Looking to contribute? We appreciate it! Check out thecontributor guide.
Package: fedora-messaging-git-hook
Fedora Messaging Git HookThis repo contains a git post-receive hook to send messages on the Fedora Messaging bus.The full documentation ison ReadTheDocs.You can install itfrom PyPI.The development layout is a bit outdated (no poetry, setup.cfg, MANIFEST.in,
etc.) because we need to work with Python 3.6, and the Python ecosystem ship
has sailed a long time ago for that version.
Package: fedora-messaging-the-new-hotness-schema
the-new-hotness Message SchemaJSON schema definitions for messages published bythe-new-hotness.Documentation for the-new-hotness Message Schema could be foundhere.Seehttp://json-schema.org/for documentation on the schema format. Seehttps://fedora-messaging.readthedocs.io/en/latest/messages.htmlfor
documentation on fedora-messaging.