Saravanan666/pypi-org huggingface dataset:
Package: infer-camembert
infer-camembertPython implementation for text classification inference with CamemBERT fine-tuned modelsThis is a simple Python implementation for the inference step of a fine-tuned text classification model based on Transformer'scamembert-basemodel and saved in HuggingFace™.Usage$pipinstallinfer-camembertFor a private model, you must provide your HuggingFace token, either as an environment variable or under the~/.huggingfacefolder:$HUGGINGFACE_TOKEN=<value>python3-minfercamembert--input=example.jsonl--dictionary=labels.json--model="your-public-or-private-model-on-huggingface"--threshold=0.1>results.jsonlInputs must be in the form of adictwith the keys being your unique IDs and the values the text on which to perform inference, eg.{"id1":"Very nice time spent in a gorgeous site.","id2":"Still a problem after three years: intolerable!!!!!!",}The same thing goes for the dictionary of labels where the keys should be your short custom labels and the value their corresponding long labels, eg.{"label0":"undefined","label1":"pleasure","label2":"fun","label3":"anger",}The results are presented as an array of predictions per input line, eg.[{"id":"id1","text":"Very nice time spent in a gorgeous site.","labels":["pleasure","fun"]},{"id":"id2","text":"Still a problem after three years: intolerable!!!!!!","labels":["anger"]}]Used as a Python library:frominfercamembertimportinfer,Labels,ModelParametersinputs={"id1":"Very nice time spent in a gorgeous site.","id2":"Still a problem after three years: intolerable!!!!!!",}labels=Labels({"label0":"undefined","label1":"pleasure","label2":"fun","label3":"anger",})params=ModelParameters("your-public-or-private-model-on-huggingface",0.1)outputs=infer(inputs,labels,params)LicenseThis module is distributed under a MIT license.See theLICENSEfile.© 2024 Cyril Dever. All rights reserved.
Package: infer-client
Python Infer ClientAbout Python Infer ClientPython Infer Clientis a python inference client library. It provides one interface to interact with many types of inference client as onnxruntime, tritonclient...Install$ pip install infer-clientUsageimport numpy as np

from infer_client.adapters.onnx import OnnxInferenceAdapter
from infer_client.inference import Inference


adapter = OnnxInferenceAdapter(model_name="resources/test_classify", version="1", limit_mem_gpu=-1)
infer_client_obj = Inference(adapter)

res = infer_client_obj.inference({"input": np.random.rand(1, 3, 224, 224)}, ["output"])ChangelogPlease seeCHANGELOGfor more information on what has changed recently.ContributingPlease seeCONTRIBUTINGfor details.Security VulnerabilitiesPlease reviewour security policyon how to report security vulnerabilities.CreditsDao Quang DuyAll ContributorsLicenseThe MIT License (MIT). Please seeLicense Filefor more information.ReferenceOnnxruntimeTriton Client
Package: infercnvpy
infercnvpy: Scanpy plugin to infer copy number variation (CNV) from single-cell transcriptomics dataInfercnv is a scalable python library to infer copy number variation (CNV) events from single cell transcriptomics data. It is heavliy inspired byInferCNV, but plays nicely withscanpyand is much more scalable.WARNING:This package is still experimental. The results have not been validated,
except in that they look similar, but not identical, to the results of InferCNV.We are happy about feedback and welcome contributions!Getting startedPlease refer to thedocumentation. In particular, theAPI documentation.InstallationYou need to have Python 3.9 or newer installed on your system. If you don't have
Python installed, we recommend installingMambaforge.There are several alternative options to install infercnvpy:Install the latest release ofinfercnvpyfromPyPI <https://pypi.org/project/infercnvpy/>_:pipinstallinfercnvpyInstall the latest development version:pipinstallgit+https://github.com/icbi-lab/infercnvpy.git@mainTo (optionally) run thecopyKATalgorithm, you need a working R installation
and thecopykatpackage installed. Usually, ifRis in yourPATH,rpy2automatically
detects your R installation. If you get an error message while importinginfercnvpy,
try setting theR_HOMEenvironment variable before importing infercnvpy:importosos.environ["R_HOME"]="/usr/lib/R"importinfercnvpyRelease notesSee thechangelog.ContactFor questions and help requests, you can reach out in thescverse discourse.
If you found a bug, please use theissue tracker.Citationn/a
Package: inferelator
Inferelator 3.0TheInferelator 3.0is a package for gene regulatory network inference that is based on regularized regression.
It is an update of theInferelator 2.0, which is an update of the originalInferelatorIt is maintained by the Bonneau lab in theSystems Biology group of the Flatiron Institute.This repository is the actively developed inferelator package for python. It works for both single-cell and bulk transcriptome experiments.
IncludesAMuSR(Castro et al 2019),
elements ofInfereCLaDR(Tchourine et al 2018),
and single-cell workflows(Jackson et al 2020).We recommend installing this package from PyPi usingpython -m pip install inferelator.
If running locally, also installjoblibbypython -m pip install joblibfor parallelization.
If running on a cluster, also installdaskbypython -m pip install dask[complete] dask_jobqueuefor dask-based parallelization.This package can also be installed from the github repository.
Clone theinferelator GitHubrepository and runpython setup.py install.Documentation is available athttps://inferelator.readthedocs.io, and
basic workflows forBacillus subtilisandSaccharomyces cerevisiaeare included with a tutorial.All current example data and scripts are available from Zenodo.
Package: inferelator-prior
inferelator-priorThis is a set of pipelines to create expression, dynamic response, and prior matrices for network inference.
They are designed to create data that is compatible with theinferelatorpackage.Usagepython -m inferelator_prior.network_from_motifs
usage: network_from_motifs.py --motif motif_PWM_file.meme
                              -f genome_fasta_file.fasta
                              -g genome_annotation_file.gtf
                              -o ~/output/path/prefix
                              --species {yeast,fly,mouse,human}
                              -b constraning_bed_file.bed
                              --cpu num_cores
                              --genes gene_list.txt
                              --tfs tf_list.txtThis requires a motif PWM database (-m PATH),
a genome to search (both sequence as a FASTA-f PATHand annotations-g PATH),
and an output prefix for several files (-o PATH).
In addition, default settings for a specific species can be set with (--species).
A BED file can be provided (-b PATH) based on some constraining experiment to restrict searching to
specific genomic areas.
This will use multiple cores to search for motifs and process the resulting data.
By default, all available processors will be used, but this can be overridden with--cpu N.
A list of genes (on e per line in a text file) can be provided to--genesand a list of tfs (one per line in a text file) can be provided to--tfs.
A network will be built for only these genes or TFs.The output from this script is an unsigned connectivity matrix (0, 1) connecting genes on rows to regulators on columns.
In addition, this produces an unfiltered score matrix (0, ) connecting genes on rows to regulators on columns.RequirementsIn addition to
python dependencies, this package also requiresSTAR,sra-tools,bedtools,samtools, andfimo.
Package: inference-logic
Inference LogicThe goal of this project is to explore how to write a minimal
set of features that allows a programmer to code declaratively
in native Python.The code is loosely based on Prolog, but rather than use the Prolog
Term/functor structure it uses JSON-like dicts and tuples.The success of this project is measured by the number of the99 problemssolved to keep the code focussed on delivering
features and not bike-sheddingThis code is experimental and incomplete. Do not use it in your
work or school! If you wish to use a serious, well tested
declarative tool in Python use the excellentpyDatalog.Free software: MIT licenseDocumentation:https://json-inference-logic.readthedocs.io.tldrfrominference_logicimportVariable,Rule,searchX,Y,Z,C,P=Variable.factory("X","Y","Z","C","P")db=[dict(parent="Abe",child="Homer"),dict(parent="Homer",child="Lisa"),dict(parent="Homer",child="Bart"),dict(parent="Homer",child="Maggie"),Rule(dict(ancestor=X,descendant=Z),dict(parent=X,child=Z)),Rule(dict(ancestor=X,descendant=Z),dict(parent=X,child=Y),dict(ancestor=Y,descendant=Z),),]query=dict(ancestor=P,descendant=C)results=search(db,query)assertnext(results)=={C:"Lisa",P:"Abe"}assertnext(results)=={C:"Bart",P:"Abe"}assertnext(results)=={C:"Maggie",P:"Abe"}assertnext(results)=={C:"Homer",P:"Abe"}assertnext(results)=={C:"Lisa",P:"Homer"}assertnext(results)=={C:"Bart",P:"Homer"}assertnext(results)=={C:"Maggie",P:"Homer"}CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.This was originally inspired bypy4funsome of this code here comes directly
from this project.Thank you tokclaureliefor helping to solve the fundamental
problem that had been bugging me for months!History0.1.0 (2020-10-05)First release on PyPI.
Package: inference-server
inference-serverDeploy your AI/ML model to Amazon SageMaker for Real-Time Inference and Batch Transform using your own Docker container image.:blue_book: Documentation:https://inference-server.readthedocs.ioInstallingpython-mpipinstallinference-serverDevelopingTo setup a scratch/development virtual environment (under.venv/), first installTox.
Then run:tox-edevTheinference-serverpackage is installed ineditable modeinside the.venv/environment.Run tests by simply callingtox.Install code quality Git hooks usingpre-commit install --install-hooks.Terms & ConditionsCopyright 2023 J.P. Morgan Chase & Co.Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.ContributingSeeCONTRIBUTING.md
Package: inference-tools
inference-toolsThis package provides a set of Python-based tools for Bayesian data analysis
which are simple to use, allowing them to applied quickly and easily.Inference-tools is not a framework for Bayesian modelling (e.g. likePyMC),
but instead provides tools to sample from user-defined models using MCMC, and to analyse and visualise
the sampling results.FeaturesImplementations of MCMC algorithms like Gibbs sampling and Hamiltonian Monte-Carlo for
sampling from user-defined posterior distributions.Density estimation and plotting tools for analysing and visualising inference results.Gaussian-process regression and optimisation.Gibbs SamplingHamiltonian Monte-CarloDensity estimationMatrix plottingHighest-density intervalsGP regressionInstallationinference-tools is available fromPyPI,
so can be easily installed usingpipas follows:pipinstallinference-toolsDocumentationFull documentation is available atinference-tools.readthedocs.io.
Package: inferex
Inferex CLIDeploy and manage your AI projects on Inferex infrastructure.See our online documentation for a tutorial.InstallationpipinstallinferexYou can invoke "inferex --help" for a list of commands. Each command may have
subcommands, which can be called with "--help" as well.Version 0.1.2:Usage:inferex[OPTIONS]COMMAND[ARGS]...InferexCLIisatoolthatenablesAIcompaniestorapidlydeploypipelines.Init,deploy,andmanageyourprojectswithInferex.Invoke"inferex --help"foralistofcommands.

Options:--versionShowtheversionandexit.--helpShowthismessageandexit.

Commands:deploy🚀Deployaproject.deployment🌎ManageInferexdeployments.pipelines📞Listpipelinesforadeployment.init✨Initializesanewproject.login🔑Fetchapikeyviausername&passwordauthentication.logs📃GetlogsofanInferexdeploymentprojects📁ManageInferexprojects.reset❌Deletesthetoken.jsonfilecreatedatlogin.CLI - Basic usageCreate or navigate to the project folder you wish to deploy. You may copy an
example project folder from the examples folder ("face_detection",
"sentiment_analysis", etc). Each example has inferex.yaml, pipeline.py, and
requirements.txt files.Run the "inferex login" command to log in with your inferex account
and save your token locally.Run "inferex deploy". This will create a tar archive of your project folder
and send it to the server for processing.That's it!inferex deploymentswill list your deployed projects and their URLs.
Package: infer-gender
Infer GenderInfers gender from the indian first name or full name.InstallationUsing pipYou can install using the pip package manager by runningpipinstallinfer-genderAlternatively, you could install the latest version directly from Github:pipinstallhttps://github.com/infer-gender/infer-gender/archive/master.zipUsing condaYou can install using the conda package manager by runningcondainstall-cconda-forgeinfer-genderFrom sourceDownload the source code by cloning the repository or by pressing 'Download ZIP' on this page.Install by navigating to the proper directory and running:pythonsetup.pyinstallUsagefrominfer_genderimportGenderPredictorgp=GenderPredictor()sample_names=['katrina kaif','narendra modi','naga budigam','james bond','samantha']predicted_genders=gp.predict_gender(sample_names)forname,pred_genderinzip(sample_names,predicted_genders):print('{}-->{}'.format(name,pred_gender))katrina kaif --> Female
narendra modi --> Male
naga budigam --> Male
james bond --> Male
samantha --> Female
Package: inferiot
Connect to Infer IoT Center using PythonFree software: MIT licenseInstallationpip install inferiotYou can also install the in-development version with:pip install https://github.com/prafulmaka/inferiotAuthorsInferIoT was written by Praful Maka, Mohammad Sameer Beig, John Groboske, Otu Ekanem and Daniel Gallagher.
Package: inferio-x
InferIO-XInference I/O ExtendedInstallation$pipinstallinferio-x==0.1.0UsagefrompydanticimportBaseModelfrominferioimportService,Endpoint,MethodclassPredictPayload(BaseModel):name:strage:intclassPredictor:def__init__(self,model_f:str)->None:self.model_f=model_fdefpredict(self,payload:PredictPayload)->dict:...if__name__=="__main__":predictor=Predictor(model_f="model.pth")service=Service(endpoints=[Endpoint(path="/",handler=predictor.predict,methods=[Method.POST]),])service.run()TestsTo run the tests, use the following command:$poetryrunpytest
Package: inferless
Python Client to call Inferless APIInstallation$pipinstallinferlessUsageThis client can be used to call Inferless API from your python code. It supports both synchronous and asynchronous calls.ConstantsFetch the URL and API_KEY from the Inferless consolehttps://console-dev.inferless.com/URL="<url>"API_KEY="<api_key>"data={"inputs":[{"name":"prompt","shape":[1],"datatype":"BYTES","data":["Once upon a time"]}]}Synchrounous callAn example to call Inferless API synchronouslyimportinferlessimportdatetimedefmain():t1=datetime.datetime.now()data=inferless.call(URL,API_KEY,data)t2=datetime.datetime.now()print(f"time taken:{t2-t1}")main()Outputtime taken: 0:00:05.218835For a particular url, the synchronous call took approximately 5 seconds to complete.Async call with callbackThis library also supports async calls to Inferless API. The following example shows how to call Inferless API in background with a callback function.importinferlessimportdatetimedefcallback_func(e,response):# e is the error object# response is the response objectwithopen("response.json","w")asf:f.write(json.dumps(response))t1=datetime.now()inferless.call_async(URL,KEY,DATA,callback_func)t2=datetime.now()print(t2-t1)Outputtime taken: 0:00:00.000141It can be seen that the program exits without waiting for the response. The response is written to a file by the callback function after it is received.
Package: infer-license
infer-licenseHeavily inspired byhttps://github.com/sol/infer-license/this will provide an
API to guess what license a given file represents.>>> from infer_license import guess_file
>>> print(guess_file("LICENSE"))
License(name='MIT License', shortname='MIT', trove_classifier='License :: OSI Approved :: MIT License')
>>> guess_file("setup.py")
NoneThere's also a handyinfer_licensescript that takes a filename.$ infer_license LICENSE
LICENSE: MITLicenseinfer-license is copyrightTim Hatch, and licensed under
the MIT license.  I am providing code in this repository to you under an open
source license.  This is my personal repository; the license you receive to
my code is from me and not from my employer. See theLICENSEfile for details.
Package: inferlo
InferLOPython library for Inference, Learning and Optimization with Graphical Models.InstallingLibrary can be installed using pip:pip install inferloDocumentationDocumentation is available athttps://inferlo.github.io/inferlo.ContributingYou can contribute to the library by creating pull requests.
Please follow theseguideleines.AuthorsMisha ChertkovDmytro FedoriakaMikhail Krechetov
Package: inferno
Unit Tests----------To run only the unit tests, and exclude the integration tests:diana@ubuntu:~/workspace/inferno$ nosetests --exclude=integrationTo run both the unit and integration tests:diana@ubuntu:~/workspace/inferno$ nosetestsIf the tests were successful, you should see something like the following.----------------------------------------------------------------------Ran 100 tests in 1.000sOKThe integration test suite assumes you have a running disco cluster.diana@ubuntu:~$ disco startMaster ubuntu:8989 startedAnd that your inferno settings (~/.inferno or /etc/inferno/settings.yaml)point to that disco master.diana@ubuntu:~$ cat ~/.infernoserver: disco://localhost
Package: infernum
InfernumInfernum is a lightweight Android native library emulation framework based onUnicorn. It is mainly used to execute the encryption algorithm, so it doesn't provide JNI or file system support. It supports arch ARM and ARM64.RequirementsPython 3.7+Unicorn 2.0.0+Installation$ pip install infernumUsageLoad modules and call functions.frominfernumimportInfernumfrominfernum.constimportARCH_ARM64# Initialize emulatoremulator=Infernum(ARCH_ARM64)# Load modulesemulator.load_module("lib64/libz.so")# Construct argumentsdata=b"infernum"v1=emulator.create_buffer(len(data))v2=len(data)emulator.write_bytes(v1,data)# Call function by symbolemulator.call_symbol("crc32",0,v1,v2)# Call function by addresssymbol=emulator.find_symbol("crc32")emulator.call_address(symbol.address,0,v1,v2)Emulate arch ARM.frominfernumimportInfernumfrominfernum.constimportARCH_ARMemulator=Infernum(ARCH_ARM)Read/Write data.# Create bufferv1=emulator.create_buffer(64)v2=emulator.create_string("infernum")# Write dataemulator.write_int(v1,1)emulator.write_bytes(v1,b"infernum")emulator.write_string(v2,"infernum")# Read dataemulator.read_int(v1)emulator.read_bytes(v1,8)emulator.read_string(v2)Hook instructions.defhook_code(uc,address,size,user_data):emu=user_data["emulator"]symbol=emulator.find_symbol("zlibVersion")emulator.add_hook(symbol.address,hook_code)Trace instructions.# Trace all instructionsemulator=Infernum(ARCH_ARM64,trace_inst=True)# Trace instructions in this moduleemulator.load_module("lib64/libz.so",trace_inst=True)Execute initialization functions in section.init_array.emulator.load_module("lib64/libsample1.so",exec_init_array=True)
Package: inferout
py-inferoutDistributed Scale Out Framework for ML model serving/inferencingThis Project is in development, Not intended for production useInstallIt is available in PyPI$pipinstallinferoutif you don't have pip command use -m pip$python-mpipinstallinferoutUsage (Quick example)First of all we need an ml model to be able to serve. It can be of any kind, any framework (pytorch, tensorflow, rasa, etc.) as long as it can be loaded in python 3.7+We need to implement two interfacesserving_engine - this is to teach inferout how to load, serve (infer), and unload the models of a specific kind/usecase.examplestorage_engine - this is to teach inferout how to get/download/locate the models.exampleGet redis server ready. Yes we need redis to use inferout. inferout uses redis to store metadata and pass messages between diferent componets and nodes.https://redis.io/downloadBootstrap cluster - this is create minimum metadata required to run cluster all you need is a cluster name and a redis URL$inferoutbootstrap_cluster--cluster-namemy_rasa_nlu--redis-urlredis:///10Launch the worker$inferoutworker--cluster-namemy_rasa_nlu--redis-urlredis:///10--storage-engines"my_rasa_nlu.storage_engine"--serving-engines"my_rasa_nlu.serving_engine"Can run multiple workers for single cluster. to run multiple workers in single system (for development and to test) we can use diferent port numbers for each worker. try "inferout worker -h" for more detailsWhat we need to make sure is worker availabily and connectivity(serving api port) between nodes else(replication of models and distributing to workers, smartly routing the inferencing requests) will be taken care by inferout framework.Create namespace$curl-XPUTlocalhost:9500/namespaces/default-d'{"settings":{"storage_engine":"my_rasa_nlu.storage_engine","serving_engine":"my_rasa_nlu.serving_engine"}}'Create model$curl-XPUTlocalhost:9500/namespaces/default/models/mymodel1-d'{"parameters":{"path":"nlu-20210726-153112.tar.gz"}}'wondering how to get this model file and where to place it for testing?
install open source rasa using pip.https://rasa.com/docs/rasa/installation$pip3installrasainit rasa project$rasainittrain your nlu model$rasatrainnlumake required directories and copy the model$mkdir/tmp/nlu_models$mkdir/temp_nlu_models$lsmodels$cpmodels/nlu-*.tar.gz/tmp/nlu_models/Query your model (inference)$curl-XPOSTlocalhost:9510/default/mymodel1-d'{"input_data":{"query":"Hi"}}'Did you find any change in port number? Yes for namespace and model creation we used 9500 but now we used 9510
inferout worker provides 2 API servicesmanagement API - create/update/delete/inspect models, namespaces, workersinferencing API - to quiry modelsWhat next?
Explore other management APIs, for now just find API endpoints insource code
Package: infer-parser
infer-parserinfer-parser is a Python library for making shell argument parsers from type hints.Installpipinstallinfer-parserExampleimporttypingastfrominfer_parserimportmake_parserparse=make_parser(t.Optional[int])assertparse(["5"])==5assertparse(["-11"])==-11assertparse([""])isNoneassertparse(["None"])isNonetry:parse(["12.13"])exceptValueError:print("not an Optional[int]")parse_tuple=make_parser(tuple[float,...])assertparse_tuple(["1.5"])==(1.5,)assertparse_tuple(["0.0","4.2","-1"])==(0.0,4.2,-1.0)assertparse_tuple([])==()try:parse(["Hello, world!"])exceptValueError:print("not a tuple[float, ...]")Limitationsinfer-parser cannot always infer a parser.importtypingastfrominfer_parserimportmake_parsertry:make_parser(t.Callable[...,int])exceptTypeError:print("not supported")LicenseMIT.