Saravanan666/pypi-org huggingface dataset:
Package: kedro-serving
General informationsSoftware repositoryLatest releaseTotal downloadsPypiCode healthBranchTestsCoverageLinksDocumentationDeploymentActivitymainWhat is kedro-serving?kedro-servingis a kedro-plugn for serving kedro pipelines. It aims to be easy to use, flexible, robust to be production ready.As it is in its early stage of developpement, it is highly discouraged to use it for production in its current state.Coming soon:enable parameters injection at runtimesupporting other inputs than pandas dataframedocumentation & examplessupport for production-ready ASGI server (e.g., gunicorn)extended configuration for artifactstestsHow do I install kedro-serving?kedro-servingis available on PyPI, so you can install it withpip:pip install kedro-servingIf you want to use the most up to date version of the package which is under development and not released yet, you can install the package from github:pip install --upgrade git+https://github.com/Galileo-Galilei/kedro-serving.gitGetting startedDocumentation coming soon...Release and roadmapTherelease historycentralizes packages improvements across time. The main features coming in next releases arelisted on github milestones. Feel free to upvote/downvote and discuss prioritization in associated issues.DisclaimerThis package is still in active development. We useSemVerprinciples to version our releases. Until we reach1.0.0milestone, breaking changes will lead to<minor>version number increment, while releases which do not introduce breaking changes in the API will lead to<patch>version number increment.If you want to see how to migrate from one version ofkedro-servingto another, see themigration guide.Can I contribute?We'd be happy to receive help to maintain and improve the package. Any PR will be considered (from typo in the docs to core features add-on) Please check thecontributing guidelines.Main contributorsThe following people actively maintain, enhance and discuss design to make this package as good as possible:Yolan Honoré-Rougé
Package: kedro-snowflake
Kedro Snowflake Pipelines pluginWe help companies turn their data into assetsAboutThis plugin allows to run full Kedro pipelines in Snowflake. Right now it supportsKedro starter, to get you up to speed fastautomatically creating Snowflake Stored Procedures from Kedro nodes (using Snowpark SDK)translating Kedro pipeline into Snowflake tasks graphrunning Kedro pipeline fully within Snowflake, without external systemusing Kedro's officialSnowparkTableDataSetautomatically storing intermediate data as Transient Tables (if Snowpark's DataFrames are used)(New!)MLflowintegration with Snowflake with example usage inSnowflightsKedro starterDocumentationFor detailed documentation refer tohttps://kedro-snowflake.readthedocs.io/UsageWith starterInstall the pluginpipinstall"kedro-snowflake>=0.1.0"Create new project with our Kedro starter ❄️Snowflights🚀:kedronew--starter=snowflights--checkout=masterAnd answer the interactive prompts ⬇️ (click to expand)Project Name
============
Please enter a human readable name for your new project.
Spaces, hyphens, and underscores are allowed.
 [Snowflights]: 

Snowflake Account
=================
Please enter the name of your Snowflake account.
This is the part of the URL before .snowflakecomputing.com
 []: abc-123

Snowflake User
==============
Please enter the name of your Snowflake user.
 []: user2137

Snowflake Warehouse
===================
Please enter the name of your Snowflake warehouse.
 []: compute-wh

Snowflake Database
==================
Please enter the name of your Snowflake database.
 [DEMO]: 

Snowflake Schema
================
Please enter the name of your Snowflake schema.
 [DEMO]: 

Snowflake Password Environment Variable
=======================================
Please enter the name of the environment variable that contains your Snowflake password.
Alternatively, you can re-configure the plugin later to use Kedros credentials.yml
 [SNOWFLAKE_PASSWORD]:       

Pipeline Name Used As A Snowflake Task Prefix
=============================================

 [default]:

Enable Mlflow Integration (See Documentation For The Configuration Instructions)
================================================================================

 [False]: 

The project name 'Snowflights' has been applied to: 
- The project title in /tmp/snowflights/README.md
- The folder created for your project in /tmp/snowflights
- The project's python package in /tmp/snowflights/src/snowflightsRun the projectcdsnowflights
kedrosnowflakerun--wait-for-completionIn existing Kedro projectInstall the pluginpipinstall"kedro-snowflake>=0.1.0"Initialize the pluginkedrosnowflakeinit<ACCOUNT><USER><PASSWORD_FROM_ENV><DATABASE><SCHEMA><WAREHOUSE>Run the projectkedrosnowflakerun--wait-for-completionKedro pipeline in Snowflake TasksExecution:
Package: kedro-softfail-runner
kedro-softfail-runnerThis file will become your README and also the index of your
documentation.Installpipinstallkedro_softfail_runnerHow to useFill me in please! Don’t forget code examples:kedro run --runner=kedro_softfail_runner.SoftFailRunnerWhy is it useful?The team created a soft-fail runner to transform errors into warnings,
allowing the pipeline to continue executing to the best of its ability
while providing a report of any nodes that failed, so that data issues
can be addressed. At that point, the pipeline run can be finalised by
executing only those missing nodes separately, using appropriate Kedro
syntax.https://kedro.org/blog/build-a-custom-kedro-runnerIt’s most useful in two different scenarios: 1. Development - you want
to detect all problematic nodes in one go 2. Deployment - you want to
run as much node as you can before the Kedro pipeline is stopped.Why is it not in Kedro yet?Note that theSoftFailRunnerdoes not return anything, that isresult = session.run()normally
returns a dictionary of free output, but it will be alwaysNoneforSoftFailRunnerdue to implementation problem.Other than that, there are no other known issues yet. In order to merge
this into the core library, this problem need to be fixed and the runner
should be test thoroughly.
Package: kedro-static-viz
Kedro-Static-Viz creates a static website for yourKedrodata pipelines.  Kedro static viz is a very small python cli that makes your kedro-viz generated pipeline.json data available to a static appication built with gatsbyjs.  The benefit of this is that it it can be built inside of a ci service such as github actions and deployed to a number to static file hosts such as Netlify, Now.sh, github pages, S3.  Free options are endless these days.  By default the site will be built to thepublicdirectory.On every push to master in this repostatic-viz.kedro.devis built and deployed.kedro static-viz!Thestatic_vizfunction is callable from inside your project directory.fromkedro_static_vizimportstatic_vizstatic_viz()fromkedro_static_viz.hooksimportStaticVizclassProjectContext(KedroContext):project_name="kedro0160"project_version="0.16.1"package_name="kedro0160"hooks=[StaticViz()]How do I install and use Kedro-Static-Viz?As a Kedro Python pluginKedro-Static-Viz is available as a Python plugin namedkedro-static-viz.The following conditions must be true in order to visualise your pipeline:Your project directory must be available to the Kedro-Static-Viz plugin.You must be using aKedrodata pipelines project structure with a complete Data Catalog, nodes and pipeline structure.To install it:pipinstallkedro-static-vizThis will installkedroas a dependency, and addkedro static-vizas an additional CLI command.To visualise your pipeline, go to your project root directory and install the project-specific dependencies by running:kedroinstallThis will install the dependencies specified inrequirements.txtin your Kedro environment (seethe Kedro documentationfor how to set up your Python virtual environment).Finally, run the following command from the project directory to visualise your pipeline:kedrostatic-vizThis command will run kedro_viz.server onhttp://127.0.0.1:4141/which cannot be accessed from another machine.Kedro-Viz has a number of options to customise running the visualisation:CLI commandDescription--portTCP port that viz will listen to. Defaults to 4141.--browser/--no-browserWhether to open viz interface in the default browser or not.--load-filePath to load the pipeline JSON file--pipelineName of themodular pipelineto visualise. If not set, the default pipeline is visualised.--env,-eKedro configuration environment. If not specified, catalog config inlocalwill be used.--directoryDirectory to render the static site to--serve/--no-serveWhether or not to serve the site after creating. Defaults to True.You're Awesomefor considering a contribution!  Contributions are welcome, please check out theContributing Guidefor more information.  Please be a positive member of the community and embrace feedbackWe useSemVerfor versioning. For the versions available, see thetags on this repository.- Waylon Walker -Original AuthorThis project is licensed under the MIT License - see the LICENSE file for details
Package: kedro-templar
Kedro TemplarAboutA plugin build forKedroto support templating configuration files.
Using this plugin you can specify a templates for your configuration files such ascatalog.yaml,paramters.yamland fill them automatically using one file with specified variables.
Using this approach you can easily parametrize you pipeline from single point.Usage examples:you can store multiple simultaneous configurations and switch between them on demand.change prefixes incatalog.yamlto reuse the same pipeline for different data,InstallPlugin can be easily installed using pypi repository.pipinstallkedro-templarTemplateskedro-templaris usingjinja2templating engine for rendering files.Examplecatalog.yamlLet's create a template forconf/base/catalog.yamlintemplates/base/catalog.yaml.sample_data:type:pandas.CSVDataSetsave_args:index:Truefilepath:s3://your_bucket/{{run_name}}/{{another_subname}}/data/sample_data.csvdefinition.yamlYour file that contains all variables used for templating.run_name:run_1another_subname:subfolderRender templatesTo render created template with provided variables run following command:kedrotemplarapply-cdefinition.yamlIf you choose to have different setup, you can check help command to see all available options:kedrotemplarapply--helpCommandsCurrently, this plugin supports 3 commands:applyA core logic of this plugin. This command is used
to create config files based on provided templates and a file with variables.You can setup a default values using environment variables:TEMPLAR_TEMPLATES_DIR- a default directory for your templatesTEMPLAR_OUTPUT_DIR- a default directory where templates will be renderedIf no value is specified explicitly and env var is empty,
the commands will use default value specified in the code.kedrotemplarapply-cdefinition.yamldownloadA helper function used to download a config file from a given S3 pathkedrotemplardownload-i<S3_PATH>/definition.yaml-odefinition.yamluploadA helper function used to upload a config file to a given S3 pathkedrotemplarupload-idefinition.yaml-o<S3_PATH>/definition.yamlContactPlugin was created by the Data Science team fromWebInterpret.
Package: kedro-vertexai
Kedro Vertex AI PluginAboutThe main purpose of this plugin is to enable running kedro pipeline on Google Cloud Platform - Vertex AI Pipelines.
It supports translation from Kedro pipeline DSL tokfp(pipelines SDK) and deployment to Vertex AI service with some convenient commands.The plugin can be used together withkedro-dockerto simplify preparation of docker image for pipeline execution.DocumentationFor detailed documentation refer tohttps://kedro-vertexai.readthedocs.io/Usage guideUsage: kedro vertexai [OPTIONS] COMMAND [ARGS]...

  Interact with Google Cloud Platform :: Vertex AI Pipelines

Options:
  -e, --env TEXT  Environment to use.
  -h, --help      Show this message and exit.

Commands:
  compile         Translates Kedro pipeline into JSON file with Kubeflow...
  init            Initializes configuration for the plugin
  list-pipelines  List deployed pipeline definitions
  run-once        Deploy pipeline as a single run within given experiment.
  ui              Open VertexAI Pipelines UI in new browser tabConfiguration filekedro initgenerates configuration file for the plugin, but users may want to adjust it to match the run environment
requirements. Check documentation for details -kedro-vertexai.readthedocs.io
Package: keebsound
KeebsoundKeebsound is a package that I built to generate realistic keyboard tap sounds. You can provide a string and it will "type" those characters out, providing ASMR goodness.
It also supports hooking into the keyboard and replacing your keytap sound with whichever key switch profile you desire.Getting StartedTODOPrerequisitesWhat things you need to install the software and how to install themGive examplesInstallingTODOUsageOptions:--hookThishooksintoyourkeyboardandplaysasoundwhenyoutype--switchesTEXTProvidethesoundprofile,bydefaultit'ssettogateron-red--delayFLOATSounddelaymultiplierforplayingkeyboardsoundfromastring--stringTEXTPasssomearbitrarystringtoplayakeyboardtypingsoundtypingsaidstring--helpShowthismessageandexit.Playing the sound of a stringpython main.py --string "Foo bar is not enough. What are you up to?" --delay 3Hooking to the keyboardpython main.py --hookYou can now start typing and it will generate sounds for you.Using explicit key switch soundspython main.py --string "Foo bar is not enough. What are you up to?" --switches gateron-redContributingTODOLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for detailsAcknowledgmentsHat tip to anyone whose code was usedInspirationetc
Package: keecli
KeeCLIKeeCLIis a command line interface to interact with KeePass databases. It takes a lot of inspiration frompass,
so if you used it in the past you will find a lot of similarities withKeeCLI.InstallationUsing pip:pipinstallkeecliFrom source:gitclonehttps://github.com/leoperard/keeclicdkeecli
pipinstallpoetry# if not already presentpoetryinstallConfigurationKeeCLIexpects 2 environment variables for the database file and the password associated.exportKEECLI_DB="/path/to/my/db.kdbx"exportKEECLI_PASSWORD="higly-secured-password"Note thatKeeCLIdoes not store your password. It only read it from the environment variable to open the database.CommandsGet an entryGet all the details of an entry in json format. If multiple entries are found, the result will be a list of entries.
You can reduce the scope of the search by specifying a group. All names are fuzzy matched. By default, the password
will be displayed with*characters, you can use the--passwordoption to print it in plain text.keecliget--help
Usage:keecliget[OPTIONS]NAMEGetdetailsforanentry.

Options:-g,--groupTEXTThegroupwheretolookfortheentry.--password/--no-passwordShowthepasswordinplaintext(default:false).--helpShowthismessageandexit.
Package: keefind
# Keefind## What it isKeefind is a simple utility to search in keepass database. I wrote it to beable to easy search for password and copy it to clipboard.## InstallationJust install latest version from PyPi:```> pip install keefind```## UsageTo use keefind you need to export couple environment variables:```> export KP_DATABASE=/path/to/keepass/database> export KP_PASSWORD_FILE=/path/to/file/with/password/to/database```Latter variable is a path to file which has to have one line with passwordto according keepass database.After that you can just start to use keefind.## ExamplesIf you need just get a password and more or less remember the structure ofyour database, you can search for exact variable. Let's say you have groupnamed 'Personal' which consist of groups 'Sites' and 'Banking'. Group 'Sites'has entry named 'github.com' in it with user 'xxx' and password 'yyy'. To getthe password you can call any of next commands```> kf pers githyyy> kf sit xxxyyy> kf pers sit github.com xxxyyy> kf pers yyyyy> kf xxxyyy```So basically under the hood keefind will get all arguments you passed toit and will try to find an entry which has all of these arguments in path,name, site, username or password fields. So in case you pass 'xxx' as anargument, it will show you all found results which have 'xxx' in them. Ifyou will pass 'xxx zzz', it will show you all found results which have**both** xxx **and*** zzz in them.There is just one available option you can use - '-v' to get more verboseoutput. Here is how to use it:```> kf -v xxxPersonal/Sites - github.comyyy> kf -v xxx | xsel -b -iPersonal/Sites - github.com> # Now your password for github is copied to clipboard. That's the trick,> # cause in case of single '-v' option passwords copied to stdout but all> # other info - to stderr, which allows you to pipe output with password(s)> kf -vv xxx{'group': 'Personal/Sites','password': 'yyy','username': 'xxx'}> kf -vvv xxx{'group': 'Personal/Sites','password': 'yyy','title': 'github.com','url': None,'username': 'xxx'}```That's mostly it, nothing more. Improvements and bugfixes are welcomed.## AuthorStanislaw Bogatkin (https://sbog.ru).
Package: keel
keelkeel is a program written in Python 3 and uses ncurses to display to the user a list of
processes and their respective PIDs.The user can easily kill the highlighted process by pressing the enter key.This was spawned from a cyber defense hackathon in the University of Glasgow. My team
had to defend our servers and we had one terminal window open to see all open connections
and another one simply for killing. With this, we combined it into one window.The program has/will have a way to switch the processes based on which user owns them
and will show either connections (tcp, udp, etc) as well as regular system processes.Follow theGithublink for more updates!ImportantThe program is not fully ready. At the moment, it is under development and not everything
will work.
Package: keel-telegram-bot
keel-telegram-botkeel-telegram-botis a telegram bot forKeel.FeaturesReceive notifications (via Webhook)List approvalsApprove pending approvalsReject pending approvalsDelete archived approvalsPermission handling based on telegram usernamesFilter visible approvals on a per-chat basisHow to useManual installationInstallInstallkeel-telegram-botusing pip:pip3installkeel-telegram-botConfigurationkeel-telegram-botusescontainer-app-confto provide configuration via a YAML file as well as ENV variables. Have a look at thedocumentation about it.Seekeel-telegram-bot_example.yamlfor an example in this repo.RunStart the bot by using:keel-telegram-botDockerTo runkeel-telegram-botusing docker you can use themarkusressel/keel-telegram-botimage from DockerHub:sudo docker run -t \
    markusressel/keel-telegram-bot:latestConfigure the image using either environment variables, or mount the configuration
file from your host system to/app/keel-telegram-bot.yaml.SetupThis bot uses the REST api provided by Keel to interact with it
and relies on the Webhook functionality to receive and forward notifications
to telegram chats. On one handkeel-telegram-botacts like the web
interface, on the other hand it acts like a proxy, both combined into a
single package.To get the commands working simply provide all the necessary details of
the configuration file.To get notifications working you will have to provide the address ofkeel-telegram-botto Keel using theWEBHOOK_ENDPOINTenv variable.
The simplest way to achieve this is by running both Keel andkeel-telegram-boton the same host and specifyinghttp://localhost:5000/.ContributingGitHub is for social coding: if you want to write code, I encourage contributions through pull requests from forks
of this repository. Create GitHub tickets for bugs and new features and comment on the ones that you are interested in.Licensekeel-telegram-bot by Markus Ressel
Copyright (C) 2020  Markus Ressel

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
Package: keenmqtt
`|Build Status| <https://travis-ci.org/ZoetropeLabs/keenmqtt>`_`|DocumentationStatus| <https://readthedocs.org/projects/keenmqtt/?badge=latest>`_`|CoverageStatus| <https://coveralls.io/github/ZoetropeLabs/keenmqtt?branch=master>`_keenmqtt========A MQTT client which will send configured MQTT messages to keen IO asevents for later analysis... figure:: docs/system_diagram.png:align: center:alt: System Diagram of keenmqttSystem Diagram of keenmqttThe problem~~~~~~~~~~~IoT data platforms are often a big investment in time and sometimesmoney, so often a simple MQTT set up is used. This should not preventone from being able to perform historical analysis of data points.See the **`blogarticle <https://zoetrope.io/tech-blog/simple-mqtt-iot-logging>`_** witha live example.The solution~~~~~~~~~~~~keenmqtt is a simple bridge which will listen for specified MQTTmessages and log them on your KeenIO project. This complete history ofevents will allow you to:1. Create graphs of old data, such as temperature.2. Use this data to refine your system.3. Display this data to your users.keenmqtt can be run as a standalone daemon, or used in a python program.Installation------------::pip install keenmqttOr clone/download the repo, run ``python setup.py install`` in the root.Usage-----Command Line~~~~~~~~~~~~Running the stand alone package requires a config file, see``example/config.yaml`` for a template.After installing, run the following to log events:::keenmqtt -c config.yamlIn your program~~~~~~~~~~~~~~~keenMQTT has been specifically designed so that almost any part of thepipeline can be overriden or customised.The source is well documented, `seereadthedocs <http://keenmqtt.readthedocs.org/en/latest/keenmqtt.html#module-keenmqtt.keenmqtt>`_**Example: Custom payload formats** As an example; if you had a sensorwhich publishes an ascii format sensor reading, you can define a custompayload decoder for topics which match that sensor value as follows:::from keenmqtt import KeenMQTTclass CustomDecoder(KeenMQTT):def decode_payload(self, topic, payload):"""Decode a plain ASCII format sensor reading"""if 'humidity' in topic:event = {"value": int(payload)}else:#Assume default JSON encodingevent = KeenMQTT.decode_payload(self, topic, payload)return eventContributing------------1. Fork it!2. Create your feature branch: ``git checkout -b my-new-feature``3. Commit your changes: ``git commit -am 'Add some feature'``4. Push to the branch: ``git push origin my-new-feature``5. Submit a pull requestHistory-------0.0.1: Working version of the CLI app.Credits-------Written by Ben Howes & Richard Webb of `Zoetrope <https://zoetrope.io>`_With thanks to:1. `KeenIO <https://keen.io>`_ for a super service.2. `Eclipse Paho <https://www.eclipse.org/paho/clients/python/>`_ for agreat MQTT client.License-------MIT Licence.. |BuildStatus| image:: https://travis-ci.org/ZoetropeLabs/keenmqtt.svg?branch=master.. |DocumentationStatus| image:: https://readthedocs.org/projects/keenmqtt/badge/?version=latest.. |CoverageStatus| image:: https://coveralls.io/repos/ZoetropeLabs/keenmqtt/badge.svg?branch=master&service=github