Saravanan666/pypi-org huggingface dataset:
Package: megaladon
MegaladonSail the Seas of Data and Dive into the Depths of Computation!Megaladon: The Orca is no match.Welcome aboard, shipmates! Let Megaladon be your steadfast vessel on the uncharted ocean of Big Data and Machine Learning. With our rich datasets and detailed mathematical models, there's no computational storm we can't weather together!"We didn't name it 'Megaladon' for nothing. It's big, it's powerful, and it's got a heck of a bite when it comes to data crunching!" - Captain CodebeardNavigation ChartDatasetSample(RowbyRow)[Input]--(Feedsinto)-->[Model]--(Outputs)-->DatasetSample(RowbyRow)Deck Logs (Changelog)v2.0.0 - The Leviathan Update- We've surfaced some serious computational power in this one! Modular integration of HuggingFace and OpenAI models.v1.5.0 - The Kraken Patch- Tightened up the tentacles of the code. Fewer bugs will be slipping through!v1.0.0 - Maiden Voyage- Initial launch! The Megaladon sets sail!Shipwright's Guide (Installation)Batten down the hatches and ready your terminal, it's time to summon the Megaladon:gitclonehttps://github.com/Megaladon-ds/Megaladon.gitcdMegaladon
pipinstall-rrequirements.txtNavigational Tools (Usage)Start your voyage with a good map.(Load your dataset)fromMegaladonimportMegaladon# Using OpenAI modelMegaladon=Megaladon(model_id="gpt-3",api_key="your-api-key",dataset="flax-sentence-embeddings/stackexchange_math_jsonl")# Using Hugging Face modelMegaladon=Megaladon(model_id="gpt2",dataset="flax-sentence-embeddings/stackexchange_math_jsonl")Set sail!(Generate explanations)explanations=Megaladon.run()Return to port.(Save your results)Megaladon.save_to_huggingface(explanations,'hf_output_dir')Please replace"your-api-key"with your actual OpenAI API key, and'hf_output_dir'with your desired output directory for the Hugging Face datasets.Lifeboats (Support)If you find yourself overboard in a sea of confusion, don't panic! Shoot a flare to our issue tracker on Github, and our dedicated crew will row to your rescue.Create New IssueCrow's Nest (Future Plans)New Species Detection- We're constantly exploring unknown waters to find and integrate new algorithms and data models into Megaladon.Crew Training- Comprehensive documentation and examples are on the horizon to help you get the most out of your voyage with Megaladon.Thank you for choosing to sail with Megaladon. May fair winds and calm seas guide your data journey!Happy Sailing!The Megaladon TeamTodo:Better promptMore seamless model handling, plug and play with any model from OpenAI or HuggingFace.Save to HuggingFace after each iteration is labeledPotentially use Parquet for optimized storageAdd in polymorphic or shape shifting preprocessing logic
Package: megaLib
megaLibmegaLib is a Python wrapper for theMegaport RESTful API.Megaportoperates a global, Software Defined Network which enables their customers to
manage their connectivity in close to real time. The Megaport API allows customers to interact with their account in
the same way as they might using theWeb Portal.Use the power of megaLib to order your very own virtual router in the cloud with connectivity to AWS in just a few
lines:Who Uses megaLibAnyone who has an existing Megaport account and would like to automate Megaport things in Python. If you don't have an
account you cansignup here.How it WorksmegaLib functions as a Python wrapper for thepublic Megaport APIby providing a library of
functions to simplify your next mega-project.If you would like more information on the Megaport API I suggest checking out theMegaport API documentationand theMegaport Knowledgebase Articleson the API.InstallationRequests is required, Requests can be installed using pipenv or pip:See theRequests documentationfor alternative installation
methods.Install megaLib using pip:Alternatively, install megaLib by cloningthis git repositoryto a local
directory, verifying the requirements are met and executing setup.py:UsageYou can find example code snippets for the functionshere.
Examples of more functional code arehere.
Additional resources can be found on theWiki.Built WithRequests- Python HTTP Requests for Humans.How to ContributeCheck for open issues or open a fresh issue to start a discussion around a feature idea or a bug.Forkthe repositoryon GitHub to start making your changes to the master
branch (or branch off of it).Write a test which shows that the bug was fixed or that the feature
works as expected.Send a pull request and bug the maintainer until it gets merged and published.LicenseThis project is licensed under the GNU General Public License v3.0 - seeLICENSEfor more details.DisclaimerThis project including this git repository, is not affiliated, associated, authorized, endorsed by, or in any way
officially connected with Megaport, or any of its subsidiaries or its affiliates.Please refrain from contacting Megaport regarding any issues or support of this project. Instead feel free to submit an
issue via this git repository.
Package: meganote-python-editor-extension
python_editorRun python script using Kernel.RequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstallpython_editorUninstallTo remove the extension, execute:pipuninstallpython_editorContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the python_editor directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstallpython_editorIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedpython-editorwithin that folder.Testing the extensionFrontend testsThis extension is usingJestfor JavaScript code testing.To execute them, execute:jlpm
jlpmtestIntegration testsThis extension usesPlaywrightfor the integration tests (aka user level tests).
More precisely, the JupyterLab helperGalatais used to handle testing the extension in JupyterLab.More information are provided within theui-testsREADME.Packaging the extensionSeeRELEASE
Package: megaparsex
creditsname by Liam Mooreintroductionmegaparsexis a Python module that features parsing and associated
utilities. It features parsing functions that detect keyphrases in input
text and that then return text, seek confirmations and run functions in
response. It features functions that accept a list of parsing functions
so that collections of parsing functions can be combined and new parsing
functions can be added easily.It features a confirmation class that contains the ability to check
responses for confirmation or denial while containing a function with
optional keyword arguments to run on detection of confirmation together
with optional specialized prompts and responses to confirmation and
denial. It features a command class that contains the ability to request
an input that is to be run as a command and to run that command, and
contains a prompt.Some associated utilities are functions for manual input, to run system
commands, to report IP information, to report weather information, to
report system status, to restart a script, and to make a reverse-SSH
connection.See example code for basic usage information.setupsudopipinstallmegaparsexcoding ideasThe multiparse function can combine a number of predefined or custom
parser functions. Simple interactions involve responding to a single
text input with text or by running a function. More complex interactions
involve responding to a single text input but with a confirmation object
which is used to ask for a confirmation before proceeding with defined
actions. Still more complex interactions could involve nested parsers,
whereby a parser or set of parsers is used to extract a response and
that response can be used to propagate to nested parsers.
Package: megaparsy
Work In Progress - ignore for now ;)Initial motivation for this project was to build an indentation-sensitive parser. This is problematic for any parser library which is limited to expressing context-free grammars (which is most of them), unless you use a separate pre-processing step (like e.g. the parser for the Python language does).Googling around this topic I came uponhttps://markkarpov.com/tutorial/megaparsec.html#indentationsensitive-parsingSo in Haskell-land we can find ‘Monadic Parser Combinators’, which do not have this restriciton to CFGs. And theMegaparseclibrary defines many useful combinators, including some which designed specifically to help with building indentation-sensitive parsers.But… it was difficult for me to learn to make a parser for the first timeandlearn Haskell simultaneously. So I looked around for a Python option and found the  brilliantParsylibrary. It provides an implementation of ‘Monadic Parser Combinators’ in Python.Nicely and simply explained here:Functional or Combinator Parsing explained by Professor Graham Hutton.So inMegaparsyI have ported a bunch of the Megaparsec combinators over to Parsy. It is surprising how smoothly it went and how similar they come out - this is testament to the genius of Parsy, which is where all the clever part is.(I haven’t done all of them yet, but the indentation-sensitive ones are here)I’ve also roughly translated across the property-based tests from their Haskell QuickCheck originals into PythonHypothesisones.
Package: megapi
# Python For MegaPi## How To Use* Compile and upload the Firmware to MegaPi ( https://github.com/Makeblock-Official/FirmwareForMegaPi )* On your Raspberry Pi,```sudo pip install megapi```* Insert the initial code for starting MegaPi first.```from megapi import *bot = MegaPi()bot.start()```* python your code## Python API* Start* **MegaPi**()* **start**()* GPIO* **digitalWrite**( pin, level )* **pwmWrite**( pin, pwm )* **digitalRead**( pin, **def** onResult )* **analogRead**( pin, **def** onResult )* Motion* DC Motor* **dcMotorRun**( port, speed )* Servo Motor* **servoRun**( port, angle )* Encoder Motor* **encoderMotorRun**( port, speed )* **encoderMotorMove**( port, speed, distance, **def** onFinish )* **encoderMotorMoveTo**( port, speed, position, **def** onFinish )* **encoderMotorSetCurPosZero( port )* **encoderMotorPosition( port, **def** onResult )* **encoderMotorSpeed( port, **def** onResult )* Stepper Motor* **stepperMotorRun**( port, speed )* **stepperMotorMove**( port, speed, distance, **def** onFinish )* **stepperMotorMoveTo**( port, speed, position, **def** onFinish )* **stepperMotorSetCurPosZero( port )* Sensors* Ultrasonic Sensor* **ultrasonicSensorRead** ( port, **def** onResult )* LineFollow Sensor* **lineFollowerRead** ( port, **def** onResult )* Light Sensor* **lightSensorRead** ( port, **def** onResult )* Sound Sensor* **soundSensorRead** ( port, **def** onResult )* Temperature Sensor* **temperatureRead** ( port, **def** onResult )* PIR Motion Sensor* **pirMotionSensorRead** ( port, **def** onResult )* Touch Sensor* **touchSensorRead** ( port, **def** onResult )* LimitSwitch* **limitSwitchRead** ( port, slot, **def** onResult )* Humiture Sensor* **humitureSensorRead** ( port, type, **def** onResult )* Gas Sensor* **gasSensorRead** ( port, **def** onResult )* Flame Sensor* **flameSensorRead** ( port, **def** onResult )* Button* **buttonRead** ( port, **def** onResult )* Potentiometer* **potentiometerRead** ( port, **def** onResult )* Joystick* **joystickRead** ( port, axis, **def** onResult )* 3-Axis Accelerometer and Gyro Sensor* **gyroRead** (port, axis, **def** onResult )* Compass* **compassRead** ( **def** onResult )* Display* RGB Led* **rgbLedSetColor** ( port, slot, index, r, g, b )* **rgbLedShow** ( port, slot )* **rgbLedDisplay** ( port, slot, index, r, g, b )* 7-segment Display* **sevenSegmentDisplay** ( port, value )* Led Matrix Display* **ledMatrixDisplayMessage** ( port, x, y, msg )* **ledMatrixDisplayRaw** ( port, buffer )* Serial LCD Display* **lcdDisplay** ( string )* Others* DSLR Shutter* **shutterOn** ( port )* **shutterOff** ( port )* **focusOn** ( port )* **focusOff** ( port )
Package: mega-pip
Mega-PipPython packaging and distrubution made easy.Installationpip3 install mega-pipCommandspublish: Publish python package-p/--pypi: Publish to pypi (default).-t/--testpypi: Publish to testpypi.reset: Removedist,build, and*.egg_infodirectories.InstructionsMake asetup.pyfile.Runmp publish.To reset, runmp reset.
Package: megaplanpy
The library support only JSON format.Supports the API version of 2011.04+SVN has been updated to version 2011.05+Example usage#!/usr/bin/env python
# -*- coding: UTF-8 -*-

from megaplanpy import Megaplan

def main():
    account = 'my_account'
    login = 'my_login'
    password = 'my_password'

    mplan = Megaplan(account, login, password)

    tasks = mplan.GetTasks().data['tasks']

if __name__ == '__main__':
    main()
Package: megaqc
MegaQC is a web application that collects results from multiple runs of MultiQC
and allows bulk visualisation.See the MultiQC website for installation instructions and documentation:https://megaqc.info/MegaQC was written by Phil Ewels (http://phil.ewels.co.uk) and Denis Moreno at SciLifeLab Sweden (http://www.scilifelab.se)
Package: megaradrp
This is Megara DRP, the data reduction pipeline for MEGARA,
the optical Integral-Field Unit and Multi-Object Spectrograph
designed for the Gran telescopio Canarias (GTC).You can installmegaradrpusing
thereleased code in PyPIor
thedevelopment version in Github.The installation instructions are available in theonline documentationordoc/installation.rstin the source distribution.LicensingMegara DRP is distributed under GNU GPL, either version 3 of the License,
or (at your option) any later version. See the file LICENSE.txt for details.AuthorsWebpage:https://guaix.fis.ucm.es/megaraMaintainers: Sergio Pascual <sergiopr@fis.ucm.es>, Nicolás Cardiel <cardiel@ucm.es>
Package: megara-tools
Tools for the analysis and visualization of data from the MEGARA,
the optical Integral-Field Unit and Multi-Object Spectrograph
designed for the Gran telescopio Canarias (GTC).You can installmegara-toolsusing
thereleased code in PyPIor
thedevelopment version in Github.Licensingmegara-tools is distributed under GNU GPL, either version 3 of the License,
or (at your option) any later version. See the file LICENSE.txt for details.AuthorsContact point: Armando Gil de Paz <agil@fis.ucm.es>The full list of authors is available inAUTHORS.rstin
the source distribution.
Package: megara-visual-glue
GlueMegaraThis visualization tool for the MEGARA multi-spectrograph (https://guaix.fis.ucm.es/megara) has been developed with the Glue viewer, since this offers the possibility of defining custom code functions and including them in the file called 'data_viewer.py'. The process has been accomplished at the Universidad Complutense de Madrid in order to create an extensible tool in Python for the analysis of data obtained with MEGARA.
Package: megasepryte
Megasepryte v1.0Class Project:Projetc.nameList of commands:-   init_project("absolute_path")Does so and so.-   new_scene("scene_name)Jadajadajada-   import_aseprite_file(scene_name, aseprite_file_path)bla bla-   list_scenes()-   export_palette("scene_name")-   export_tiles("scene_name")-   export_plane_a("scene_name")-   export_plane_b("scene_name")-   export_scene("scene_name")-   test_scene("scene_name")-   save_project()-   save_project_as("absolute_path")
Package: mega-spark
Mega Spark说明该项目旨在通过spark进行一站式数据分析与模型训练，保证最终落地的只有分析报告，可视化，以及模型训练评估结果，其次
该项目将pysaprk封装成mega对象来延续pandas的使用方法，进而实现在大数据场景下的使用pandas方法进行
数据分析和模型训练，消除相关同学在spark上投入的学习成本目前提供以下模块：mlmega_xgboostsqlmegaframetomega本地安装如果要给该项目贡献代码，在本地调试好后测试，本地安装方法$ git clone .
$ cd megaspark
$ python install .在线安装建议使用官方镜像，安装最新版本。$ pip install mega-spark -i https://pypi.Python.org/simple/使用教程以tomega模块为例，安装完成之后，可以使用以下方法导入使用import megaspark.tomega as mg

data_df = mg.read_csv("path/to/file.csv")
data_df.mega.head(5)打包发布$ cd /path/to/megaspark
$ python3 setup.py sdist bdist_wheel
$ pip3 install twine
$ python3 -m twine upload dist/*注意事项如果使用ml模块中的xgboost，需要在SPARK_HOME/jar中添加xgboost4j-0.72.jar以及xgboost4j-spark-0.72.jar
Package: megatemp
# AGM library[![Build Status](https://travis-ci.com/Brian-Williams/actagm.svg?token=z7gYD5GwEhzpEFmQxsP2&branch=master)](https://travis-ci.com/Brian-Williams/actagm)### Example usage` >>> from actagm.agm_helper import AgmHelper >>> ah =AgmHelper(<dnsname or ip address>, <user>, <password>) >>> ah.login() >>> r =ah.get('/cluster')>>>print(r.json())`### Platform Dependant Requirements
AgmHelper requires openssl version 1.0.1+ for TLSv1.2. We do not backport ssl so python 2.7.9+ or 3.4+ is required.
Package: megatools
Megatools (1.10.3)(UNDER CONSTRUCTION)Simple python Megatools wrapperMegatoolsMegatools (github)InstallationpipinstallmegatoolsUsageInitializationfrommegatoolsimportMegatoolsmegatools=Megatools()# Use megatools command in path# ORmegatools=Megatools(executable="D:\\megatools\\megatools.exe")Get mega link filenamefilename=megatools.get_filename("https://mega.nz/#!PpVB0CTZ!bwa51HbeKaVjuCff_lzbH4nQnV27uBxmcF89PnnACvY")# boot_T295XXU2ASJ1.img.tardl - download exported files from mega.nz# Basic usagemegatools.dl("https://mega.nz/#!PpVB0CTZ!bwa51HbeKaVjuCff_lzbH4nQnV27uBxmcF89PnnACvY")# With all optional arguments and their default valuesmegatools.dl("https://mega.nz/#!PpVB0CTZ!bwa51HbeKaVjuCff_lzbH4nQnV27uBxmcF89PnnACvY",path=None,no_progress=False,print_names=False,disable_resume=False,username=None,password=None,reload=False,limit_speed=0,proxy=None,netif=None,ip_proto=None,config=None,ignore_config_file=False,display_output=False,debug=None,version=None)For more informations, please see megatools dl commandmegatoolsdl--helpAnother commands incommingTODOBetter get_filename algo, especially about error/returncode
Package: megatron-11b
Megatron 11BPorting of Megatron LM 11B model published on facebook on Huggingface Transformers.This repo contains the model's code, checkpoints and parallelization examples.Installationpip install megatron-11bUsage1. TokenizerThe usage of tokenizer is the same as other tokenizers of the existing Huggingface.BOS and EOS token are automatically attached, so if you want to use it as a prompt, please exclude EOS token (using[:-1])frommegatron_11bimportMegatronTokenizertokenizer=MegatronTokenizer.from_pretrained("hyunwoongko/megatron-11B")tokens=tokenizer.encode("Kevin is")# [0, 21910, 16] ---> include EOStokens=tokenizer.encode("Kevin is")[:,:-1]# [0, 21910, 16, 2] ---> exclude EOS2. ModelWe currently support the CausalLM model and the SequenceClassification model.The usage of model is also the same as other models of the existing Huggingface.frommegatron_11bimportMegatronForCausalLM,MegatronForSequenceClassificationmodel_clm=MegatronForCausalLM.from_pretrained("hyunwoongko/megatron-11B")model_clf=MegatronForSequenceClassification.from_pretrained("hyunwoongko/megatron-11B")3. Generationfrommegatron_11bimportMegatronForCausalLM,MegatronTokenizertokenizer=MegatronTokenizer.from_pretrained("hyunwoongko/megatron-11B")model=MegatronForCausalLM.from_pretrained("hyunwoongko/megatron-11B").half().cuda()inputs="Kevin is"inputs=tokenizer.encode(inputs,return_tensors="pt").cuda()[:,:-1]# exclude EOSoutput=model.generate(inputs,num_beams=5,no_repeat_ngram_size=4,repetition_penalty=1.2)print(tokenizer.batch_decode(output))output of generation.
Kevin is a great guy.
4. Model ParallelismCurrently, I'm preparing an opensource calledParallelformersthat can parallelize all models of Huggingface Transformers.I plan to support model parallelization through this library. (maybe I can release it next month)The relevant code can be found viaMegatronPolicyobject below.fromparallelformers.polices.baseimportPolicy,Layerfromparallelformers.utils.dist_utilsimportAllReduceLinearfrommegatron_11b.modeling_megatronimportMegatronDecoderLayerclassMegatronPolicy(Policy):@staticmethoddefreplace_arguments(config,world_size):return{# 1. reduce hidden size"self_attn.embed_dim":config.d_model//world_size,# 2. reduce number of heads"self_attn.num_heads":config.encoder_attention_heads//world_size,}@staticmethoddefattn_qkv():return[Layer(weight="self_attn.q_proj.weight",bias="self_attn.q_proj.bias",),Layer(weight="self_attn.k_proj.weight",bias="self_attn.k_proj.bias",),Layer(weight="self_attn.v_proj.weight",bias="self_attn.v_proj.bias",),]@staticmethoddefattn_out():return[Layer(weight="self_attn.out_proj.weight",bias="self_attn.out_proj.bias",replace=AllReduceLinear,),]@staticmethoddefmlp_in():return[Layer(weight="fc1.weight",bias="fc1.bias",),]@staticmethoddefmlp_out():return[Layer(weight="fc2.weight",bias="fc2.bias",replace=AllReduceLinear,),]@staticmethoddeforiginal_layer_class():returnMegatronDecoderLayerReferenceshttps://github.com/pytorch/fairseq/tree/master/examples/megatron_11bhttps://github.com/huggingface/transformers/pull/10301
Package: megawidget
A mix of standard Tkinter widgets and megawidgets (theme used:cyberpunk-theme)MegawidgetA collection of useful widgets for your Tkinter Python appsThis project is part of thePyrustic Open Ecosystem.Installation|ReferenceOverviewThemegawidgetsare extensively used in multiple projects:Hubstore: a desktop app to distribute, promote, discover, manage, install, and run Python apps.Dresscode: high-productivity framework to build desktop apps.Jupitest: graphical test runner.Rustiql: graphical SQLite editor.and more...Read thereference!Installationpipinstallmegawidget