Saravanan666/pypi-org huggingface dataset:
Package: hsettings
DescriptionThis package can load settings from multiple sources and hybrid them into one dict-like object.InstallationFor Python 3.5+pipinstallhsettingsUsageLoad settingsLoad settings from dict.fromhsettings.loadersimportDictLoaderdata1={'k1':'v1','k2':'v2','k3':1,'k4':'1'}settings=DictLoader.load(data1)//typecastssettings=DictLoader.load(data1,casts={'k3':str,'k4':int})assertsettings.get('k3')=='1'assertsettings.get('k4')==1//keysincludessettings=DictLoader.load(data1,includes=['k1','k2'])assertsettings.as_dict()=={'k1':'v1','k2':'v2'}//keysexcludessettings=DictLoader.load(data1,excludes=['k3','k4'])assertsettings.as_dict()=={'k1':'v1','k2':'v2'}//keysincludesandexcludessettings=DictLoader.load(data1,includes=['k1','k2'],excludes=['k2','k3'])assertsettings.as_dict()=={'k1':'v1'}//mapkeystoinnerkeyssettings=DictLoader.load(data1,key_mappings={'k2':'k2.k2-1','k3':'k2.k2-2'})assertsettings.as_dict()=={'k1':'v1','k2':{'k2-1':'v2','k2-2':1},'k4':'1'}//mapkeystoinnerkeysandonlycontainsthesekeys//thisisusefultoloadusedefinedenvssettings=DictLoader.load(data1,key_mappings={'k2':'k2.k2-1','k3':'k2.k2-2'},only_key_mappings_includes=True)assertsettings.as_dict()=={'k2':{'k2-1':'v2','k2-2':1}}Load settings from json file.fromhsettings.loadersimportJsonLoadersettings=JsonLoader.load(json_file)Load settings from yaml file.fromhsettings.loadersimportYamlLoadersettings=YamlLoader.load(yaml_file)Load settings from environment and/or env file.
Support casts, env_to_key_mapping, includes, excludes and only_key_mappings_includes as DictLoader.
Use env_to_key_mapping and only_key_mappings_includes parameters to only get specific env settings.//loadfromenvironmentfromhsettings.loadersimportEnvLoadersettings=EnvLoader.load()//loadfromenvironmentandenvfilefromhsettings.loadersimportEnvLoadersettings=EnvLoader.load(env_file)Use settingsdata1={'k1':'v1','k2':{'k2-1':'v2-1','k2-2':'v2-2'},'k3':['v3-1','v3-2'],'k4':1,'k5':[0,1,2],'k6':{'k6-1':'','k6-2':None,'k6-3':1.2},'k7':{'k7-1':{'k7-1-1':7}}}settings=Settings(data1)//getsettingsbygetmethodor[]print(settings.get('k1'))//outputv1print(settings['k1'])//outputv1//usedot(.)togetinnervalueprint(settings.get('k2.k2-1'))//outputv2-1print(settings['k2.k2-1'])//outputv2-1//setdefaultvalueifnotset//noteNoneisnotequaltonotsetprint(settings.get('not_set','yes'))//outputyes//getwholesettingsasdictprint(settings.as_dict())//cloneanewsettingssettings2=settings.clone()print(settings2==settings)//outputTrueprint(settings2issettings)//outputFalse//setsettingsbysetmethodor[]settings.set('k3','v3')print(settings['k3])//outputv3settings['k3']='vv3'print(settings['k3'])//outputvv3//mergesettings,keysconflictwillbeoverrideddata2={'k1':'kk1','k2':{'k2-1':'kk2-1'},'k5':[3,4],'k6':{'k6-2':'kk6-2','k6-4':2.4}}settings3=settings.clone()settings3.merge(data2)print(settings3['k2.k2-1'])print(settings3['k2.k2-1'])//outputkk2-1//outputv2-1More examples are in tests.TestRun unit testpytest
Package: hs-formation
FormationA generic functional middleware infrastructure for Python.Take a look:fromdatetime.datetimeimportnowfromhs_formationimportwrapfromrequestsimportgetdeflog(ctx,call):print("started")ctx=call(ctx)print("ended")returnctxdeftimeit(ctx,call):started=now()ctx=call(ctx)ended=now()-startedctx['duration']=endedreturnctxdefto_requests(ctx):get(ctx['url'])returnctxfancy_get=wrap(to_requests,middleware=[log,timeit])fancy_get({'url':'https://google.com'})Quick StartInstall using pip/pipenv/etc. (we recommendpoetryfor sane dependency management):$ poetry add formationBest PracticesAcontextobject is a loose bag of objects. With that freedom comes responsibility and opinion.For example, this is how Formation models arequestsintegration, with data flowing insidecontext:It models aFormationHttpRequestwhich abstracts the essentials of making an HTTP request (later shipped torequestsitself in the way that it likes)It tucksFormationHttpRequestunder thefmtn.reqkey.Additional information regarding such a request is keptalongsidefmtn.req. For example a request id is kept in thereq.idkey. This creates a flat (good thing) dict to probe. The reason additional data does not have thefmtnprefix is that you can always build your own that uses a different prefix (which you cant say about internal Formation inner workings).added support for async http client via aio_httpYou can use this viafor_aio_httpThanks:To allContributors- you make this happen, thanks!CopyrightCopyright (c) 2018@jondot. SeeLICENSEfor further details.
Package: hsgconv
# hsgconv
Coordinate conversion methods between [England’s Highways Standards and British National Grid (OSBG36 or EPSG:27700)][1] in pure Python.![][hestandardgrids]## Install
The module can be installed through pip by running`pip install hsgconv`. The example shows the recommended way to proceed and shows  cumulative errors.___Note:Tested on Python 2.7 and IronPython 2.7 - should work on 3.x too.[1]:http://www.standardsforhighways.co.uk/ha/standards/ians/pdfs/ian99.pdf“Standards for Highways”
[hestandardgrids]:https://github.com/ccampanile/hsgconv/raw/master/examples/localgrids.PNG
Package: hsh
Documentation:https://chrissimpkins.github.io/hsh/Descriptionhsh is a cross-platform command line application that generates file hash digests and performs file integrity checks via file hash digest comparisons. It supports the following hash algorithms:MD5SHA1SHA224SHA256SHA384SHA512InstallInstall withpipusing the command:$pipinstallhshordownload the source repository, unpack it, and navigate to the top level of the repository.  Then enter:$pythonsetup.pyinstallUpgradeYou can upgrade your hsh version with the command:$pipinstall--upgradehshExamples of hsh File Hash Digest FeaturesDefault SHA256 File Hash Digests$hshsomefile.txtSHA256(somefile.txt):5c2b47648ae96e60b5d02c573aefa6b01fb49d1b9e1ebd8a1b1a794cf522e6e3Change the File Hash Digest Algorithm with a Command$hshsha1somefile.txtSHA1(somefile.txt):0e1fb609e951c85e01f4048f01f0b51256bb3917Examples of hsh File Integrity Check FeaturesDefault SHA256 Digest File to File Comparisons$hshfirstfile.txtsecondfile.txtSHA256(firstfile.txt):0dbe4bb7df0f6d271e8f1fc30ec586fdfb5218e5a615c9abd3843943b4779e9eSHA256(secondfile.txt):0dbe4bb7df0f6d271e8f1fc30ec586fdfb5218e5a615c9abd3843943b4779e9eThehashdigestsareidentical.Automated File to File Hash Digest Comparisons for Supported Hash Types$hshfirstfile.txt0e1fb609e951c85e01f4048f01f0b51256bb3917SHA1(firstfile.txt):0e1fb609e951c85e01f4048f01f0b51256bb3917SHA1(test):0e1fb609e951c85e01f4048f01f0b51256bb3917Thehashdigestsareidentical.File Hash Digest to File Hash Digest Comparisons$hshda24f4932321286ac849f9145707f0e8da24f4932321286ac849f9145707f0e9da24f4932321286ac849f9145707f0e8da24f4932321286ac849f9145707f0e9===============================^ThehashdigestsareNOTidentical.A diff string is displayed below the hash digests when they differ as shown in the example above.UsageCommand line syntax and available commands & options are available inthe documentationor by entering the command:$hsh--helpLicenseMIT License
Package: hshop-api
# hshop-api
![PyPI - Downloads](https://img.shields.io/pypi/dw/hshop-api)
Python API for interacting with the [hShop](https://hshop.erista.me).
# Install
The project exists on PyPI, so just run:` pip installhshop-api`# Usage
This project still has a long way to go, but here’s what you can do right now and an example:### Download content`py importhshop-apias hs hs.download(478,r"C:\\Users\\Alexander\\Desktop\\CIAs\\")# Download Mario Kart 7 (Legit CIA), and send the path where it's going to be located to the API. NB: If you're on Windows, you have to put in 2 backslashes to escape the backslash and put an r before you begin the path string. This does not affect Linux or macOS.) `# Donate
Please don’t donate to me if you liked this project, donate to hShop instead.
Package: h-signature
H-SignatureA C++ or Python Library for computing the h-signature as defined in [1].What is the H-signature? In the simplest case, it tells you whether two closed 3D curves are linked, or unlinked. The figure below shows some examples.In the general case, the H-signature is computed between one closed curve$\tau$and a skeleton of closed curves$S=\{S_1,\dots,S_m\}$. In code, each curve is a matrix of [3xN] (C++) or [Nx3] (Python) where N is the number of points in the curve. The H-signature is a vector (ordered list) of integers, where each element correspond to the h-signature of$\tau$with respect to one curve in$S$.InstallationFor python:pipinstallh-signatureFor C++:# The only option we offer is building from source with cmakegitclonegit@github.com:UM-ARM-Lab/h-signature.gitcdh-signature
mkdirbuild# The only dependency is Eigen3sudoaptinstalllibeigen3-dev# Build and run testscdbuild
cmake..
make
ctestAPIThere is only one function!# Python with numpyfromh_signature.h_signature_npimportget_h_signature# Tau is an [Nx3] numpy array and skeleton is a dict of [3xN] numpy arraysh_sig=get_h_signature(tau,skeleton)print(h_sig)// C++ with Eigen#include<h_signature/h_signature.hpp>// The above header has these typedefs// typedef Eigen::Matrix3Xd Loop;// typedef std::map<std::string, Eigen::Matrix3Xd> Skeleton;// And this is the function, that's it!HSignatureget_h_signature(Loopconst&loop,Skeletonconst&skeleton);Citation
Package: hsipy
hsipy: Is a Python module to perform phasor analysis and visualization.Hyperspectral imaging (HSI) have become paramount
in biomedical science. The power of the combination between traditional
imaging and spectroscopy opens the possibility to address information
inaccessible before. For bioimaging analysis of these data, the Phasor
Plots are tools that help the field because of their straightforward
approach. Thus it is becoming a key player in democratizing access to HSI,
and improve open source software for bioimaging communities.hsipy is a module for HSI data analysis using the phasor approach.
The phasor approach was developed as model free method
and relies on the Fourier Transform properties.Documentation[Git Repository]https://github.com/schutyb/rep-hsipyPhasor AnalysisConsidering an hyperspectral image stack, the fluorescence spectra at each pixel can be
transformed in phasor coordinates (G (λ)) and (S (λ)) as described in the following
equations. I(λ) represent the intensity at every wavelength (channel), n is the
number of the harmonic and λ i the initial wavelength. The, x and y coordinates
are plotted in the spectral phasor plot.$$ G(\lambda) = \frac{\int_L I(\lambda) cos\left( 2\pi n \frac{\lambda - \lambda_i}{\lambda_{max} - \lambda_{min}} \right)}{\int_L I(\lambda)d\lambda}$$$$ S(\lambda) = \frac{\int_L I(\lambda) sen\left( 2\pi n \frac{\lambda - \lambda_i}{\lambda_{max} - \lambda_{min}} \right)}{\int_L I(\lambda)d\lambda} j $$The angular position in the spectral phasor plot relates to the center of mass of
the emission spectrum and the modulus depends on the spectrum’s full width at
the half maximum (FWHM). For instance, if the spectrum is broad its location
should be close to the center. Otherwise, if there is a red shift in the spectrum,
its location will move counterclockwise toward increasing angle from position
(1, 0). Spectral phasors have the same vector properties as lifetime phasors.
A detailed description of the spectral phasor plot properties can be found in
Malacrida et al. 1.InstallationpipinstallhsipycondainstallhsipyAuthors@schutybLicensebsd-3-clauseContributingContributions are always very well welcome. The PhasorPy library intends
to create an open-source and collaborative community between spectroscopy
and fluorescence microscopy users with the same functionalities as SimFCS
but accessible and self-sustainable in the long term as other Python
libraries and communities.References[1] Malacrida, L., Gratton, E. & Jameson, D. M. Model-free methods to study
membrane environmental probes: A comparison of the spectral phasor and
generalized polarization approaches. Methods Appl. Fluoresc. 3, 047001 (2015).
Package: hsir
HSIROut-of-box Hyperspectral Image Restoration ToolboxDenoising for remotely sensed images fromQRNN3DInstallpipinstallhsirUsageHere are some runable examples, please refer to the code for more options.pythonhsirun/train.py-aqrnn3d.qrnn3d
pythonhsirun/test.py-aqrnn3d.qrnn3d-rqrnn3d.pth-ticvl_512_50BenchmarkPretrained Models|Training Log|DatasetsBaidu Drive's Share Code=HSIRSupported ModelsDenoisingSuper ResolutionSpectral Compressive ImagingSpectral ReconstructionHSID-CNN (TGRS'2018)QRNN3D (TNNLS'2020)TS3C (NeurIPS'2021)GRUNet (Neurocomputing'2021)Bi3DQRNN (JStar'2021)SSPSR (TCI'2020)MCNet (Remote Sense'2020)IFN (TGRS'2020)3D-FCNN (Remote Sense'2017)MST (CVPR'2022)CST (ECCV'2022)MST++ (CVPRW'2022)HDNET (CVPR'2022)BIRNAT (TPAMI'2022)DGSMP (CVPR'2021)GAP-Net (arxiv'2020)TSA-Net (ECCV'2020)ADMM-Net (ICCV'2019)λ-Net (ICCV'2019)Gaussian Denoising on ICVLSigma=30Sigma=50Sigma=70Sigma=BlindParams(M)Runtime(s)FLOPsPSNRSSIMSAMPSNRSSIMSAMPSNRSSIMSAMPSNRSSIMSAMNoisy18.590.110.080714.150.0460.99111.230.0251.10517.340.1140.859BM4D15438.450.9340.12635.600.8890.16933.700.8450.20737.660.9140.143TDL1840.580.9570.06238.010.9320.08536.360.9090.10539.910.9460.072ITSReg90741.480.9610.08838.880.9410.09836.710.9230.11240.620.9530.087LLRT62741.990.9670.05638.990.9450.07537.360.9300.08740.970.9560.064KBR175541.480.9840.08839.160.9740.10036.710.9610.11340.680.9790.080WLRTR160042.620.9880.05639.720.9780.07337.520.9670.09541.660.9830.064NGmeet16642.990.9890.05040.260.9800.05938.660.9740.06742.230.9850.053HSID0.40338.700.9490.10336.170.9190.13434.310.8860.16137.800.9350.116QRNN3D0.860.7342.220.9880.06240.150.9820.07438.300.9740.09441.370.9850.068TS3C0.830.9542.360.9860.07940.470.9800.08739.050.9740.09641.520.9830.085GRUNet14.20.8742.840.9890.05240.750.9830.06239.020.9770.08042.030.9870.057Complex Denoising on ICVLnon-iidg+stripeg+deadlineg+impulsemixtureParams(M)Runtime(s)FLOPsPSNRSSIMSAMPSNRSSIMSAMPSNRSSIMSAMPSNRSSIMSAMPSNRSSIMSAMNoisy18.250.1680.89817.800.1590.91017.610.1550.91714.800.1140.92614.080.0990.944LRMR32.800.7190.18532.620.7170.18731.830.7090.22729.700.6230.31128.680.6080.353LRTV33.620.9050.07733.490.9050.07832.370.8950.11531.560.8710.24230.470.8580.287NMoG34.510.8120.18733.870.7990.26532.870.7970.27628.600.6520.48627.310.6320.513TDTV38.140.9440.07537.670.9400.08136.150.9300.09936.670.9350.09434.770.9190.113HSID0.40338.400.9470.09537.770.9420.10437.650.9400.10235.000.8990.17434.050.8880.181TS3C0.830.9541.120.9860.06940.660.9850.07739.380.9820.10035.920.9510.20534.360.9450.230QRNN3D0.860.7342.790.9780.05242.350.9760.05542.230.9760.05639.230.9450.10938.250.9380.107GRUNet14.20.8742.890.9920.04742.390.9910.05042.110.9910.05040.700.9850.06738.510.9810.081AcknowledgementQRNN3DDPHSIRMSTTLCCitationIf you find this repo helpful, please considering citing us.@article{LAI2022281,title={Deep plug-and-play prior for hyperspectral image restoration},journal={Neurocomputing},volume={481},pages={281-293},year={2022},issn={0925-2312},doi={https://doi.org/10.1016/j.neucom.2022.01.057},author={Zeqiang Lai and Kaixuan Wei and Ying Fu},}
Package: hskl
HSKL: Hyperspectral-scikit-learnHyperspectral image analysis usingscikit-learnInstallationThe package can be installed frompip:pip install hsklUsageTraining a pixel-level classifier for segmentation:importosfromhskl.demoimportdl_hyrank,load_hyrankimporthskl.classificationasclassificationimporthskl.utilsasutils# Download, unpack, and load HyRANK dataset from current directory.path=os.getcwd()ifnotos.path.exists("HyRANK_satellite"):dl_hyrank(path)images,labels,_=load_hyrank(path)# Dimensional reduction using PCA, retain 99.9% image variancepca=utils.pca_fit(images[0])train,_=utils.pca_apply(images[0],pca,0.999)test,_=utils.pca_apply(images[1],pca,0.999)label=labels[0]test_mask=labels[1]>0# Train a classifier and predict test image labelscl=classification.HyperspectralClassifier(method_name="LinearDiscriminantAnalysis")cl.fit(train,label)prediction=cl.predict(test)# Visualization of training data, test prediction, and test ground truthfig_objs_train=utils.overlay(train,label)utils.save_overlay(fig_objs_train,"hyrank_train.png")fig_objs_predict=utils.overlay(test,prediction*test_mask)utils.save_overlay(fig_objs_predict,"hyrank_predict.png")fig_objs_test=utils.overlay(test,labels[1])utils.save_overlay(fig_objs_test,"hyrank_test.png")Output:Training image and ground truth labels:Test image and ground truth labels:Test image and predicted labels:Notes:Shape oftrainandtestarrays are (DimX, DimY, SpectralChannels).Shape oflabelandpredictionarrays are (DimX, DimY).Labeling convention for classifiers:
(a) Datatype:label.dtype == np.uint8.
(b) Labeled classes start from integer 1. Pixels withlabel == 0are ignored (masked out).Dimension(s) oftrainandlabelmust be consistent:train.shape[0] == label.shape[0]andtrain.shape[1] == label.shape[1].Inputs:train,test, andlabelcan also be lists ofnp.ndarrays with each element satisfying the preceeding requirements.Planned FeaturesIn the near-term:Test scripts and dataGrid search cross validationIn the long-term, support for:PipelinesPatch-based featurizerDask-enabled parallelismDeep learning (PyTorch) modelsReferencesKarantzalos, Konstantinos, Karakizi, Christina, Kandylakis, Zacharias, & Antoniou, Georgia. (2018). HyRANK Hyperspectral Satellite Dataset I (Version v001). Zenodo.http://doi.org/10.5281/zenodo.1222202Some functionalities in this package are provided by Spectral Python (SPy):https://github.com/spectralpython/spectral
Package: hsl-builder
HSL Builder RepoPython pip package for creating HSL Elements.Installation:You can install hsl builder using pip.pipinstall--prehsl_builderORYou can also addhsl_builder==0.0.1b0it to your projectrequirements.txtUsage:#import hsl builderfromhsl_builderimportButtonfromhsl_builder.elementsimportActionable,ActionableType,URI# Create a buttonbutton=Button("Title")# Create link actionableactionable=Actionable("actionable text",ActionableType.APP_ACTION,URI.LINK)actionable.payload={'url':'https://www.haptik.ai'}# Add actionable to buttonbutton.actionables.append(actionable)# generate hsl for our button objecthsl=button.to_hsl()PublishingMake sure you have specified the correct version specified insetup.pyRemove existing build filesrm-rfrm-rfdist/build/hsl_builder.egg-info/Build package wheelpythonsetup.pysdistbdist_wheelPublishing packageMake sure that correct version is specified in setup.pyyou'll need to create an account at pypi beforehandpublish to test.pypi.org to make sure that whl is built correctly and can be installed and used from other machinespython-mtwineupload--repository-urlhttps://test.pypi.org/legacy/dist/*publish to pypipython-mtwineuploaddist/*
Package: hsluv
A Python implementation ofHSLuv(revision 4).Installationpip install hsluvPython 2 users:pip install hsluv==5.0.0UsageThis library does not hide (clamp) floating point error, e.g. you might receive a value outside
of the expected range. If you wish to display the outputs of this library, consider rounding them
for your purpose. The floating point error has not been quantified, but at least 10 decimal digits
should be free of it.hsluv_to_hex([hue, saturation, lightness])hueis a float between 0 and 360,saturationandlightnessare floats between 0 and 100. This
function returns the resulting color as a hex string.hsluv_to_rgb([hue, saturation, lightness])Like above, but returns a list of 3 floats between 0 and 1, for each RGB channel.hex_to_hsluv(hex)Takes a hex string and returns the HSLuv color as a list of floats as defined above.rgb_to_hsluv([red, green, blue])Like above, butred,greenandblueare passed as floats between 0 and 1.For HPLuv (the pastel variant), usehpluv_to_hex,hpluv_to_rgb,hex_to_hpluvandrgb_to_hpluv.TestingRunpython setup.py test.AuthorsRobert McGinley (mcginleyr1)Alexei Boronine (boronine)
Package: hsluv-restored
A Python implementation ofHSLuv(revision 4).Installationpip install hsluvUsageThis library does not hide (clamp) floating point error, e.g. you might receive a value outside
of the expected range. If you wish to display the outputs of this library, consider rounding them
for your purpose. The floating point error has not been quantified, but at least 10 decimal digits
should be free of it.hsluv_to_hex([hue, saturation, lightness])hueis a float between 0 and 360,saturationandlightnessare floats between 0 and 100. This
function returns the resulting color as a hex string.hsluv_to_rgb([hue, saturation, lightness])Like above, but returns a list of 3 floats between 0 and 1, for each RGB channel.hex_to_hsluv(hex)Takes a hex string and returns the HSLuv color as a list of floats as defined above.rgb_to_hsluv([red, green, blue])Like above, butred,greenandblueare passed as floats between 0 and 1.For HPLuv (the pastel variant), usehpluv_to_hex,hpluv_to_rgb,hex_to_hpluvandrgb_to_hpluv.TestingRunpython setup.py test.AuthorsRobert McGinley (mcginleyr1)Alexei Boronine (boronine)
Package: hsmfile
# hsmfile: a Python module to expedite access to files on a remote data store## SynopsisThis module addresses the common case where there are large data files on a supercomputer and these are to be processed, maybe on the
supercomputer or maybe on local machines that can mount the supercomputer file system. The hsmfile module provides functions toSpecify the file locations in a platform-independent way, given some platform-specific configuration data; andCopy files automatically as necessary from the supercomputer to the local machines.It is based on a set of IDL functions with a similar purpose with the prefix
[mgh_san](https://github.com/hadfieldnz/idl-roms/tree/master/san).For more information, see the [documentation on GitHub Pages](https://hadfieldnz.github.io/hsmfile/).
Package: hsml
Hopsworks Model ManagementHSML is the library to interact with the Hopsworks Model Registry and Model Serving. The library makes it easy to export, manage and deploy models.The library automatically configures itself based on the environment it is run.
However, to connect from an external Python environment additional connection information, such as host and port, is required. For more information about the setup from external environments, see the setup section.Getting Started On HopsworksInstantiate a connection and get the project model registry and serving handlesimporthsml# Create a connectionconnection=hsml.connection()# Get the model registry handle for the project's model registrymr=connection.get_model_registry()# Get the model serving handle for the current model registryms=connection.get_model_serving()Create a new modelmodel=mr.tensorflow.create_model(name="mnist",version=1,metrics={"accuracy":0.94},description="mnist model description")model.save("/tmp/model_directory")# or /tmp/model_fileDownload a modelmodel=mr.get_model("mnist",version=1)model_path=model.download()Delete a modelmodel.delete()Get best performing modelbest_model=mr.get_best_model('mnist','accuracy','max')Deploy a modeldeployment=model.deploy()Start a deploymentdeployment.start()Make predictions with a deployed modeldata={"instances":[model.input_example]}predictions=deployment.predict(data)You can find more examples on how to use the library inexamples.hopsworks.ai.DocumentationDocumentation is available atHopsworks Model Management Documentation.IssuesFor general questions about the usage of Hopsworks Machine Learning please open a topic onHopsworks Community.Please report any issue usingGithub issue tracking.ContributingIf you would like to contribute to this library, please see theContribution Guidelines.