Saravanan666/pypi-org huggingface dataset:
Package: neurst
The primary motivation of NeurST is to facilitate NLP researchers to get started on end-to-end speech translation (ST) and build advanced neural machine translation (NMT) models.Seeherefor a full list of NeurST examples. And we present recent progress of end-to-end ST technology athttps://st-benchmark.github.io/.NeurST is based on TensorFlow2 and we are working on the pytorch version.NeurST NewsAug 16, 2021: Release of models and results forIWSLT 2021 offline ST and simultaneous translation task.June 15, 2021: Integration ofLightSeqfor training speedup, see theexperimental branch.March 28, 2021: The v0.1.1 release includes the instructions of weight pruning and quantization aware training for transformer models, and several more features. See therelease notefor more details.Dec. 25, 2020: The v0.1.0 release includes the overall design of the code structure and recipes for training end-to-end ST models. See therelease notefor more details.HighlightsProduction ready: The model trained by NeurST can be directly exported as TF savedmodel format and use TensorFlow-serving. There is no gap between the research model and production model. Additionally, one can useLightSeqfor NeurST model serving with a much lower latency.Light weight: NeurST is designed specifically for end-to-end ST and NMT models, with clean and simple code. It has no dependency on Kaldi, which simplifies installation and usage.Extensibility and scalability: NeurST has the careful design for extensibility and scalability. It allows users to customizeModel,Task,Datasetetc. and combine each other.High computation efficiency: NeurST has high computation efficiency and can be further optimized by enabling mixed-precision and XLA. Fast distributed training usingByteps/Horovodis also supported for large-scale scenarios.Reliable and reproducible benchmarks: NeurST reports strong baselines with well-designed hyper-parameters on several benchmark datasets (MT&ST). It provides a series of recipes to reproduce them.Pretrained Models & Performance BenchmarksNeurST provides reference implementations of various models and benchmarks. Please seeexamplesfor model links and NeurST benchmark on different datasets.Text TranslationTransformer on WMT14 en->deSpeech-to-Text Translationlibri-transMuST-CRequirements and InstallationPython version >= 3.6TensorFlow >= 2.3.0Install NeurST from source:git clone https://github.com/bytedance/neurst.git
cd neurst/
pip3 install -e .If there exists ImportError during running, manually install the required packages at that time.Citation@InProceedings{zhao2021neurst,
  author       = {Chengqi Zhao and Mingxuan Wang and Qianqian Dong and Rong Ye and Lei Li},
  booktitle    = {the 59th Annual Meeting of the Association for Computational Linguistics (ACL): System Demonstrations},
  title        = {{NeurST}: Neural Speech Translation Toolkit},
  year         = {2021},
  month        = aug,
}ContactAny questions or suggestions, please feel free to contact us:zhaochengqi.d@bytedance.com,wangmingxuan.89@bytedance.com.AcknowledgementWe thank Bairen Yi, Zherui Liu, Yulu Jia, Yibo Zhu, Jiaze Chen, Jiangtao Feng, Zewei Sun for their kind help.
Package: neurtu
Simple performance measurement toolneurtu is a Python package providing a common interface for multi-metric benchmarks
(including time and memory measurements). It can can be used to estimate time
and space complexity of algorithms, while pandas integration
allows quick analysis and visualization of the results.neurtumeans “to measure / evaluate” in Basque language.See thedocumentationfor more details.Installationneurtu requires Python 2.7 or 3.4+, it can be installed with,pip install neurtupandas >=0.20is an optional (but highly recommended) dependency.QuickstartTo illustrate neurtu usage, will will benchmark array sorting in numpy. First, we will
generator of cases,importnumpyasnpimportneurtudefcases()rng=np.random.RandomState(42)forNin[1000,10000,100000]:X=rng.rand(N)tags={'N':N}yieldneurtu.delayed(X,tags=tags).sort()that yields a sequence of delayed calculations, each tagged with the parameters defining individual runs.We can evaluate the run time with,>>>df=neurtu.timeit(cases())>>>print(df)wall_timeN10000.000014100000.0001341000000.001474which will internally usetimeitmodule with a sufficient number of evaluation to work around the timer precision
limitations (similarly to IPython’s%timeit). It will also display a progress bar for long running benchmarks,
and return the results as apandas.DataFrame(if pandas is installed).By default, all evaluations are run withrepeat=1. If more statistical confidence is required, this value can
be increased,>>>neurtu.timeit(cases(),repeat=3)wall_timemeanmaxstdN10000.0000120.0000140.000002100000.0001160.0001490.0000291000000.0013230.0017140.000339In this case we will get a frame with apandas.MultiIndexfor
columns, where the first level represents the metric name (wall_time) and the second – the aggregation method.
By defaultneurtu.timeitis called withaggregate=['mean','max', 'std']methods, as supported
by thepandas aggregation API. To disable,
aggregation and obtains timings for individual runs, useaggregate=False.
Seeneurtu.timeit documentationfor more details.To evaluate the peak memory usage, one can use theneurtu.memitfunction with the same API,>>>neurtu.memit(cases(),repeat=3)peak_memorymeanmaxstdN100000.00.00.01000000.00.00.010000000.00.00.0More generallyneurtu.Benchmarksupports a wide number of evaluation metrics,>>>bench=neurtu.Benchmark(wall_time=True,cpu_time=True,peak_memory=True)>>>bench(cases())cpu_timepeak_memorywall_timeN100000.0001000.00.0001421000000.0011490.00.00168010000000.0136770.00.018347includingpsutil process metrics.For more information see thedocumentationandexamples.Licenseneurtu is released under the 3-clause BSD license.
Package: neutompy
NeuTomPy toolboxNeuTomPy toolboxis a Python package for tomographic data processing and reconstruction.
Such toolbox includes pre-processing algorithms, artifacts removal and a wide range of iterative
reconstruction methods as well as the Filtered Back Projection algorithm.
The NeuTomPy toolbox was conceived primarily for Neutron Tomography and developed to support
the need of users and researchers to compare state-of-the-art reconstruction methods and choose the optimal data-processing workflow for their data.FeaturesReaders and writers for TIFF and FITS files and stack of imagesData normalization with dose correction, correction of the rotation axis tilt, ring-filters, outlier removals, beam-hardening correctionA wide range of reconstruction algorithms powered byASTRA toolbox: FBP, SIRT, SART, ART, CGLS, NN-FBP, MR-FBPImage quality assessment with several metricsInstallationNeuTomPy toolbox supportsLinux,WindowsandMac OS64-bit operating systems.First of all, install acondapython environment withPython 3.5 or 3.6.It is required to install some dependencies, hence run the following inside a conda environment:conda install -c simpleitk simpleitk
conda install -c astra-toolbox astra-toolbox
conda install -c conda-forge ipython numpy numexpr matplotlib astropy tifffile opencv scikit-image read-roi mkl_fft scipy six tqdm pywaveletsThen install NeuTomPy toolbox viapip:pip install neutompyNB: If a segmentation fault occurs when importing NeuTomPy, install PyQt5 viapip:pip install PyQt5UpdateTo update a NeuTomPy installation to the latest version run:pip install neutompy --upgradeDocumentationComplete documentation can be found on Read the Docs:https://neutompy-toolbox.readthedocs.io.Tutorials and code examples of typical usage can be found in the folderexamples.A sample dataset for testing purpose can be foundhere. This dataset includes neutron radiographs of a phantom sample acquired at the IMAT beamline, ISIS neutron spallation source, UK.ReferenceIf you use the NeuTomPy toolbox for your research, please cite the following paper:D. Micieli, T. Minniti, G. Gorini, “NeuTomPy toolbox, a Python package for tomographic data processing and reconstruction”, SoftwareX, Volume 9 (2019), pp. 260-264,https://doi.org/10.1016/j.softx.2019.01.005.LicenseThe project is licensed under theGPLv3license.ContactIf you want to contact us for any reasons, please send an email to:neutompy@gmail.com
Package: neutralise-link
neturalise-linkWhat are the objectives?Remove trackersRemove referrersIdentify malicious intentVerify URL validityImprove URL load speedsGetting startedPrerequisitesHave python3 installed (e.g. using anaconda/homebrew)Have build installed -python3 -m pip install --upgrade buildBuilding the packageNavigate to root directory of the project and run:python3 -m buildInstall the package found inneutralise-link/dist/in your repo usingpip3 installfollowed by the relative path of the.tar.gzpackage file located in the project.How does it work?Having importedneutralise-linkyou may use theneutralisefunction which takes a URL string as the argument.By default, the function will returnNonein two cases:The link is invalidThe link is deemed maliciousYou may override the 2nd case by calling the function with the optional parameter,safe=false.Example Codefromneutralise_linkimportneutralisedefmain(url:str)->str:"""Validate user URL input for storing."""url=neutralise(url=url,safe=True)ifnoturl:print("URL is malformed or malicious.")print("URL is safe")
Package: neutralNEMO
neutralNEMO (v0.9.4, early release)Calculate neutral surfaces for NEMO (Madec et al., 2019) model runs using neutralNEMO!This package makes it easier to use the highly-optimized algorithms of theneutraloceanpackage (Stanley et al., 2021) with model outputs from NEMO.The package contains routines to correctly load temperature and salinity data alongside the necessary horizontal and vertical grid information.A steamlined routine is also included to calculate an approximately neutral density surface which is fixed to a specific location in time and space. Additional options to calculate the Veronis density and potential density surfaces are included.InstallationneutralNEMO can be installed using conda:conda install -c conda-forge neutralnemoor alternatively, neutralNEMO can be installed through pippip install neutralNEMOThe future of neutralNEMOThis is currently an early release of neutralNEMO and I am keen to here how others get on with the package when they use it on their model runs.  If it works well for you, please spread the word and let me know as it will make my day! If you do encounter any problems, please contact me or raise an issue, I will try to help in any way I can.DocumentationDocumentation and examples can be found athttps://neutralnemo.readthedocs.io
Package: neutralocean
neutraloceanCalculate neutral surfaces in the ocean, using Python.The major task of this software is to calculateomega-surfaces, following the algorithm ofStanley et al. (2021).  Omega surfaces are highly accurate approximately neutral surfaces that work by iteratively reducing the neutrality error.There are also routines to calculate potential density surfaces, specific volume anomaly surfaces, neutral trajectories, the Veronis density label, and measures of neutrality error.Topobaric surfaces(Stanley, 2019a)in their modified form(Stanley 2019b)are the most accurate approximately neutral surfaces that posses an exact geostrophic streamfunction (furnishing an Ertel potential vorticity with no baroclinic production term).  Software to compute topobaric surfaces is significantly more complicated and is currently only available in the original MATLABneutral-surfacestoolbox.How to cite?If you use this software, the most appropriate paper to cite isStanley et al. (2021).InstallationSimply execute either$ pip install neutraloceanif you use pip, or$ conda install -c conda-forge neutraloceanif you use conda.DocumentationSeehttps://neutralocean.readthedocs.org
Package: neutrino
NeutrinoA high performance library for storing scalar timeseries data in redis.InstallationTo install, simply type:$ sudo pip install neutrinoDesignTime series are stored as scalar values in bins with a fixed time step.
These bins are stored in chunks, and each chunk is stored under a single
redis key.Instead of specifying these parameters for each time series individually,
the parameters are specified for apattern, which is a regular
expression.  When time series data is later added, the name of each time
series is matched to these patterns, and for each match a time series is
added.For example, the rule>>> ts.add_rule(r'spam.*','f',1,10,100,'avg')will match any key starting with ‘spam’. Any key matching this pattern
will be stored in a time series which will average multiple values in a
single bin, with a time step of 1 second, a chunk size of 10, and chunks
will expire after 100 seconds.
Package: neutrino-api-client-requests
NeutrinoAPI Python Requests SDKPython client using the requests HTTP clientThe official API client and SDK built byNeutrinoAPIFeaturePlatform Version>= 3HTTP LibraryRequestsJSON LibraryNativeHTTP/2NoHTTP/3NoCodeGen Version4.6.13Getting startedFirst you will need a user ID and API key pair:SignUpTo Initializefromneutrino_api.neutrino_api_clientimport*client=NeutrinoAPIClient("<USER_ID>","<API_KEY>")Running Examples$python3-mneutrino_api.examples.ip_infoYou can find examples of all APIs inneutrino_api/examples/For SupportContact us
Package: neutrino-engine
Neutrino EngineNeutrino is a deep learning library for optimizing and accelerating deep neural networks to make them faster, smaller and more energy-efficient. Neural network designers can specify a variety of pre-trained models, datasets and target computation constraints and ask the engine to optimize the network. High-level APIs are provided to make the optimization process easy and transparent to the user. Neutrino can be biased to concentrate on compression (relative to disk size taken by the model) or latency (forward call’s execution time) optimization.Community ReleaseOur community edition provides all the important features to experience the power and usability of model optimization with Neutrino. With the community version, engineers and researchers can verify the seamless integration of Neutrino into standard AI processes, test compatibility with existing model development and explore the benefits of optimization to various products. Feelfreeto use it as you please! The aim of the community edition is multifold, with examples such as:Provide hands-on experience with automated model architecture optimization and see first-hand the possibilities withDeeplite NeutrinoCompare and complement the results obtained usingDeeplite Neutrinowith other open-source and industry model architecture optimization frameworksExport an optimized model to test integration with endpoint applicationsVerify the integration ofDeeplite Neutrinowithin industry and production pipelinesUtilizeDeeplite Neutrinoto accelerate academic research, expedite results and share your achievements in research papersHave fun! Users can play around withDeeplite Neutrinoand enjoy the advantages of model architecture optimization in various use-casesFor detailed comparison of features on our community and production editions, refer to thedocumentationGet Your Free Community LicenseThe community license key is completely free-to-obtain and free-to-use.Fill out this simple formto obtain the license key for the Community Version of Deeplite Neutrino™.InstallationUsepipto installneutrino-enginefrom PyPi repository. We recommend creating a new python virtualenv, then pip install using the following commands.pip install --upgrade pip
    pip install neutrino-engine
    pip install neutrino-torchFor other methods of installation and detailed instructions, refer to thedocumentation
Package: neutrino-torch
Neutrino TorchNeutrino is a deep learning library for optimizing and accelerating deep neural networks to make them faster, smaller and more energy-efficient. Neural network designers can specify a variety of pre-trained models, datasets and target computation constraints and ask the engine to optimize the network. High-level APIs are provided to make the optimization process easy and transparent to the user. Neutrino can be biased to concentrate on compression (relative to disk size taken by the model) or latency (forward call’s execution time) optimization.Community ReleaseOur community edition provides all the important features to experience the power and usability of model optimization with Neutrino. With the community version, engineers and researchers can verify the seamless integration of Neutrino into standard AI processes, test compatibility with existing model development and explore the benefits of optimization to various products. Feelfreeto use it as you please! The aim of the community edition is multifold, with examples such as:Provide hands-on experience with automated model architecture optimization and see first-hand the possibilities withDeeplite NeutrinoCompare and complement the results obtained usingDeeplite Neutrinowith other open-source and industry model architecture optimization frameworksExport an optimized model to test integration with endpoint applicationsVerify the integration ofDeeplite Neutrinowithin industry and production pipelinesUtilizeDeeplite Neutrinoto accelerate academic research, expedite results and share your achievements in research papersHave fun! Users can play around withDeeplite Neutrinoand enjoy the advantages of model architecture optimization in various use-casesFor detailed comparison of features on our community and production editions, refer to thedocumentationGet Your Free Community LicenseThe community license key is completely free-to-obtain and free-to-use.Fill out this simple formto obtain the license key for the Community Version of Deeplite Neutrino™.InstallationUsepipto installneutrino-engineandneutrino-torchfrom PyPi repository. We recommend creating a new python virtualenv, then pip install using the following commands.pip install --upgrade pip
    pip install neutrino-engine
    pip install neutrino-torchFor other methods of installation and detailed instructions, refer to thedocumentation
Package: neutron
Neutron is an OpenStack project to provide “network connectivity as a service”
between interface devices (e.g., vNICs) managed by other OpenStack services
(e.g., Nova).To learn more about neutron:Documentation:https://docs.openstack.org/neutron/latest/Features:https://specs.openstack.org/openstack/neutron-specsDefects:https://launchpad.net/neutronRelease notes:https://docs.openstack.org/releasenotes/neutron/index.htmlSource:https://opendev.org/openstack/neutronIf you would like to contribute to Neutron, please read the fileCONTRIBUTING.rstor see the Neutron contributor guide:https://docs.openstack.org/neutron/latest/contributor/contributing.htmlGet in touch viaemail. Use
[Neutron] in your subject.
Package: neutron-arista-ccf-lldp
LLDP Agent for Big Switch Networks integration.This custom LLDP agent is used to send LLDPs on interfaces connected to
Big Cloud Fabric (BCF). In environments with os-net-config installed, it reads
config from os-net-config to automagically identify and send LLDPs.For all other purposes, Big Switch Openstack Installer (BOSI) configures the
service file based on environment info.
Package: neutron-bsn-lldp
LLDP Agent for Big Switch Networks integration.This the python27 version that is used for RHOSP13 and OpenStack Queens branch.For Earlier version, the LLDP Agent is inside the plugin package.Python3 environment is not supported by this version.This custom LLDP agent is used to send LLDPs on interfaces connected to
Big Cloud Fabric (BCF). In environments with os-net-config installed, it reads
config from os-net-config to automagically identify and send LLDPs.For all other purposes, Big Switch Openstack Installer (BOSI) configures the
service file based on environment info.
Package: neutron-cinema
promptPrompt: Probability-Conserved Cross Section Biasing Monte Carlo Particle Transport System.Introduction of system can be found atarXiv.Installing on Linux(Ubuntu) from sourceTo build Prompt, several prerequisites are needed.sudo apt install g++ cmake python3-dev python3-pip python3-venvClone the source and compile it asgit clone https://gitlab.com/cinema-developers/prompt.git
cd prompt
. env.sh
cimbuildInstalling on Linux with pippip install neutron-cinemaRuning Prompt simulationsAfter installation, GDML formatted simulation input files can be launched by Prompt asprompt [-g file] [-s seed] [-n number] [-v]OptionDefultDescription-gSet GDML input file.-s4096Set the seed for the random generator-n100Set the number of primary neutron events-vThe flag to activate the visualisationExample of visualising the geometry defined in the total_scattering.gdml:prompt -g total_scattering.gdml -vThe simulation will produce histogroms in the MCPL format only in the production run, if any scorers are specified. To run the simulation with 1e6 neutronsprompt -g total_scattering.gdml -n 1e6After the execution, seven histogrom files and accosiated python analysis template scripts will be generated. The reults can be plotted aspython ScorerDeltaMomentum_PofQ_HW_view.pyThere are many input examples are available in the gdml sub-directory.Connect with us:cinema-users@outlook.com
Package: neutron-dynamic-routing
Team and repository tagsThis package contains neutron-dynamic-routing code which depends upon neutron
and it’s related libraries to run.Project ResourcesThe homepage for Neutron is:https://launchpad.net/neutron.  Use this
site for asking for help, and filing bugs. We use a single launchpad
page for all Neutron projects.Code is available on opendev.org at:https://opendev.org/openstack/neutron-dynamic-routingRefer to Neutron documentation for more information:Neutron README.rstRelease notes for the project can be found at:https://docs.openstack.org/releasenotes/neutron-dynamic-routing/
Package: neutron-fwaas
Welcome!This package contains the code for the Neutron Firewall as a Service
(FWaaS) service. This package requires Neutron to run.External Resources:The homepage for Neutron is:https://launchpad.net/neutron.  Use this
site for asking for help, and filing bugs. We use a single Launchpad
page for all Neutron projects.Code is available on git.openstack.org at:
<https://opendev.org/openstack/neutron-fwaas>.Please refer to Neutron documentation for more information:Neutron README.rstGet release notes:Neutron FWaaS Release Notes
Package: neutron-fwaas-dashboard
OpenStack Dashboard panels for Neutron FWaaSDocumentation:https://docs.openstack.org/neutron-fwaas-dashboard/latest/Source:https://opendev.org/openstack/neutron-fwaas-dashboardBugs:https://bugs.launchpad.net/neutron-fwaas-dashboardRelease Notes:https://docs.openstack.org/releasenotes/neutron-fwaas-dashboard/
Package: neutron-lbaas
WarningNeutron-lbaas is now deprecated. Please see the FAQ:https://wiki.openstack.org/wiki/Neutron/LBaaS/DeprecationWelcome!This package contains the code for the Neutron Load Balancer as a
Service (LBaaS) service. This includes third-party drivers. This package
requires Neutron to run.External Resources:The homepage for Octavia LBaaS is:https://launchpad.net/octavia.  Use this
site for asking for help, and filing bugs.Code is available on git.openstack.org at:
<http://git.openstack.org/cgit/openstack/neutron-lbaas>.Please refer to Neutron documentation for more information:Neutron README.rstRelease notes for the project can be found at:
<https://docs.openstack.org/releasenotes/neutron-lbaas/index.html>.