Saravanan666/pypi-org huggingface dataset:
Package: ontoportal-client
OntoPortal ClientA client to BioPortal and other OntoPortal instances.💪 Getting StartedOne of several OntoPortal clients can be instantiated and
accessed with the same API:fromontoportal_clientimportBioPortalClientbioportal_client=BioPortalClient()ontologies=bioportal_client.get_ontologies()Complete documentation can be found onontoportal-client.readthedocs.io🚀 Installation$pipinstallontoportal_clientThe most recent code and data can be installed directly from GitHub with:$pipinstallgit+https://github.com/cthoyt/ontoportal-client.git⚙️ ConfigurationConfiguration of the API tokens for for each of the clients is mediated bypystow. Using BioPortal as an example,
you can either:Pass the API key in the construtor of the client classSet theBIOPORTAL_API_KEYenvironment variableCreate a file~/.config/bioportal.iniand set theapi_keyvalue under
the[bioportal]section.👐 ContributingContributions, whether filing an issue, making a pull request, or forking, are appreciated. SeeCONTRIBUTING.mdfor more information on getting involved.👋 Attribution⚖️ LicenseThe code in this package is licensed under the MIT License.🍪 CookiecutterThis package was created with@audreyfeldroy'scookiecutterpackage using@cthoyt'scookiecutter-snekpacktemplate.🛠️ For DevelopersSee developer instructionsThe final section of the README is for if you want to get involved by making a code contribution.Development InstallationTo install in development mode, use the following:$gitclonegit+https://github.com/cthoyt/ontoportal-client.git
$cdontoportal-client
$pipinstall-e.🥼 TestingAfter cloning the repository and installingtoxwithpip install tox, the unit tests in thetests/folder can be
run reproducibly with:$toxAdditionally, these tests are automatically re-run with each commit in aGitHub Action.📖 Building the DocumentationThe documentation can be built locally using the following:$gitclonegit+https://github.com/cthoyt/ontoportal-client.git
$cdontoportal-client
$tox-edocs
$opendocs/build/html/index.htmlThe documentation automatically installs the package as well as thedocsextra specified in thesetup.cfg.sphinxplugins
liketexextcan be added there. Additionally, they need to be added to theextensionslist indocs/source/conf.py.📦 Making a ReleaseAfter installing the package in development mode and installingtoxwithpip install tox, the commands for making a new release are contained within thefinishenvironment
intox.ini. Run the following from the shell:$tox-efinishThis script does the following:UsesBump2Versionto switch the version number in thesetup.cfg,src/ontoportal_client/version.py, anddocs/source/conf.pyto not have the-devsuffixPackages the code in both a tar archive and a wheel usingbuildUploads to PyPI usingtwine. Be sure to have a.pypircfile configured to avoid the need for manual input at this
stepPush to GitHub. You'll need to make a release going with the commit where the version was bumped.Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can
usetox -e bumpversion minorafter.
Package: ontor
ONTology editOR (ontor)ontology editor built onOwlready2functionalityeach instance of the ontor class represents an individual ontology and provides support for:creating new, loading existing, and saving ontologiesmodifying ontologies:import other ontologiessimply extract information such as axioms and class restrictionsinsert classes, properties, instances, relations, and restrictionsinsert general class axioms using a workaround for Owlready2delete classes, properties, instances, relations, and restrictions but preserve the ontology's structure by reassigning subclasses and instances appropriatelyreasoning over ontologies and debugging by interactively deleting problematic axiomsvisualizing the entire ontology or selected parts thereofontor provides a tuple based syntax with JSON and CSV support for ontology editing to facilitate focusing on the ontology's contentrequirements and installationPython 3.9+install ontor using pipfrom PyPI:pip install ontorfrom GitHub, in editable mode:pip install -e .generate documentation via sphinx using the makefile indocs/:make htmldemothe directoryexample/includes a demo application inspired byProtégé's pizza examplegeneral class axiomsin addition to class axioms, General Class Axioms (GCAs) can express more complex statements - the generic axioms are equivalented using helper classesin the example, a uniform price of 5 is set for all pizzas with seafood toppings without making use of an explicitly defined class for these pizzas:\[
  ["has_topping",null,"min",1,"seafood_topping",null,null,null,null,null,null,null,true],
  ["has_price",null,"value",null,null,"float",null,null,5,null,null,null,true]
]this allows a reasoner to infer that the price for all instances ofseafood_pizzaas well as for the  instanceAnother_pizzais 5interactive debugginginteractively debug an ontologyin the example:ontor3.debug_onto()visualizationvisualize selected instances, classes, and properties in a given radius around a focus node; e.g., all nodes in a radius of two relations around the node "John"in the example:ontor3.visualize(classes=["human", "pizza"], properties=["likes", "diameter_in_cm"], focusnode="John", radius=2)workflowWhen creating ontologies from scratch, note that some functions have to be called in a specific order:add_taxo- the taxonomy has to be created first to ensure that all classes are defined, which are required by the properties, axioms, and individualsadd_ops,add_dps- properties must be defined before axioms can be specifiedadd_axioms,add_gcas,add_instances- axioms and instances can only be added when all the necessary classes and properties have been definedlicenseGPL v3.0contactFelix Ocker -felix.ocker@googlemail.com
Package: ontospy
OntospyPython library and command-line interface for inspecting and visualizing RDF models.LinksPypiGithubDocsChangelogYouTubeDescriptionOntospy is a lightweight Python library and command line tool for inspecting and visualizing vocabularies encoded using W3C Semantic Web standards, that is, RDF or any of its dialects (RDFS, OWL, SKOS).The basic workflow is simple: load a graph by instantiating theOntospyclass with a file containing RDFS, OWL or SKOS definitions. You get back a object that lets you interrogate the ontology. That's all!The same functionalities are accessible also via a command line application. This is an interactive environment (like a repl) that allows to load ontologies from a local repository, interrogate them and cache them so that they can be quickly reloaded for inspection later on.Generating ontology documentationOntospy can be used to generate HTML documentation for an ontology pretty much out-of-the-box.See the websiteExamples of ontology documentation generated via Ontospy, or jump straight to the sampleCIDOC-CRMorFOAFdocumentation pages.From version 2.0, the documentation generation libraries are installed by default with Ontospy. Previously, third party dependencies (e.g. Django) had to be installed separately.StatusI have little time to spend on this project these days, so I'm mainly focusing on bug fixes and maintenance. Happy to review PRs if you want to add more functionalities!Development# git clone the repo first
$ mkvirtualenv ontospy
$ pip install -r requirements.txt
$ pip install -e .Documentationhttp://lambdamusic.github.io/Ontospy/
Package: onto-vae
OntoVAEOntoVAE is a package that can be used to integrate biological ontologies into latent space and decoder of Variational Autoencoder models.
This allows direct retrieval of pathway activities from the model.
OntoVAE can also be used to simulate genetic or drug induced perturbations, as demonstrated in our manuscript
'Biologically informed variational autoencoders allow predictive modeling of genetic and drug induced perturbations'.InstallationIn the future, installation via pip will be supported. For now, you can install the package through github as follows:git clone https://github.com/hdsu-bioquant/onto-vae.git
cd onto-vaeIt is best to first create a new environment, e.g. with conda, and then install the package inside.conda create -n ontovae python=3.7
conda activate ontovae
pip install -r requirements.txtFor on example on how to use our package, please see the Vignette! If you want to run the Vignette as Jupyter notebook, inside your conda environment, also install Jupyter and then open the jupyter notebook:conda install jupyter
jupyter notebook
Package: ontoviz
OntovizOntology visualization with PythonInstallationpython -m pip install ontovizExampleontoviz_to_dot -o test.dot test.ttl -O ontology.ttl
    dot -Tpng -o test.png test.dotUse-oto indicate the path of output fileUse-Oto indicate the input ontology (Optional).Use-Cto indicate the configuration file (Optional).max_label_length: config the max length of labels. If the text exceeds the length, exceeded part will be replaced with "…". Default value is0.blacklist: config the predicate that you don't want to see in the graph.class_inference_in_object: config the predicate that can inference the object is aClass, even if the class doesn't defined in the ontology.label_property: config the predicate that used for labeling nodes, if such a label exists, it will display inside the node.tooltip_property: config the predicate that contains the tooltip texts.bnode_regex: a list of regexes, if an uri matches, then it will be dispaly as a blank node without its uri nor label. It can be useful if you have a lot of reifications.colors: config the colors of nodesclass,literal,instancecan accept HEX value(e.g."#ff0000"), MATLAB style(e.g."r"), and color name (e.g."red")."colors": {
"class": "#ff0000",
"literal": "r",
"instance": "red",
}instancecan also accept a dict value to specify the color of each class instance. And use"default"to to set color for undefined instances."instance": {
"https://tac.nist.gov/tracks/SM-KBP/2018/ontologies/SeedlingOntology#Facility": "#a6cee3",
"default": "#ffff99"
}filled: config whether fill the node, default value:true.Classes defined in the ontology will be omitted in the output graph. This action can be switched with argument-V.Useful Graphviz flags-Kto specify whichlayout algorithmto use. E.g.-Kneatoand-Ksfdp. Notice that inorder to usesfdplayout algorithm, you will need to build your graphviz withGTS.-Tto specify theoutput format.-Gto set agraph attribute. E.g.-Goverlap=prismRequirementsAll the minimal Python requirements are installed duringpython -m pip install ontovizFor a development setup dogit clone https://github.com/WWU-AMM/ontoviz
cd ontoviz
virtualenv venv
. venv/bin/activate
python -m pip install -e .[full]
pre-commit installThis will also install tools for documentation building and testingIn order to convertdotintopngorsvgimage, you will needGraphviz.TestingSimply runpytestfrom the repository root. All tests are also run automatically by github actions on push/PR.
Package: ontoxref
ontoxref for Python 3.xFind mappings (or cross-references) between terms from ontologies and coding standardsPrerequisiteThe client module requires Python 3.xInstallpip install ontoxrefExample codefromontoxref.backendimportConnectionManagercon_mngr=ConnectionManager.init("/path/to/xref-all.json")con=con_mngr.get_connection()service=con.get_service("XrefService")# Find ICD10CM code for 'EFO:0000095'"service.find_xref("EFO:0000095","ICD10CM")# Find DOID code for 'MONDO:0004948'"service.find_xref("MONDO:0004948","DOID")
Package: ont-seqdist-cuda101
SeqdistProbability distributions over sequences in pytorch and cupy.Installpip install seqdistHow to useComparison against builtin pytorch implementation of the standard CTC loss:sample_inputs = logits, targets, input_lengths, target_lengths = ctc.generate_sample_inputs(T_min=450, T_max=500, N=128, C=20, L_min=80, L_max=100)
print('pytorch loss: {:.4f}'.format(ctc.loss_pytorch(*sample_inputs)))
print('seqdist loss: {:.4f}'.format(ctc.loss_cupy(*sample_inputs)))pytorch loss: 12.8080
seqdist loss: 12.8080Speed comparisonPytorch:report(benchmark_fwd_bwd(ctc.loss_pytorch, *sample_inputs))fwd: 4.79ms (4.17-5.33ms)
bwd: 9.69ms (8.33-10.88ms)
tot: 14.47ms (12.67-16.20ms)Seqdist:report(benchmark_fwd_bwd(ctc.loss_cupy, *sample_inputs))fwd: 7.22ms (6.78-7.85ms)
bwd: 6.21ms (5.82-8.57ms)
tot: 13.43ms (12.63-16.41ms)Alignmentsbetas = [0.1, 1.0, 10.]
alignments = {'beta={:.1f}'.format(beta): to_np(ctc.soft_alignments(*sample_inputs, beta=beta)) for beta in betas}
alignments['viterbi'] = to_np(ctc.viterbi_alignments(*sample_inputs))
fig, axs = plt.subplots(2, 2, figsize=(15, 8))
for (ax, (title, data)) in zip(np.array(axs).flatten(), alignments.items()):
    ax.imshow(data[:, 0].T, vmax=0.05);
    ax.set_title(title)
Package: ont-seqdist-cuda102
SeqdistProbability distributions over sequences in pytorch and cupy.Installpip install seqdistHow to useComparison against builtin pytorch implementation of the standard CTC loss:sample_inputs = logits, targets, input_lengths, target_lengths = ctc.generate_sample_inputs(T_min=450, T_max=500, N=128, C=20, L_min=80, L_max=100)
print('pytorch loss: {:.4f}'.format(ctc.loss_pytorch(*sample_inputs)))
print('seqdist loss: {:.4f}'.format(ctc.loss_cupy(*sample_inputs)))pytorch loss: 12.8080
seqdist loss: 12.8080Speed comparisonPytorch:report(benchmark_fwd_bwd(ctc.loss_pytorch, *sample_inputs))fwd: 4.79ms (4.17-5.33ms)
bwd: 9.69ms (8.33-10.88ms)
tot: 14.47ms (12.67-16.20ms)Seqdist:report(benchmark_fwd_bwd(ctc.loss_cupy, *sample_inputs))fwd: 7.22ms (6.78-7.85ms)
bwd: 6.21ms (5.82-8.57ms)
tot: 13.43ms (12.63-16.41ms)Alignmentsbetas = [0.1, 1.0, 10.]
alignments = {'beta={:.1f}'.format(beta): to_np(ctc.soft_alignments(*sample_inputs, beta=beta)) for beta in betas}
alignments['viterbi'] = to_np(ctc.viterbi_alignments(*sample_inputs))
fig, axs = plt.subplots(2, 2, figsize=(15, 8))
for (ax, (title, data)) in zip(np.array(axs).flatten(), alignments.items()):
    ax.imshow(data[:, 0].T, vmax=0.05);
    ax.set_title(title)
Package: ont-seqdist-cuda111
SeqdistProbability distributions over sequences in pytorch and cupy.Installpip install seqdistHow to useComparison against builtin pytorch implementation of the standard CTC loss:sample_inputs = logits, targets, input_lengths, target_lengths = ctc.generate_sample_inputs(T_min=450, T_max=500, N=128, C=20, L_min=80, L_max=100)
print('pytorch loss: {:.4f}'.format(ctc.loss_pytorch(*sample_inputs)))
print('seqdist loss: {:.4f}'.format(ctc.loss_cupy(*sample_inputs)))pytorch loss: 12.8080
seqdist loss: 12.8080Speed comparisonPytorch:report(benchmark_fwd_bwd(ctc.loss_pytorch, *sample_inputs))fwd: 4.79ms (4.17-5.33ms)
bwd: 9.69ms (8.33-10.88ms)
tot: 14.47ms (12.67-16.20ms)Seqdist:report(benchmark_fwd_bwd(ctc.loss_cupy, *sample_inputs))fwd: 7.22ms (6.78-7.85ms)
bwd: 6.21ms (5.82-8.57ms)
tot: 13.43ms (12.63-16.41ms)Alignmentsbetas = [0.1, 1.0, 10.]
alignments = {'beta={:.1f}'.format(beta): to_np(ctc.soft_alignments(*sample_inputs, beta=beta)) for beta in betas}
alignments['viterbi'] = to_np(ctc.viterbi_alignments(*sample_inputs))
fig, axs = plt.subplots(2, 2, figsize=(15, 8))
for (ax, (title, data)) in zip(np.array(axs).flatten(), alignments.items()):
    ax.imshow(data[:, 0].T, vmax=0.05);
    ax.set_title(title)
Package: ont-seqdist-cuda112
SeqdistProbability distributions over sequences in pytorch and cupy.Installpip install seqdistHow to useComparison against builtin pytorch implementation of the standard CTC loss:sample_inputs = logits, targets, input_lengths, target_lengths = ctc.generate_sample_inputs(T_min=450, T_max=500, N=128, C=20, L_min=80, L_max=100)
print('pytorch loss: {:.4f}'.format(ctc.loss_pytorch(*sample_inputs)))
print('seqdist loss: {:.4f}'.format(ctc.loss_cupy(*sample_inputs)))pytorch loss: 12.8080
seqdist loss: 12.8080Speed comparisonPytorch:report(benchmark_fwd_bwd(ctc.loss_pytorch, *sample_inputs))fwd: 4.79ms (4.17-5.33ms)
bwd: 9.69ms (8.33-10.88ms)
tot: 14.47ms (12.67-16.20ms)Seqdist:report(benchmark_fwd_bwd(ctc.loss_cupy, *sample_inputs))fwd: 7.22ms (6.78-7.85ms)
bwd: 6.21ms (5.82-8.57ms)
tot: 13.43ms (12.63-16.41ms)Alignmentsbetas = [0.1, 1.0, 10.]
alignments = {'beta={:.1f}'.format(beta): to_np(ctc.soft_alignments(*sample_inputs, beta=beta)) for beta in betas}
alignments['viterbi'] = to_np(ctc.viterbi_alignments(*sample_inputs))
fig, axs = plt.subplots(2, 2, figsize=(15, 8))
for (ax, (title, data)) in zip(np.array(axs).flatten(), alignments.items()):
    ax.imshow(data[:, 0].T, vmax=0.05);
    ax.set_title(title)
Package: ont-seqdist-cuda113
SeqdistProbability distributions over sequences in pytorch and cupy.Installpip install seqdistHow to useComparison against builtin pytorch implementation of the standard CTC loss:sample_inputs = logits, targets, input_lengths, target_lengths = ctc.generate_sample_inputs(T_min=450, T_max=500, N=128, C=20, L_min=80, L_max=100)
print('pytorch loss: {:.4f}'.format(ctc.loss_pytorch(*sample_inputs)))
print('seqdist loss: {:.4f}'.format(ctc.loss_cupy(*sample_inputs)))pytorch loss: 12.8080
seqdist loss: 12.8080Speed comparisonPytorch:report(benchmark_fwd_bwd(ctc.loss_pytorch, *sample_inputs))fwd: 4.79ms (4.17-5.33ms)
bwd: 9.69ms (8.33-10.88ms)
tot: 14.47ms (12.67-16.20ms)Seqdist:report(benchmark_fwd_bwd(ctc.loss_cupy, *sample_inputs))fwd: 7.22ms (6.78-7.85ms)
bwd: 6.21ms (5.82-8.57ms)
tot: 13.43ms (12.63-16.41ms)Alignmentsbetas = [0.1, 1.0, 10.]
alignments = {'beta={:.1f}'.format(beta): to_np(ctc.soft_alignments(*sample_inputs, beta=beta)) for beta in betas}
alignments['viterbi'] = to_np(ctc.viterbi_alignments(*sample_inputs))
fig, axs = plt.subplots(2, 2, figsize=(15, 8))
for (ax, (title, data)) in zip(np.array(axs).flatten(), alignments.items()):
    ax.imshow(data[:, 0].T, vmax=0.05);
    ax.set_title(title)
Package: ont-seqdist-cuda114
SeqdistProbability distributions over sequences in pytorch and cupy.Installpip install seqdistHow to useComparison against builtin pytorch implementation of the standard CTC loss:sample_inputs = logits, targets, input_lengths, target_lengths = ctc.generate_sample_inputs(T_min=450, T_max=500, N=128, C=20, L_min=80, L_max=100)
print('pytorch loss: {:.4f}'.format(ctc.loss_pytorch(*sample_inputs)))
print('seqdist loss: {:.4f}'.format(ctc.loss_cupy(*sample_inputs)))pytorch loss: 12.8080
seqdist loss: 12.8080Speed comparisonPytorch:report(benchmark_fwd_bwd(ctc.loss_pytorch, *sample_inputs))fwd: 4.79ms (4.17-5.33ms)
bwd: 9.69ms (8.33-10.88ms)
tot: 14.47ms (12.67-16.20ms)Seqdist:report(benchmark_fwd_bwd(ctc.loss_cupy, *sample_inputs))fwd: 7.22ms (6.78-7.85ms)
bwd: 6.21ms (5.82-8.57ms)
tot: 13.43ms (12.63-16.41ms)Alignmentsbetas = [0.1, 1.0, 10.]
alignments = {'beta={:.1f}'.format(beta): to_np(ctc.soft_alignments(*sample_inputs, beta=beta)) for beta in betas}
alignments['viterbi'] = to_np(ctc.viterbi_alignments(*sample_inputs))
fig, axs = plt.subplots(2, 2, figsize=(15, 8))
for (ax, (title, data)) in zip(np.array(axs).flatten(), alignments.items()):
    ax.imshow(data[:, 0].T, vmax=0.05);
    ax.set_title(title)
Package: on.video
OverviewThis product aims at enabling you to self-host your videos efficiently,
not bogging your Plone server down with actual video handling
(conversion etc.). Instead, you simply generate small text files telling
Plone where to actually find the videos, banner images etc. - see below.This product adds a “Video” content type with the following features:For each video, some metadata, like author and date of recording,
are collected.The videos themselves are served off the bare file system. The
product itself contains only sort of pointers to video files.The user should be able to view the file in a number of formats,
resolutions etc., and to download the videos.Conversion of videos from one format to another is outside the
scope of this product.Access control to the actual video files is outside the scope of
this product. Only the metadata, as defined in this product’s
schema, will be workflowed.The product will determine, at run time, the available formats for
a given video, and offer the user only these for viewing or download.The product aims to offer access to said videos for as many
client environments as possible.The product makes use of flowplayer (www.flowplayer.org).Structure of a metadata fileEach video requires a “metadata” file which adheres to the following
structure:First line:
Column 1: “thumbnail”
Column 2: filename for the thumbnail, if anySecond line:
Column 1: “playing time”
Column 2: time spec (HH:MM:SS)Third line:
Column 1: “selected”
Column 2: file name of the video that is to be played in the browserFourth line:
Column 1: “default size”
Column 2: size specification in width x height, eg. “500x300”Remaining lines:
Column 1: resolution and format, eg. “640x480, OGV”
Column 2: complete file name (without directories)You generate these files out-of-band in accord with your requirements for
video hosting.DevelopmentThe project is mirrored athttps://github.com/muellert/on.video.ChangelogVersion 0.2.2 (2013-06-14)Repair the source distribution to finally include all versioned filesVersion 0.2 (2013-02-20)Made most fields optionalThe video metadata file must now exist and validate upon adding the video.Added a gallery viewAdded a script to mass-add videos from the command lineAdded a portlet to show selected videosBugfixesVersion 0.1.1i (2012-11-28)Initial “real” release
Package: onvif-client
onvif-clientWS-Discovery and Simple ONVIF camera client library.UsageAn example of using WsDiscoveryClient classfromonvifimportWsDiscoveryClientwsd_client=WsDiscoveryClient()nvts=wsd_client.search()fornvtinnvts:print(nvt)wsd_client.dispose()An example of using OnvifClient classfromonvifimportOnvifClientonvif_client=OnvifClient('192.168.0.10',80,'user','password')profile_tokens=onvif_client.get_profile_tokens()forprofile_tokeninprofile_tokens:print(profile_token)Requirementsonvif-client 0.0.1 RequirementsPython >= 3.9WSDiscovery >= 2.0.0onvif2-zeep >= 0.3.4Installing onvif-clientUse pip to install the binary wheels onPyPI$pipinstallonvif-clientSupportBugs may be reported athttps://github.com/hasimoka/onvif-client/issuesContactIf you have any questions or comments about onvif-client,
please feel free to contact me via
eMail:hasimoka@gmail.comThis project is hosted athttps://github.com/hasimoka/onvif-client
Package: onweb
onwebCheck the status of some website.Installation$pipinstallonwebDocumentation$onwebhttps://google.comhttps://www.google.com301https://google.com200https://www.google.comHandle redirect code (e.g. 301):$onweb--redirecthttps://google.comhttps://www.google.com200https://www.google.com200https://www.google.com
Package: onx
Noughts & Crosses (Tic Tac Toe)Noughts & Crosses (Tic Tac Toe) terminal based, client-server online game with your partner through websockets.RequiresPython 3.10Install$ pip install onxor$ poetry shell
$ poetry add onxPlay GameFor running your game board just type in a terminal:$ onxYou will see a game board in a waiting for your partner state.Then ask your partner to run the same cli command withexactlythe same cli options.
You will be matched to your partner by cli options (size and winning sequence length) on a server side.If you are running a game with a public server than I'll suggest you to make a shorter delay between running your game board and your partners board. Just for reducing the probability to be matched with somebody else.There are command line options for changing game board settings.-gor--grid-sizechanges grid size.-wor--wining-lengthchanges winning sequence length.-hor--helpprints help.$ onx -g14 -w3Run Server and Client LocallySet up env variables.$ export LOCALHOST="0.0.0.0"
$ export PORT=8888Run server.$ onx -dRun client.$ onxRun Tests$ git clone git@github.com:vyalow/onx.git
$ cd onx
$ poetry shell
$ poetry install --no-root
$ pytest --covKnown Limitationsonxis currently based ontextualTUI framework which is awesome
but is at an extremely early development stage. As a result you may be faced with some rendering problem like711,710.
I'll suggest you to run a game board in a fullscreen mode for now.Public server is currently running on a free Heroku app. It means that a good enough SLA is not expected.Releasemake release version=[patch | minor | major]