Saravanan666/pypi-org huggingface dataset:
Package: optimizely-cli
optimizely-cli is a command-line interface for Optimizely projects.It aims to give you quick access to your experiment data as well as store a
local copy of your data so that you can manage and track changes in the version
control system of your choice.System Requirementsoptimizely-cli runs on Linux and Mac OS X, and requires Python 2.7.x+. It may
work on other versions of Python.InstallationInstall or upgrade optimizely-cli using pip:$ pip install –upgrade optimizely-cliIf you get some sort of error about TLS versions like this:“There was a problem confirming the ssl certificate: [SSL: TLSV1_ALERT_PROTOCOL_VERSION]”Then install a newer version of pip and then install again:$ curlhttps://bootstrap.pypa.io/get-pip.py| pythonGetting StartedTo use optimizely-cli, you need to link it to your Optimizely account.
The quickest way to get started is to run theopti initcommand and follow
the instructions:$ opti init
    First visit https://app.optimizely.com/v2/profile/api to create a new access token
    Enter the token you created here:
    Verifying token...
    Token is valid
    Credentials written to .optimizely-credentials.json
    Do not add this file to version control!
    It should stay private

    Checking for an existing project...
    Successfully created project (id: <project_id>)
    Config file written to .optimizely.jsonYou can move.optimizely-credentials.jsonto your home directory (theoptitool will look there as long as your project is under your home):$ mv .optimizely-credentials.json ~/Or add it to your .gitignore file like this:$ echo .optimizely-credentials.json >> .gitignoreDon’t commit your credentials to version control!UsageFor a full list of commands, just typeoptiat the command line. Here are some examples of available sub-commands:attributeManage Audience AttributesaudienceManage Optimizely audiencesenvironmentManage EnvironmentseventList, create, and manage Optimizely eventsexperimentList, create, and manage Optimizely experimentsfeatureManage FeaturesgroupManage Optimizely exclusion groupsinitLink an Optimizely project with your repositoryprojectList, create, and manage Optimizely projectspullPull down the current state of an Optimizely projectpushPush back local data to an Optimizely projectExamplesGet a list of projects$ opti project listCreate a new project$ opti project createGet a list of events$ opti event listCreate a new event$ opti event create new_eventPull all Optimizely experiment data and write them to a localoptimizely/directory$ opti pullCollect changes to your localoptimizely/directory and apply them your data in Optimizely$ opti pushCommand CompletionTheoptiutility can complete your commands. Just add the following to your .bashrc:eval “$(_OPTI_COMPLETE=source opti)”ContributingPlease see ourContributing Guidelines.Additional CodePlease seeAdditional Code.Any use of the Optimizely Service is subject to ourTerms of Serviceor the separate, written agreement between your company and Optimizely (if any).
Package: optimizers-af
optimizers_afoptimizers_afprovides functions for optimizing objective functions.PrerequisitesBefore you begin, ensure you have installed the latest version of Python.Installingoptimizers_afTo installoptimizers_af, follow these steps:Linux and macOS:pip3 install optimizers-afWindows:pip install optimizers-afUsingoptimizers_afThere are will be examples of how to useoptimizers_af.Contributing tooptimizers_afTo contribute tooptimizers_af, follow these steps:Fork this repository.Create a branch:git checkout -b <branch_name>.Make your changes and commit them:git commit -m '<commit_message>'Push to the original branch:git push origin <project_name>/<location>Create the pull request.Alternatively see the GitHub documentation oncreating a pull request.Contributors@AlbertFarkhutdinovContactIf you want to contact me you can reach me atalbertfarhutdinov@gmail.com.LicenseThis project uses the following license:MIT License.
Package: optimizo
OptimizoOptimizo its an tool that allows to set up instructions and run them later. In the mean you will have to run them manually usingpython optimizo.py -r group_nameHow to Install: For usersClone itpip install optimizoPlaypython -m optimizo -hHow to Install: For developersClone itgit clone https://github.com/takumade/Optimizo.gitInstall Requirementscd Optimizo && pip install -r requirementsPlaypython optimizo.py -hFor ContributorsClass name are in camel case e.gKillServerVariables, Functions and Methods should be in snake case e.gkill_serverFile names should be in snake_case e.gpush_to_github.pyInstructions are executed fromclasses/run_instructions.pyInstructions are added fromclasses/add_instructions.pyHow does it workYou create your config fileoptimizo.py -cORoptimizo.py -c -w .It will generate something like this:{
    "develop": [{
        "name": "copy",
        "src": "C:\\xampp\\htdocs\\projects\\Optimizo\\config.py",
        "dst": "C:\\xampp\\htdocs\\projects\\Optimizo\\classes"
    }, {
        "name": "replace",
        "src": "C:\\xampp\\htdocs\\projects\\Optimizo\\config.py",
        "search": "excluded_folders",
        "replace": "excluded_beans"
}]}You then add your instructions like thisMethod 1: Interactiveoptimizo.py -g develop -aMethod 2: Semi-Interactiveoptimizo.py -g develop -i moveIt will ask you for instruction name and extra details.
You can also add multiple instructions like theseNote:Develop here is a group name in your config, it can becats, chickens, deploy, hide, etcNote:* If a group is not available it is addedRun your group of instructionoptimizo.py -r developNote:If you know what you are doing you can manually modifyoptimizo.jsonNote:Instructions are run one after the other.InstructionsHere is a list of supported intructionsInstructionDescriptionImplementedmoveMove a file from one part to anotherYescopyCopy a file from source to destYesreplaceReplace text in a fileYesminifyMinify text in a fileYescommandExecute a commandYesWhat am i doing with this toolI use it optimize and deploy my Laravel and Python projectsI use to automate some menial tasks
Package: optiml
OptiMLOptiML is a sklearn compatible implementation ofSupport Vector MachinesandDeep Neural Networks,
both with some of the most successful features according to the state of the art.This work was motivated by the possibility of being able to solve the optimization problem deriving from the mathematical
formulation of these models through a wide range of optimization algorithms object of study and developed for the
Numerical Methods and Optimization course  @Department of Computer Science@University of Pisaunder the supervision of prof.Antonio Frangioni.ContentsNumerical OptimizationUnconstrained OptimizationLine Search Methods1st Order MethodsSteepest Gradient DescentConjugate GradientFletcher–Reeves formulaPolak–Ribière formulaHestenes-Stiefel formulaDai-Yuan formula2nd Order MethodsNewtonQuasi-NewtonBFGSL-BFGSStochastic MethodsStochastic Gradient DescentMomentumPolyakNesterovAdamMomentumPolyakNesterovAMSGradMomentumPolyakNesterovAdaMaxMomentumPolyakNesterovAdaGradAdaDeltaRMSPropMomentumPolyakNesterovSchedulesStep sizeDecayingLinear AnnealingRepeaterMomentumSutskever BlendProximal Bundle withcvxpyinterface toecos,osqp,scs,etc.Constrained Quadratic OptimizationBox-Constrained Quadratic MethodsProjected GradientFrank-Wolfe or Conditional GradientActive SetInterior PointLagrangian DualAugmented Lagrangian DualMachine LearningSupport Vector MachinesFormulationsPrimalWolfe DualLagrangian DualSupport Vector ClassifierLossesHinge (L1 Loss)Squared Hinge (L2 Loss)Support Vector RegressionLossesEpsilon-insensitive (L1 Loss)Squared Epsilon-insensitive (L2 Loss)KernelsLinearSVCSVRPolynomialSVCSVRGaussianSVCSVRLaplacianSVCSVRSigmoidOptimizers (ad hoc)Sequential Minimal Optimization (SMO)QP solver withqpsolversinterface tocvxopt,quadprog,qpOASES,etc.Neural NetworksNeural Network ClassifierNeural Network RegressorLossesMean Absolute Error (L1 Loss)Mean Squared Error (L2 Loss)Binary Cross EntropyCategorical Cross EntropySparse Categorical Cross EntropyRegularizersL1 or LassoL2 or Ridge or TikhonovActivationsLinearSigmoidTanhReLUSoftMaxLayersFully ConnectedInitializersXavier or Glorot (normal and uniform)He (normal and uniform)Installpip install optimlLicenseThis software is released under the MIT License. See theLICENSEfile for details.
Package: optimModels
optimModelsThe Python framework under development will allow the strain design of single and microbial community cultures.
The main focus is to provide support for different modeling approaches.Simulation: allows to simulate different kinetic metabolic models considering different phenotypes.
For the kinetic models simulation, we use the Odespy framework, which offers a unified interface to a large collection of software for solving systems of ordinary differential equations (ODEs).Optimization: perform the optimization using evolutionary computation. The inspyred framework is used for creating biologically-inspired computational intelligence algorithms in Python, including evolutionary computation.DocumentationFor documentation and API please check:http://optimModels.readthedocs.io/Instalationpip install optimModelsCredits and LicenseDeveloped at:Centre of Biological Engineering, University of Minho (2017)Released under an Apache License.
Package: optimo
Optimo Python libraryThis is a python library forOptimoRoute’s web service.Note:Although this is used internally and we continually improve it, it
is still Beta software. Use with care.RequirementsPython (has only been tested with python 2.7)An access key provided to you byOptimoRouteInstallationInstallation usingpip:pip install optimoUsagefromdatetimeimportdatetimefromdecimalimportDecimalfromoptimoimportOptimoAPI,Driver,WorkShift,Order,RoutePlan# Instantiate the interface to optimoroute's API.optimo_api=OptimoAPI('https://api.optimoroute.com','some_access_key')# Create a workshift for the driver.work_shift=WorkShift(start_work=datetime(year=2014,month=12,day=5,hour=8,minute=0),end_work=datetime(year=2014,month=12,day=5,hour=14,minute=0),)# Create the driver and also pass the workshift we created above.driver=Driver(id='123',start_lat=Decimal('53.350046'),start_lng=Decimal('-6.274655'),end_lat=Decimal('53.341191'),end_lng=Decimal('-6.260402'),work_shifts=[work_shift],)order1=Order(id='123',lat=Decimal('53.343204'),lng=Decimal('-6.269798'),duration=20,)order2=Order(id='456',lat=Decimal('53.341820'),lng=Decimal('-6.264991'),duration=25,)# Create the route plan, and also pass the orders and the driver created above.# The 'request_id' is the one we'll use later to check the results of the# plan optimization.routeplan=RoutePlan(request_id='1234',callback_url='https://callback.com/1234',status_callback_url='https://status.callback.com/1234',drivers=[driver],orders=[order1,order2],)# Start the plan optimization. If there is no exception raised then we assume# success.optimo_api.plan(routeplan)# Get the result of the optimization. We use the 'request_id' we provided to# optimoroute previously.data=optimo_api.get('1234')# If the optimization has finished we can see the result. Otherwise the data# will be None.{u'creationTime':u'2014-12-04T17:01:52',u'requestId':u'1234',u'result':{u'routes':[{u'driverId':u'123',u'orders':[{u'id':u'123',u'scheduledAt':u'2014-12-05T08:04'},{u'id':u'456',u'scheduledAt':u'2014-12-05T08:27'}]}],u'unservedOrders':[]},u'success':True}# We can stop an already running optimization. If stopped previously no# exceptions will be raised, it will return None implying it was successful.optimo_api.stop('1234')
Package: optimod
OptimodOptimod is an innovative Python package designed to streamline Python project development. It automatically scans your Python project, identifies missing modules, installs them, and generates arequirements.txtfile. This tool is ideal for Python developers looking to automate the setup of their development environment and ensure consistency across different setups.FeaturesAutomatic Module Detection: Scans your Python project files to detect import statements and identifies any missing modules.Module Installation: Automatically installs the detected missing modules.Requirements File Creation: Generates arequirements.txtfile, listing all the installed modules with their respective versions, ensuring easy replication of the environment.InstallationTo install Optimod, simply run the following command in your terminal:pipinstalloptimodUsageUsing Optimod is simple and efficient. To run Optimod on your Python project, activate your virtual environment and specify the path to your project directory in the following manner:optimod/source/to/project
Package: optimoida
optimoida=========|forthebadge|📄 Overview---------------------------The **optimoida** is a command line application to **optimize imagefile** (``jpg``, ``png`` only) by `TinyPNG <https://tinypng.com/>`__DEMO~~~~~~~~~~~~~~~~~~~~~~|DEMO|⌛️ Prepare to use optimoida------------------------------------1. Get TinyPNG API Key~~~~~~~~~~~~~~~~~~~~~~You have to get TinyPNG API Key at`here <https://tinypng.com/developers>`__.2. Write optimoida.ini~~~~~~~~~~~~~~~~~~~~~~Please write API Key you got to ``optimoida.ini``. And move``optimoida.ini`` to ``~/.optimoida.ini``::$ mv optimoida.ini ~/.optimoida.ini✏️ Usage---------------::optimoida [PATH [PATH ...]]argument~~~~~~~~-  **PATH** - The image file or directory path. allowed multipleargument.📥 Installation--------------------------::$ pip install optimidaor::$ git clone git@github.com:alice1017/optimoida.git$ cd optimoida$ python setup.py build install👀 Contribution-------------------1. Forks on `Github <https://github.com/alice1017/optimoida>`__2. Find a bug? Send a pull request to get it merged and published... |forthebadge| image:: http://forthebadge.com/images/badges/made-with-python.svg:target: http://forthebadge.com.. |DEMO| image:: https://asciinema.org/a/177778.png:target: https://asciinema.org/a/177778
Package: optimove
This library allows you to quickly and easily use the Optimove Web API
via Python based onOptimove
documentationInstallationFrom PypipipinstalloptimoveFrom sourcepythonsetup.pyinstallQuick startCreate a new clientfromoptimove.clientimportClientclient=Client('username','password')Orfromoptimove.clientimportClientclient=Client()client.general.login('username','password')TestTests are available intests/folder.The fixture used for the tests are from the documentation provided by
Optimove.pythonsetup.pytestUsageRoadmapMissing features[ ] Add missing pagination parameters / Refacto pagination parameters
(maybe with a decorator)[ ] Test pagination[ ] Better authentication management[ ] Auto-reconnect as soon as possible (based on expire value)[ ] Use custom exceptions[ ] Prepare compatibility with Python 3.4+New API functions[ ]
[GetCustomerOneTimeActionsByCampaign](http://docs.optimove.com/api-usage-guide/#GetCustomerOneTimeActionsByCampaign)
– Returns a list of customers and the details associated with a
particular one-time campaign (i.e., via a manually-imported customer
list)[ ]
[SendTransactionalMail](http://docs.optimove.com/api-usage-guide/#SendTransactionalMail)
– Sends a transactional email to a list of recipients[ ]
[GetTransactionalTemplateMetrics](http://docs.optimove.com/api-usage-guide/#GetTransactionalTemplateMetrics)
– Returns post-execution metrics for a specific transactional mail
template over time[ ]
[GetTransactionalUserMetrics](http://docs.optimove.com/api-usage-guide/#GetTransactionalUserMetrics)
– Returns post-execution transactional email metrics for a specific
recipient[ ]
[GetCustomerProductsByCampaign](http://docs.optimove.com/api-usage-guide/#GetCustomerProductDetailsByCampaign)
- Returns an array of customer IDs and recommended Product IDs for
each customer targeted by a particular product recommendation
campaign[ ]
[GetCustomerProductDetailsByDate](http://docs.optimove.com/api-usage-guide/#GetCustomerProductDetailsByDate)
– Returns an array of customer IDs and recommended Product IDs for
each customer targeted by any product recommendation campaign on a
particular date[ ]
[GetCampaignInteractionCustomers](http://docs.optimove.com/api-usage-guide/#GetCampaignInteractionCustomers)
– Returns an array of Customer IDs and the Campaign ID and Template
ID for each customer who performed a particular interaction with a
campaign that was delivered on a particular date via a particular
channelHow to contributeTroubleshootingFor any issue pleasecreate a new
issueAbout
Package: optimparallel
optimparallel - A parallel version ofscipy.optimize.minimize(method='L-BFGS-B')Usingoptimparallel.minimize_parallel()can significantly reduce the
optimization time. For an objective function with an execution time
of more than 0.1 seconds and p parameters the optimization speed
increases by up to factor 1+p when no analytic gradient is specified
and 1+p processor cores with sufficient memory are available.A similar extension of the L-BFGS-B optimizer exists in the R packageoptimParallel:optimParallel on CRANR Journal articleInstallationTo install the package run:$pipinstalloptimparallelUsageReplacescipy.optimize.minimize(method='L-BFGS-B')byoptimparallel.minimize_parallel()to execute the minimization in parallel:fromoptimparallelimportminimize_parallelfromscipy.optimizeimportminimizeimportnumpyasnpimporttime# objective functiondeff(x,sleep_secs=0.5):print('fn')time.sleep(sleep_secs)returnsum((x-14)**2)# start valuex0=np.array([10,20])# minimize with parallel evaluation of 'fun' and# its approximate gradient.o1=minimize_parallel(fun=f,x0=x0,args=0.5)print(o1)# test against scipy.optimize.minimize()o2=minimize(fun=f,x0=x0,args=0.5,method='L-BFGS-B')print(all(np.isclose(o1.x,o2.x,atol=1e-10)),np.isclose(o1.fun,o2.fun,atol=1e-10),all(np.isclose(o1.jac,o2.jac,atol=1e-10)))The evaluatedxvalues,fun(x), andjac(x)can be returned:o1=minimize_parallel(fun=f,x0=x0,args=0.5,parallel={'loginfo':True})print(o1.loginfo)More examples are given inexample.pyand the Jupyter Notebookexample_extended.ipynb.Note for Windows users: It may be necessary to runminimize_parallel()in the main scope. Seeexample_windows_os.py.CitationWhen using this package please cite:Gerber, F.optimparallel - A parallel version of `scipy.optimize.minimize(method='L-BFGS-B'), 2020,http://doi.org/10.5281/zenodo.3888570Gerber, F., and Furrer, R.optimParallel: An R package providing a parallel version of the L-BFGS-B optimization method.The R Journal, 11(1):352–358, 2019.http://doi.org/10.32614/RJ-2019-030AuthorFlorian Gerber,flora.fauna.gerber@gmail.com,https://user.math.uzh.ch/gerber.ContributorLewis Blake,https://github.com/lewisblakeContributionsContributions via pull requests are welcome.https://github.com/florafauna/optimParallel-pythonhttps://pypi.org/project/optimparallel
Package: optimum-deepsparse
optimum-deepsparseAccelerated inference of 🤗 models on CPUs using theDeepSparse Inference Runtime.InstallOptimum DeepSparse is a fast-moving project, and you may want to install from source.pip install git+https://github.com/neuralmagic/optimum-deepsparse.gitInstalling in developer modeIf you are working on theoptimum-deepsparsecode then you should use an editable install by cloning and installingoptimumandoptimum-deepsparse:git clone https://github.com/huggingface/optimum
git clone https://github.com/neuralmagic/optimum-deepsparse
pip install -e optimum -e optimum-deepsparseNow whenever you change the code, you'll be able to run with those changes instantly.How to use it?To load a model and run inference with DeepSparse, you can just replace yourAutoModelForXxxclass with the correspondingDeepSparseModelForXxxclass.import requests
from PIL import Image- from transformers import AutoModelForImageClassification+ from optimum.deepsparse import DeepSparseModelForImageClassificationfrom transformers import AutoFeatureExtractor, pipeline

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

model_id = "microsoft/resnet-50"- model = AutoModelForImageClassification.from_pretrained(model_id)+ model = DeepSparseModelForImageClassification.from_pretrained(model_id, export=True, input_shapes="[1,3,224,224]")feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
cls_pipe = pipeline("image-classification", model=model, feature_extractor=feature_extractor)
outputs = cls_pipe(image)Supported TaskModel Class"image-classification"DeepSparseModelForImageClassification"text-classification"/"sentiment-analysis"DeepSparseModelForSequenceClassification"audio-classification"DeepSparseModelForAudioClassification"question-answering"DeepSparseModelForQuestionAnswering"image-segmentation"DeepSparseModelForSemanticSegmentationIf you find any issue while using those, please open an issue or a pull request.
Package: optimum-furiosa
optimum-furiosaAccelerated inference of 🤗 models using FuriosaAI NPU chips.Furiosa SDK setupA Furiosa SDK environment needs to be enabled to use this library. Please refer to Furiosa'sInstallationguide.InstallTo install the latest release of this package:pipinstalloptimum[furiosa]Optimum Furiosa is a fast-moving project, and you may want to install from source.pip install git+https://github.com/huggingface/optimum-furiosa.gitInstalling in developer modeIf you are working on theoptimum-furiosacode then you should use an editable install
by cloning and installingoptimumandoptimum-furiosa:git clone https://github.com/huggingface/optimum
git clone https://github.com/huggingface/optimum-furiosa
pip install -e optimum -e optimum-furiosaNow whenever you change the code, you'll be able to run with those changes instantly.How to use it?To load a model and run inference with Furiosa NPU, you can just replace yourAutoModelForXxxclass with the correspondingFuriosaAIModelForXxxclass.import requests
from PIL import Image- from transformers import AutoModelForImageClassification+ from optimum.furiosa import FuriosaAIModelForImageClassificationfrom transformers import AutoFeatureExtractor, pipeline

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

model_id = "microsoft/resnet-50"- model = AutoModelForImageClassification.from_pretrained(model_id)+ model = FuriosaAIModelForImageClassification.from_pretrained(model_id, export=True, input_shape_dict={"pixel_values": [1, 3, 224, 224]}, output_shape_dict={"logits": [1, 1000]},)feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
cls_pipe = pipeline("image-classification", model=model, feature_extractor=feature_extractor)
outputs = cls_pipe(image)If you find any issue while using those, please open an issue or a pull request.