Saravanan666/pypi-org huggingface dataset:
Package: mlflow-ray-serve
MLflow-Ray-ServeAn experimental plugin that integratesRay Servewith the MLflow pipeline.mlflow-ray-serveenables MLflow users to deploy MLflow models at scale on Ray Serve.This plugin implements thePython APIandcommand-line interfacefor MLflow deployment plugins.Installationpipinstallmlflow-ray-serveThe following packages are required and will be installed along with the plugin:"ray[serve]""mlflow>=1.12.0"This plugin requires Ray version 1.7.0 or greater.UsageThis plugin must be used with a detached Ray Serve instance running on a Ray cluster.  An easy way to set this up is by running the following two commands:raystart--head# Start a single-node Ray cluster locally.servestart# Start a detached Ray Serve instance.The API is summarized below. For full details see the MLflow deployment pluginPython APIandcommand-line interfacedocumentation.Create deploymentDeploy a model built with MLflow using Ray Serve with the desiredconfiguration parameters; for example,num_replicas.  Currently this plugin only supports thepython_functionflavor of MLflow models, and this is the default flavor.CLImlflowdeploymentscreate-tray-serve-m<modeluri>--name<deploymentname>-Cnum_replicas=<numberofreplicas>Python APIfrommlflow.deploymentsimportget_deploy_clienttarget_uri='ray-serve'plugin=get_deploy_client(target_uri)plugin.create_deployment(name=<deploymentname>,model_uri=<modeluri>,config={"num_replicas":4})Update deploymentModify the configuration of a deployed model and/or replace the deployment with a new model URI.CLImlflowdeploymentsupdate-tray-serve--name<deploymentname>-Cnum_replicas=<newnumberofreplicas>Python APIplugin.update_deployment(name=<deploymentname>,config={"num_replicas":<newnumberofreplicas>})Delete deploymentDelete an existing deployment.CLImlflowdeploymentsdelete-tray-serve--name<deploymentname>Python APIplugin.delete_deployment(name=<deploymentname>)List deploymentsList the names of all the models deployed on Ray Serve.  Includes models not deployed via this plugin.CLImlflowdeploymentslist-tray-servePython APIplugin.list_deployments()Get deployment detailsCLImlflowdeploymentsget-tray-serve--name<deploymentname>Python APIplugin.get_deployment(name=<deploymentname>)Run prediction on deployed modelFor the prediction inputs, DataFrame, Tensor and JSON formats are supported by the Python API.  To invoke via the command line, pass in the path to a JSON file containing the input.CLImlflowdeploymentspredict-tray-serve--name<deploymentname>--input-path<inputfilepath>--output-path<outputfilepath>output-pathis an optional parameter. Without it, the result will be printed in the terminal.Python APIplugin.predict(name=<deploymentname>,df=<predictioninput>)Plugin helpPrints the plugin help string.CLImlflowdeploymentshelp-tray-serve
Package: mlflow-registry-mongostore
mlflow-tracking-mongostoreMlflow plugin to use MongoDB as a backend for the MLflow tracking service. To use this plugin, you need a running instance of MongoDB.Run 'pip install mlflow-tracking-mongostore' to register the plugin as an entrypoint with MongoDB backend.Installationpip install mlflow-tracking-mongostoreUsagemlflow server --backend-store-uri mongodb://USER:PASSWORD@MONGO_HOST:DB_NAMEORmlflow server --backend-store-uri mongodb+srv://USER:PASSWORD@$MONGO_HOST:DB_NAME
Package: mlflow-rest-client
Python client forMLflowREST API.Features:UnlikeMLflow Tracking clientall REST API methods are exposed to user.All class fields are validated withpydantic.Basic and Bearer auth is supported.All methods and classes are documented.Limitations:There is no integration with ML frameworks and libraries.
You should use officialMLflow clientinstead.There is no integration with S3 or other artifact storage type.
You should access it directly withboto3or other client.Only Python 3.7+ is supported. Python 3.6 and lower already reached end of life.DocumentationSeehttps://mlflow-rest-client.readthedocs.io/Contribution guideSeeCONTRIBUTING.rstSecuritySeeSECURITY.rstInstallationStable releaseStable version is released on every tag tomasterbranch. Please use stable releases on production environment.
Version example:2.0.0pipinstallmlflow-rest-client==2.0.0# exact versionpipinstallmlflow-rest-client# latest releaseDevelopment releaseDevelopment version is released on every commit todevbranch. You can use them to test some new features before official release.
Version example:2.0.0.dev5pipinstallmlflow-rest-client==2.0.0.dev5# exact dev versionpipinstall--premlflow-rest-client# latest dev versionDevelopmentClone repo:gitclonegit@github.com:MobileTeleSystems/mlflow-rest-client.gitcdmlflow-rest-clientInstall dependencies for development:pipinstall-rrequirements-dev.txtInstall pre-commit hooks:pre-commitinstallpre-commitautoupdatepre-commitinstall-hooksTest pre-commit hooks run:pre-commitrun--all-files-vUsageMake sure you have anMLflow Tracking Serverrunning.frommlflow_rest_clientimportMLflowRESTClientclient=MLflowRESTClient("https://mlflow.domain",ignore_ssl_check=True)experiment=client.get_or_create_experiment("experiment_name")run=client.create_run(experiment.id)Seesample.pyfor more examples.
Package: mlflow-server-proxy
MLFlow Server ProxyMLFlow Server Proxy lets you run arbitrary external MLFlow tracking server alongside your notebook server and provide authenticated web access to them using a path/mlflownext to others like/lab.Alongside the python package that provides the main functionality, the JupyterLab extension@jupyterlab/server-proxyprovides buttons in the JupyterLab launcher window to get to MLFlow tracking server.This package was built using thejupyter-server-proxycookiecutter template.🤹‍InstallationPython packageYou can currently install this package from PyPI.pipinstallmlflow-server-proxyTo install with all the resources then usepipinstall'mlflow-server-proxy[resources]'The above command will install MLFlow as well.🤹‍SetupThe environment variableMLFLOW_STOREhas to be set as backend storage for MLFlow to log models and artifacts. Default is/tmpThis environment variable is then used in setting server uri while using MLFlow in your code as below:importmlflowserver_uri=os.environ["MLFLOW_STORE"]mlflow.set_tracking_uri(server_uri)RequirementsInstall MLFlowThis package's executes the standardmlflow servercommand. This command assumes themlflowexecutable required to start the application is globally available.pipinstallmlflowInstall Jupyter NotebookThis extension relies on the Jupyter Notebook to run.Refer to Jupyter's official documentaionfor installation instructions.Release HistoryVersion 0.1.0Initial release - File based backendMaintainerGottam Sai Bharath🏆ContributingWe welcome Your interest in the American Express Open Source Community on Github. Any Contributor to
any Open Source Project managed by the American Express Open Source Community must accept and sign
an Agreement indicating agreement to the terms below. Except for the rights granted in this
Agreement to American Express and to recipients of software distributed by American Express, You
reserve all right, title, and interest, if any, in and to Your Contributions. Pleasefill out the Agreement.🗝️LicenseAny contributions made under this project will be governed by theApache License 2.0.🗣️Code of ConductThis project adheres to theAmerican Express Community Guidelines. By participating, you are expected to honor these
guidelines.
Package: mlflow-slurm
MLFlow-SlurmBackend for executing MLFlow projects on Slurm batch systemUsageInstall this package in the environment from which you will be submitting jobs.
If you are submitting jobs from inside jobs, make sure you have this package
listed in your conda or pip environment.Just list this as your--backendin the job run. You should include a json
config file to control how the batch script is constructed:mlflowrun--backendslurm\--backend-configslurm_config.json\examples/sklearn_elasticnet_wineIt will generate a batch script named after the job id and submit it via the
Slurmsbatchcommand. It will tag the run with the Slurm JobIDConfigure JobsYou can set values in a json file to control job submission. The supported
properties in this file are:Config File SettingUsepartitionWhich Slurm partition should the job run in?accountWhat account name to run undergpus_per_nodeOn GPU partitions how many GPUs to allocate per nodegresSLURM Generic RESources requestsmemAmount of memory to allocate to CPU jobsmodulesList of modules to load before starting jobtimeMax CPU time job may runsbatch-script-fileName of batch file to be produced. Leave blank to have service generate a script file name based on the run IDDevelopmentThe slurm docker deployment is handy for testing and development. You can start
up a slurm environment with the included docker-compose file
Package: mlflow-token
mlflow-tokenObtain an access token for an MLFlow instance deployed behind OAuth2-proxy and
keycloak.This script will use your current setting ofMLFLOW_TRACKING_URIto look for
the keycloak redirect from it's OAuth2-proxy. From there it will start an
OAuth device flow to allow you to obtain a valid access token. You can use this
to update yourMLFLOW_TRACKING_TOKENby executing the command as%export$(mlflow-token)and following the prompt.Usage In Jupyter NotebookIf you want to authenticate to an MLFlow instance from within a Jupyter notebook
you can add the following lines to a cell:importmlflow_tokenmlflow_token.setup_mlflow_environment("https://mlflow-demo.software-dev.ncsa.illinois.edu/")This will update the notebook'sos.environso you can immediately use the
mlflow SDK. The token will eventually expire, so you may need to occasionally
re-run the cell.The cell will print the url for the user to visit and wait for the device
flow to complete.
Package: mlflow-tracking-mongostore
mlflow-tracking-mongostoreMlflow plugin to use MongoDB as a backend for the MLflow models registry service. To use this plugin, you need a running instance of MongoDB.Run 'pip install mlflow-registry-mongostore' to register the plugin as an entrypoint with MongoDB backend.Installationpip install mlflow-registry-mongostoreUsagemlflow server --backend-store-uri mongodb://USER:PASSWORD@MONGO_HOST:DB_NAMEORmlflow server --backend-store-uri mongodb+srv://USER:PASSWORD@$MONGO_HOST:DB_NAME
Package: mlflow-watsonml
Mlflow-WatsonMLA plugin that integratesWatsonMLwith MLflow pipeline.mlflow_watsonmlenables mlflow users to deploy mlflow pipeline models into WatsonML.
Command line APIs of the plugin (also accessible through mlflow's python package) makes the deployment process seamless.InstallationPlugin package which is available in pypi and can be installed usingpipinstallmlflow-watsonmlWhat does it doInstalling this package uses python's entrypoint mechanism to register the plugin into MLflow's plugin registry. This registry will be invoked each time you launch MLflow script or command line argument.AuthenticationIn order to connect to WatsonML, refer to.env.templateCreate deploymentThecreatecommand line argument andcreate_deploymentpython
APIs does the deployment of a model built with MLflow to WatsonML.CLImlflowdeploymentscreate-twatsonml-m<model-uri>--name<deployment-name>-C"software_spec_type=runtime-22.2-py3.10"Python APIfrommlflow.deploymentsimportget_deploy_clienttarget_uri='watsonml'plugin=get_deploy_client(target_uri)plugin.create_deployment(name=<deployment-name>,model_uri=<model-uri>,config={"software_spec_type":"runtime-22.2-py3.10"})Update deploymentUpdate API can used to modify the configuration parameters such as number of workers, version etc., of an already deployed model.
WatsonML will make sure the user experience is seamless while changing the model in a live environment.CLImlflowdeploymentsupdate-twatsonml--name<deploymentname>-C"software_spec_type=runtime-22.1-py3.10"Python APIplugin.update_deployment(name=<deploymentname>,config={"software_spec_type":"runtime-22.1-py3.10"})Delete deploymentDelete an existing deployment. Exception will be raised if the model is not already deployed.CLImlflowdeploymentsdelete-twatsonml--name<deploymentname/versionnumber>Python APIplugin.delete_deployment(name=<deploymentname>)List all deploymentsLists the names of all the models deployed on the configured WatsonML.CLImlflowdeploymentslist-twatsonmlPython APIplugin.list_deployments()Get deployment detailsGet API fetches the details of the deployed model. By default, Get API fetches all the versions of the deployed model.CLImlflowdeploymentsget-twatsonml--name<deploymentname>Python APIplugin.get_deployment(name=<deploymentname>)Run Prediction on deployed modelPredict API enables to run prediction on the deployed model.For the prediction inputs, DataFrame and JSON formats are supported. The python API supports all of these three formats. When invoked via command line, one needs to pass the json file path that contains the inputs.CLImlflowdeploymentspredict-twatsonml--name<deploymentname>--input-path<inputfilepath>--output-path<outputfilepath>output-path is an optional parameter. Without output path parameter result will be printed in console.Python APIplugin.predict(name=<deploymentname>,df=<predictioninput>)Plugin helpRun the following command to get the plugin help string.CLImlflowdeploymentshelp-twatsonml
Package: mlflow-wrapper
MLFlow WrapperMlflow Wrapper is a python library intended to abstract some functionality away from the developer
when interacting with the mlflow library.
The library supports/ improves the handling of experiments and offers helper functions which are not available in mlflow
by default.
Furthermore, the library offers functions to interact with Runs itself, and uploading/downloading files.Installationpip install mlflow-wrapperQuick startSome quick start examples. Please refer to thewikifor a complete overview of all functions.Experiment Handlerfrom mlflow_wrapper.experiment_handler import ExperimentHandler

# Connect to a local mlflow server
exp_handler:ExperimentHandler = ExperimentHandler()

# Get the id of a new experiment which does no exist so far
exp_id = exp_handler.get_experiment_by_name(experiment_name="New Experiment")Run Handlerfrom mlflow_wrapper.run_handler import RunHandler

run_handler: RunHandler = RunHandler()

# Delete a parent run and all associated children run. 
# Does only delete the first occurence of the given run name. If multiple runs do have the same name,
# this command needs to be executed multiple times

run_handler.delete_run(experiment_id=exp_id, run_name="My Run")Bugs & IssuesPlease use the GitHub issue tracker for issues. I will try to get to them asap.FeedbackFeedback is most welcomed and I will respond asap.
Package: mlflow-xethub
MLflow-XetHubThis plugin integrates XetHub with MLflow so that you can use existing MLflow code to track experiments but store artifacts to XetHub.Install pluginInstall from PyPI for the plugin's published versionpip install mlflow[xethub]Or clone this repo and install locally for the latest codegitclonehttps://github.com/xetdata/MLflow-XetHub.gitcdMLflow-XetHub
pipinstall.Authenticate with XetHubIf you haven't already,create an XetHub account.The plugin usesPyXetto access XetHub, so you need to authenticate with XetHub in one of the following two ways.Option 1:Log in with Xet CLIxet login --email <email address associated with account> --user <user name> --password <personal access token>Option 2:Export xet credentials as environment variablesexportXET_USER_EMAIL=<email>exportXET_USER_NAME=<username>exportXET_USER_TOKEN=<personal_access_token>Create a XetHub repo to store your artifactsGo tohttps://xethub.com/andcreate a new repoto store your MLflow artifacts.Orlog in with Xet CLIandxet repo make  xet://<username>/<repo> --private / --publicRun your MLflow as isRun MLflow server specifying XetHub repo to store artifactNo need to modify your MLflow code. The plugin will automatically detect MLflow runs and artifacts and store them in your XetHub repo once you start the MLflow server with:mlflowserver--backend-store-uri./mlruns--artifacts-destinationxet://<username>/<repo>/<branch>--default-artifact-rootxet://<username>/<repo>/<branch>which uses themlrunsdirectory on your machine as file store backend and XetHub asartifact storebackend.Run MLflow experimentExperiments are logged in the directory where MLflow server is started, and the plugin and MLflow need to be running in the same python environment.
So make sure to run your MLflow code and server in the same directory as well as having the plugin and MLflow installed under the same environment.UsingMLflow's quickstartas an example,importmlflowimportosimportnumpyasnpfrommlflowimportlog_artifactsfromsklearn.model_selectionimporttrain_test_splitfromsklearn.datasetsimportload_diabetesfromsklearn.ensembleimportRandomForestRegressorwithmlflow.start_run():mlflow.autolog()db=load_diabetes()X_train,X_test,y_train,y_test=train_test_split(db.data,db.target)# Create and train models.rf=RandomForestRegressor(n_estimators=100,max_depth=6,max_features=3)rf.fit(X_train,y_train)# Use the model to make predictions on the test dataset.predictions=rf.predict(X_test)ifnotos.path.exists("outputs"):os.makedirs("outputs")withopen("outputs/pred.txt","w")asf:f.write(np.array2string(predictions))log_artifacts("outputs")Store artifacts on XetHub and visualize in MLflow UIThe artifacts will be automatically stored on XetHub under the specified repo and branch.And the MLflow server will show the artifacts with UI on the defaulthttp://127.0.0.1:5000or your own host.
Package: mlflow-yarn
mlflow-yarnBackend implementation for runningMLFlow projectsagainst a Hadoop/YARN backend).To activate just do 'pip install mlflow-yarn' along your mlflow environment. It will register thepluginas an entrypoint with the YARN backend.$pipinstallmlflow-yarnmlflow-yarn only supports Python ≥3.6.DevelopementInstall from source$gitclonehttps://github.com/criteo/cluster-pack
$cdcluster-pack
$pipinstall.ExamplesExample withpip only projectDependencies are pulled from requirements.txt$gitclonehttps://github.com/criteo/mlflow-yarn
$pipinstallmlflow
mlflowruntests/resources/pip_project-ecompute_intersection-Psize=10000--backendyarnExample withconda projectDependencies are pulled from conda.yaml$gitclonehttps://github.com/criteo/mlflow-yarn
$pipinstallmlflow
$mlflowruntests/resources/conda_project-ecompute_intersection-Psize=10000--backendyarnMore infoson setting up a project file.Docker container environment is currently not supported.
Package: mlfns
Machine Learning - Python utility functionsConvenient Python utility functions commonly used in the AI machine learning
projects.Build PackageReferences:Packaging Python ProjectsDependencies:setuptools,wheelandtwine$ python setup.py sdist bdist_wheel

# For testing, upload to test.pypi.org
$ twine upload --repository-url https://test.pypi.org/legacy/ dist/*

# Install package from test.pypi.org
pip install --index-url https://test.pypi.org/simple/ --no-deps mlfns

# Release upload to pypi.org
$ twine upload --repository-url https://pypi.org/legacy/ dist/*

# Install package from test.pypi.org
pip install --index-url https://pypi.org/simple/ --no-deps mlfns
Package: mlfompy
MLFoMpyMLFoMpy is a post-processing tool for semiconductor TCAD data. The center object of this tool is the fds (FoMpy Dataset),
in which through the parsers, all the necessary information is stored. There are also defined different utilities to
process the data stored in the fds object, as to extract figures of merit, to plot or to save results in files.The figure shows basic workflow behind the MLFoMpy library:MLFoMpy documentationThe project documentation is available inhttps://mlfompy.readthedocs.io/In the project folderexamplesthere are several scripts andJupyter Notebooksthat show the library functionality:Basic usage of the software functionalities inbasic_usage.pyFigure of merit extraction for drift-diffusion, Monte Carlo, or combined simlations inextract_dd_mc.pyMachine learning models and utilities to predict figures of merit and I-V characteristic intrain_mlp_ler.ipynb,train_mlp_mgg_fom.ipynbandtrain_mlp_mgg_iv.ipynbInstallationFirst you need to have installedpip3on your system. For Ubuntu, open up a terminal and type:sudo apt update
sudo apt install python3-pipInstalation of MLFoMpy via pip3For basic usage of the tool (figure of merit extraction), install the tool using pip3:pip3 install mlfompyTo add the machine learning functionality for figure of merit and I-V characteristic
predictions, install the library using the command:pip3 install mlfompy[ML]and check the library is installed by importing it from apython3 terminal:import mlfompyUnless an error comes up, MLFoMpy is now installed on your environment.For more detailed explanation about instalation, please, check
thedocumentation.Documentation generationDocumentation of the project should be available athttps://mlfompy.readthedocs.io/.
To generate a local copy of the documentation, first it is necesary to install the
toolSphinxwith the following command:pip3 install sphinxThen, to generate the documentation, run the following command in the project directory:sphinx-build -a doc DOC_DESTINATION_DIRDOC_DESTINATION_DIR is the directory where the documentation will be generated.
Normally you should choose a directory outside of the project directory.Once generated, the documentation, in HTML format, can be opened using a web browser,
using the following destination:file://DOC_DESTINATION_DIR/index.html