Saravanan666/pypi-org huggingface dataset:
Package: ml6-kfp-components
# ML6 kfp-componentsThis is a package for reusable kubeflow pipelines components.We consider reusable components to be so general that they can be used
in any pipeline without further customization.For the purposes of this repository, we distinguish three kinds of components:
- lightweight (python function) components
- custom image (python function) components
- yaml based componentsAn example for each type of component can be found underml6_kfp_components/examples.Lightweight components are simple python function components which are
built on public base images (possibly with minor additional installed packages).Custom image components are python function components with more custom dependencies.
We create a custom container image and host it in a publicly accessible Google Artifact Registry repository.Yaml based components are the most general framework.
Your component can be based on any programming language and can be arbitrarily complex.
Just package your component as a container image and define an interface using a yaml file.In this package, we not only define these components, but also preload them,
so that users can easily use the components by just installing this package and
importing the components they want to use.
Package: mLab-Doppler-Radar-Tool
mLab Doppler Radar Tool: UART serial & data plotbefore you install mLab Doppler Radar Tool,
please check if you already install PyQt5 or not.Table of content[Installation] (# install)[example script] (# run)[update] (# update)installRaspberry PiforRaspberry Pi,please install PyQt5 as following.
In Raspbian Stretch Lite the following worked for:$sudo apt-get update
$sudo apt-get install qt5-default pyqt5-dev pyqt5-dev-tools
$sudo python3 -m pip install mLab-Doppler-Radar-ToolLinuxforUbuntuuser, in linux terminal:$sudo python3 -m pip install PyQt5
$sudo python3 -m pip install mLab-Doppler-Radar-ToolWindowsforwindowsuser, in CMD terminal:python -m pip install PyQt5
python -m pip install mLab-Doppler-Radar-Toolrunthen copy, run our example code:from mLab_DopplerRadar import MainWindow
from PyQt5 import QtCore, QtWidgets, QtGui
import sys

if __name__ == "__main__":
    print("system start)
    app = QtWidgets.QApplication(sys.argv)
    ui = MainWindow()
    ui.show()
    sys.exit(app.exec_())like:sudo python3 <your file location/name>updateto update to the latest version, please uninstall and install again, or input:sudo python3 -m pip install --upgrade mLab-Doppler-Radar-ToolLicenseMIT License. Copyright (c) 2019 Agatha Kuan
Package: mlabtex
mlabtexPurposemlabtex provides a renderer for latex code in mayavi.Installationpip install mlabtexFunctionsThe following functions are providedrender_latex-- A renderer for latex-code to produce image files.mlabtex-- A renderer for latex code in mayavi.mlabimg-- A renderer for image files in mayavi.DependenciesNumPyMayaviFor renderingmatplotlibsympyExampleYou can use it like the mlab.surf routine:from mayavi import mlab
from mlabtex import mlabtex

text = (
    r'Sebastian M\"uller, '
    + r'$f(x)=\displaystyle\sum_{n=0}^\infty '
    + r'f^{(n)}(x_0)\cdot\frac{(x-x_0)^n}{n!}$'
)
tex = mlabtex(
    0., 0., 0.,
    text,
    color=(0., 0., 0.),
    orientation=(30., 0., 0.),
    dpi=1200,
)
mlab.axes()
mlab.show()Copyright Sebastian Mueller 2019
Package: mLab-WalkSpeed-Radar-Tool
before you install mLab Doppler Radar Tool,
please check if you already install PyQt5 or not.installRaspberry PiforRaspberry Pi,please install PyQt5 as following.
In Raspbian Stretch Lite the following worked for:$sudo apt-get update
$sudo apt-get install qt5-default pyqt5-dev pyqt5-dev-tools
$sudo python3 -m pip install mLab-WalkSpeed-Radar-Tool
Package: mlaction
mlactionMachine Learning in Action 机器学习实战 Python3GitHub:https://github.com/iOSDevLog/mlaction方法截图PyPI 境像加速pip3install-ihttps://pypi.tuna.tsinghua.edu.cn/simplemlaction代码规范参考：https://docs.python.org/3/tutorial.vscode/settings.json{"python.pythonPath":"/Users/iosdevlog/.Envs/mlaction/bin/python","python.linting.flake8Enabled":true,"python.formatting.provider":"yapf","python.linting.flake8Args":["--max-line-length=248"],"python.linting.pylintEnabled":false}字符串使用双引号：""安装pipinstallmlaction开发本地开发pip3install-e.测试pytestmlaction发布python3setup.pysdistbdist_wheel
twineuploaddist/*生成CHANGELOGnpm install -g conventional-changelog-cli
./version.sh联系方式网站:http://2019.iosdevlog.com/微信公众号: AI 开发日志Licensemlaction is released under the MIT license. SeeLICENSEfor details.
Package: mlads-lukem-fyp
Machine Learning for Anomaly Detection in Network TrafficA Final Year Project by Luke MorrisAn overviewI will be attempting to create a solution including machine learning to detect anomalies in a given dataset of network traffic. This will involve picking apart a PCAP provided by the user or using (wire|t)shark / tcpdump to read packets directly from the network to provide the machine learning algorithm with data. The ML algorithm will then be able to 'learn' normal traffic sequences. This enables the algorithm to determine when an anomaly is detected and thus alert the owner of the network.This project will be in the form of:A final report including a literature reviewThis softwareA portfolio including meeting minutes, CV and a self reviewThe software provided in this project contains a database, a machine learning algorithm trained to detect anomalies in a PCAP file, and a system to alert users.Installing the softwareThis software is available on PyPI as mlads_lukem_fyp and can be installed using pip:pip install mlads_lukem_fypRunning the softwareTo run the software and begin detecting anomalies, run the MLADS.py file from the mlads_lukem_fyp directory.Alternatively:>>> from mlads_lukem_fyp.MLADS import start_mlads
>>> start_mlads()Using MLADSView AlertsThe page used to view previous alerts or detections by the software. Alerts can be searched through using the fields at the top of the page.When an alert is highlighted, further details on the alert can be viewed.Analyse PCAPsPCAP files can be 'uploaded' to the software. The file is fed through a feature extractor into a CSV that is then used by the machine learning algorithm.Alerts are generated and sent via SMS and email. These alerts can also be viewed in the 'View Alerts' page.This page runs very slowly when loading a large file, please be patient.Edit ContactsThe contacts to be alerted when the software detects anomalies are kept up to date here, and contacts stored in a database.Live CaptureComing soon...
Package: mlagents
Unity ML-Agents TrainersThemlagentsPython package is part of theML-Agents Toolkit.mlagentsprovides a set of reinforcement and imitation learning algorithms designed to be
used with Unity environments. The algorithms interface with the Python API
provided by themlagents_envspackage. Seeherefor
more information onmlagents_envs.The algorithms can be accessed using the:mlagents-learnaccess point. Seeherefor more information on using this
package.InstallationInstall themlagentspackage with:python-mpipinstallmlagents==1.0.0Usage & More InformationFor more information on the ML-Agents Toolkit and how to instrument a Unity
scene with the ML-Agents SDK, check out the mainML-Agents Toolkit documentation.LimitationsResuming self-play from a checkpoint resets the reported ELO to the default
value.
Package: mlagents-envs
Unity ML-Agents Python InterfaceThemlagents_envsPython package is part of theML-Agents Toolkit.mlagents_envsprovides three Python APIs that allows direct interaction with the
Unity game engine:A single agent API (Gym API)A gym-like multi-agent API (PettingZoo API)A low-level API (LLAPI)The LLAPI is used by the trainer implementation inmlagents.mlagents_envscan be used independently ofmlagentsfor Python
communication.InstallationInstall themlagents_envspackage with:python-mpipinstallmlagents_envs==1.0.0Usage & More InformationSeeGym API GuidePettingZoo API GuidePython API Guidefor more information on how to use the API to interact with a Unity environment.For more information on the ML-Agents Toolkit and how to instrument a Unity
scene with the ML-Agents SDK, check out the mainML-Agents Toolkit documentation.Limitationsmlagents_envsuses localhost ports to exchange data between Unity and
Python. As such, multiple instances can have their ports collide, leading to
errors. Make sure to use a different port if you are using multiple instances
ofUnityEnvironment.Communication between Unity and the PythonUnityEnvironmentis not secure.On Linux, ports are not released immediately after the communication closes.
As such, you cannot reuse ports right after closing aUnityEnvironment.
Package: mlagents-jf
Unity ML-Agents TrainersThemlagentsPython package is part of theML-Agents Toolkit.mlagentsprovides a set of reinforcement and imitation learning algorithms designed to be
used with Unity environments. The algorithms interface with the Python API
provided by themlagents_envspackage. Seeherefor
more information onmlagents_envs.The algorithms can be accessed using the:mlagents-learnaccess point. Seeherefor more information on using this
package.InstallationInstall themlagentspackage with:python-mpipinstallmlagents==0.28.0Usage & More InformationFor more information on the ML-Agents Toolkit and how to instrument a Unity
scene with the ML-Agents SDK, check out the mainML-Agents Toolkit documentation.Limitationsmlagentsdoes not yet explicitly support multi-agent scenarios so training
cooperative behavior among different agents is not stable.Resuming self-play from a checkpoint resets the reported ELO to the default
value.
Package: mlagents-video-streamer
ML-Agents Video StreamerNow you can Live Stream the Agent's learning behavior to Twitch/YouTube from Google Colab while training these Agents.Try Google Colab NotebookWhat’s In This DocumentInstallationImports and UsageLicenseInstallation!pipinstallmlagents-video-streamerAnd if you already havemlagents-video-streamerthen upgrade it by this command.!pipinstall--upgrademlagents-video-streamerImports and Usagefrommlagents_video_streamerimportSetupVirtualDisplayfrommlagents_video_streamerimportVideoStreamerNow Setup the Virtual Display:SetupVirtualDisplay()Define your live stream information:# stream_info dictionary should be in this format onlystream_info={"URL":"rtmp://live.twitch.tv/app/",# example of Twitch URL"secret":"--- secret here ---"}Initialize theVideoStreamerclass withstream_info:videoStreamer=VideoStreamer(stream_info)*If you don't passstream_infothen it will simply store the video locally in thevideosdirectory.Start the video streamer before starting with the training process:videoStreamer.start()Now capture the training process, using subprocess library:importsubprocessfromrandomimportrandrangetry:train=subprocess.run(["mlagents-learn","config.yaml","--run-id=train-1","--env=3DBall_example/3DBall.x86_64","--base-port="+str(randrange(9000,9999))],cwd="/content/",stdout=subprocess.PIPE)print("Training process has been successfully ended.")exceptExceptionase:print("You killed the training process in between.")finally:videoStreamer.close()*At the end don't forget to close the video streamer by usingclose()method onvideoStreamerobject as shown in the above example.LicenseLicensed under theMIT License.
Package: mlagility
The MLAgility ProjectMLAgility offers a complementary approach to MLPerf by examining the capability of vendors to provide turnkey solutions to a corpus of hundreds of off-the-shelf models. All of the model scripts and benchmarking code are published as open source software. The performance data is available at ourHuggingface Space.Benchmarking ToolOurbenchitCLI allows you to benchmark Pytorch models without changing a single line of code. The demo below shows BERT-Base being benchmarked on both Nvidia A100 and Intel Xeon. For more information, check out ourTutorialsandTools User Guide.You can reproduce this demo by trying out theJust Benchmark BERTtutorial.1000+ ModelsThis repository is home to a diverse corpus of hundreds of models. We are actively working on increasing the number of models on our model library. You can see the set of models in each category by clicking on the corresponding badge.Benchmarking resultsWe are also working on making MLAgility results publicly available at ourHuggingface Space. Check it out!How everything fits togetherThe diagram above illustrates the MLAgility repository's structure. Simply put, the MLAgility models are leveraged by our benchmarking tool, benchit, to produce benchmarking outcomes showcased on our Hugging Face Spaces page.InstallationPlease refer to ourmlagility installation guideto get instructions on how to install mlagility.ContributingWe are actively seeking collaborators from across the industry. If you would like to contribute to this project, please check out ourcontribution guide.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: mlaide
Python Client Library for ML AideRun example scriptsInexamples/you can find some python scripts as an example.
The shell scripts can be used for easier usage.Run the following commands once in a terminal sessioncs/source./set_api_key.shAfter that you can use the following command to run the scripts
over and over again:./run_example.shContributionPrerequisitesInstallPythonInstallPython PoetryOptional - Install IDE:PyCharmorVisual Studio CodeSetup EnvironmentInstall environment and download dependenciespoetryinstallActivate environmentpoetryshellRun TestspytestRun Tests with Coveragecoverage run --branch --source mlaide -m pytest
coverage htmlBuildpoetry buildPublish (on PyPI)poetry publishLinksHomepage:https://mlaide.comQuickstart:https://docs.mlaide.com/start/quickstart/Tutorial:https://docs.mlaide.com/tutorial/introduction/Documentation:https://docs.mlaide.com/
Package: mlalgs
Machine Learning Algorithms from Scratch in PythonInstallationFirst, install Python 3.6 fromhttps://www.python.org, and then run:pip install mlalgsImplementationsLogistic Regression:Source,NotebookCitationIf you use the library in academic work, please consider citinghttps://doi.org/10.1117/12.2304418.Bibtex Entry:@inproceedings{tahmassebi2018ideeple,title={ideeple: Deep learning in a flash},author={Tahmassebi, Amirhessam},booktitle={Disruptive Technologies in Information Sciences},volume={10652},pages={106520S},year={2018},organization={International Society for Optics and Photonics}}APA Entry:Tahmassebi, A. (2018, May). ideeple: Deep learning in a flash. In Disruptive
Technologies in Information Sciences (Vol. 10652, p. 106520S). International
Society for Optics and Photonics.
Package: mlapi
MercadoLibre's Python SDKThis is the official Python SDK for MercadoLibre's Platform.How do I install it?clone repository
   https://github.com/mercadolibre/python-sdk.gitHow do I use it?The first thing to do is to instance aMeliclass. You'll need to give aclientIdand aclientSecret. You can obtain both after creating your own application. For more information on this please read:creating an applicationIncluding the LibInclude the lib meli in your projectAttentionDon't forget to set the authentication URL of your country in file lib/config.iniimportsyssys.path.append('../lib')frommeliimportMeliStart the development!Create an instance of Meli classSimple like thismeli=Meli(client_id=1234,client_secret="a secret")With this instance you can start working on MercadoLibre's APIs.There are some design considerations worth to mention.This SDK is just a thin layer on top of an http client to handle all the OAuth WebServer flow for you.There is JSON parsing. this SDK will includejsonfor internal usage.If you already have the access_token and the refresh_token you can pass in the constructormeli=Meli(client_id=1234,client_secret="a secret",access_token="Access_Token",refresh_token="Refresh_Token")How do I redirect users to authorize my application?This is a 2 step process.First get the link to redirect the user. This is very easy! Just:redirectUrl=meli.auth_url(redirect_URI="http://somecallbackurl")This will give you the url to redirect the user. You need to specify a callback url which will be the one that the user will redirected after a successfull authrization process.Once the user is redirected to your callback url, you'll receive in the query string, a parameter namedcode. You'll need this for the second part of the process.meli.authorize(code="the received code",redirect_URI="http://somecallbackurl")This will get anaccess_tokenand arefresh_token(is case your application has theoffline_access) for your application and your user.At this stage your are ready to make call to the API on behalf of the user.Making GET callsparams={'access_token':meli.access_token}response=meli.get(path="/users/me",params=params)Making POST callsparams={'access_token':meli.access_token}#this body will be converted into json for youbody={'foo':'bar','bar':'foo'}response=meli.post(path="/items",body=body,params=params)Making PUT callsparams={'access_token':meli.access_token}#this body will be converted into json for youbody={'foo':'bar','bar':'foo'}response=meli.put(path="/items/123",body=body,params=params)Making DELETE callsparams={'access_token':meli.access_token}response=meli.delete(path="/questions/123",params=params)ExamplesDon't forget to check out our examples codes in the folderexamplesCommunityYou can contact us if you have questions using the standard communication channels described in thedeveloper's siteI want to contribute!That is great! Just fork the project in github. Create a topic branch, write some code, and add some tests for your new code.Thanks for helping!
Package: mlapp
MLApp ·MLApp is a Python library for building scalable data science solutions that meet modern software engineering standards.MLApp was built and hardened in an enterprise context, to solve scalability issues for mid-size to Fortune 50 companies. It is applicable to a variety of data science use cases including machine learning, deep learning, NLP and optimization.Embedded MLOps: Standardizes the way models and their metadatas are registered, stored and deployed.Project scaffolding: Generates an opinionated project file structure that enforces modern engineering standards and improves readability and documentation across solutions.Boilerplates: Includes a library of pre-built model templates that can be easily customized to accelerate development of common use cases.Utilities: Includes an extendable set of utilities that increase developer productivity - including functions for selecting features and optimizing hyperparameters.Connectors: Allows developers to easily integrate their projects with common data and analytics services.Deployment integration: Applications built using MLApp can easily be deployed on common open and proprietary platforms, including Kubernetes and Azure Machine Learning.Getting startedInstall MLApp via pip:pip install mlappNavigate to an empty project folder and generate the project scaffold:mlapp initInstall a working example using boilerplates:mlapp boilerplates install basic_regressionUpdate the run.py file in your project directory to point to the Basic Regression asset that you just installed:configs = [
    {
        'config_path': "assets/basic_regression/configs/basic_regression_train_config.py",
        'asset_name': "basic_regression",
        'config_name': "basic_regression_config"
    }
]Execute the run.py script:python run.pyCongrats! You've trained your first model in MLApp. Take a look at the output directory to see the results.Next stepsA great place to start is thecrash course.You should also check out the fullproject documentation.ContributingWe welcome contributions from the community. Please refer toCONTRIBUTINGfor more information.
Package: ml-applications
PyTorch Lightning Applications以 PyTorch Lightning 實作的機器學習與深度學習應用。本地開發載下 repo：exportREPO=pytorch_lightning_applications
gitclonehttps://github.com/leemengtaiwan/$REPO.gitcd$REPO建立新的Anaconda環境並安裝函式庫：condacreate-n$REPOpython=3.6-y
condaactivate$REPOpipinstall-rrequirements.txt設置nbdev環境並以可修改的方式安裝此 package：nbdev_install_git_hooks
nbdev_build_lib
pipinstall-e.
nbdev_build_docs
Package: ml-apps
PyTorch Lightning Applications以 PyTorch Lightning 實作的機器學習與深度學習應用。本地開發載下 repo：exportREPO=pytorch_lightning_applications
gitclonehttps://github.com/leemengtaiwan/$REPO.gitcd$REPO建立新的Anaconda環境並安裝函式庫：condacreate-n$REPOpython=3.6-y
condaactivate$REPOpipinstall-rrequirements.txt設置nbdev環境並以可修改的方式安裝此 package：nbdev_install_git_hooks
nbdev_build_lib
pipinstall-e.
nbdev_build_docs
Package: mlaut
MLAUT is a modelling and workflow toolbox in python, written with the aim of simplifying large scale benchmarking of machine learning strategies, e.g., validation, evaluation and comparison with respect to predictive/task-specific performance or runtime. Key features are:automation of the most common workflows for benchmarking modelling strategies on multiple datasets including statistical post-hoc analyses, with user-friendly default settings.unified interface with support for scikit-learn strategies, keras deep neural network architectures, including easy user extensibility to (partially or completely) custom strategies.higher-level meta-data interface for strategies, allowing easy specification of scikit-learn pipelines and keras deep network architectures, with user-friendly (sensible) default configurationseasy setting up and loading of data set collections for local use (e.g., data frames from local memory, UCI repository, openML, Delgado study, PMLB).back-end agnostic, automated local file system management of datasets, fitted models, predictions, and results, with the ability to easily resume crashed benchmark experiments with long running times.
Package: mlax-nn
MLAX: Functional NN library built on top of Google JAXOverview|Installation|Quickstart|Examples|DocumentationWhat is MLAX?MLAX is a purely functional neural network library built on top of GoogleJAX.MLAX follows object-oriented semantics like Keras and PyTorch but remains fully
compatible with native JAX transformations.Learn more about MLAX onRead the Docs.InstallationInstall JAXfirst if you have not
already.pip install mlax-nnQuickstartThis is a simple lazy linear layer defined in MLAX.importjaxfromjaximport(numpyasjnp,nn,random)frommlaximportModule,Parameter,VariableclassLinear(Module):def__init__(self,rng,out_features):super().__init__()self.rng=Variable(data=rng)self.out_features=out_featuresself.kernel_weight=Parameter()self.bias_weight=Parameter()# Define a ``set_up`` method for lazy initialziation of parametersdefset_up(self,x):rng1,rng2=random.split(self.rng.data)self.kernel_weight.data=nn.initializers.lecun_normal()(rng1,(x.shape[-1],self.out_features))self.bias_weight.data=nn.initializers.zeros(rng2,(self.out_features,))# Define an ``forward`` method for the forward passdefforward(self,x,rng=None,inference_mode=False,batch_axis_name=()):returnx@self.kernel_weight.data+self.bias_weight.dataIt is fully compatible with native JAX transformations:defloss_fn(x,y,model):pred,model=model(x,rng=None,inference_mode=True)returnjnp.mean(y-pred)**2,modelx=jnp.ones((4,3),dtype=jnp.float32)y=jnp.ones((4,4),dtype=jnp.float32)model=Linear(random.PRNGKey(0),4)loss,updated_model=loss_fn(x,y,model)print(loss)# Now let's apply `jax.jit` and `jax.value_and_grad`(loss,updated_model),grads=jax.jit(jax.value_and_grad(loss_fn,has_aux=True))(x,y,model)print(loss)print(grads)For end-to-end examples with reference PyTorch implementations, visit MLAX'sGitHub.View the full documentation onRead the Docs.Bugs and Feature RequestsPleasecreate an issueon MLAX's
Github repository.ContributionIf you wish to contribute, thank you and please contact me by email:y22zong@uwaterloo.ca.
Package: mlbackend
# Hyperplan ML BackendThe goal of Hyperplan is to help you manage and serve your machine learning projects. Why should you use it ?You want to enhance your productivity: Write reusable code accross projects.You want to easily extend your application: Hook functions let you register side effects to execute on the data that flows through your algorithms.You want your application to be testable: Hyperplan is primarily made out of simple testable functions.