Saravanan666/pypi-org huggingface dataset:
Package: kedifa
OverviewA system to upload SSL keys for some nodes to securely download them.0.0.7 (2023-10-05)select only valid CRLs0.0.6 (2021-10-13)switch to python3/python2 compatibilitystabilised tests0.0.5 (2021-09-29)fixing release0.0.4 (2021-09-29)require minimal zc.lockfile version0.0.3 (2021-09-29)support multiple CRLs0.0.2 (2020-01-27)add missing CHANGES.rst0.0.1 (2020-01-27)first published version
Package: kedlang
KedKed is the first programming language known to hail fromThe People's Republic of Cork.
It was first discovered and partially described byAdam Lynchathttps://adam-lynch.github.io/ked/.kedlangis an attempt at creating a lexer, parser and interpreter for Ked with a few additions and guesses to round out the language.DescriptionThe Ked interpreter can be installed usingpip.$pipinstallkedlangKed scripts are executed by passing them to the interpreter.$kedlangscript.kedDisclaimerThis is very much a work in progress, and as such is practically guaranteed to be riddled with all kinds of interesting and convoluted quirks and bugs. For the love of Cork, don't try to use this in production. Or in development. Or anywhere, really.NoteThis project has been set up using PyScaffold 3.2.3. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: kedm
kEDMkEDM (Kokkos-EDM) is a high-performance implementation of theEmpirical
Dynamical Modeling (EDM)framework. The goal of kEDM is to provide an optimized and parallelized
implementation of EDM algorithms for high-end CPUs and GPUs, while ensuring
compatibility with the original reference implementation
(cppEDM).Following EDM algorithms are currently implemented in kEDM:Simplex projection [1]Sequential Locally Weighted Global Linear Maps (S-Map) [2]Convergent Cross Mapping (CCM) [3]InstallationCPU (Linux and macOS)pip3 install kedmNVIDIA GPU (CUDA 11.2 or later)pip3 install kedm-11xNVIDIA GPU (CUDA 12.0 or later)pip3 install kedm-12xCitingPlease cite the following papers if you find kEDM useful:Keichi Takahashi, Kohei Ichikawa, Joseph Park, Gerald M. Pao, “Scalable Empirical Dynamic Modeling
with Parallel Computing and Approximate k-NN Search,” IEEE Access, vol. 11, pp. 68171–68183,
Jun. 2023.10.1109/ACCESS.2023.3289836Keichi Takahashi, Wassapon Watanakeesuntorn, Kohei Ichikawa, Joseph Park,
Ryousei Takano, Jason Haga, George Sugihara, Gerald M. Pao, "kEDM: A
Performance-portable Implementation of Empirical Dynamical Modeling," Practice
& Experience in Advanced Research Computing (PEARC 2021), Jul. 2021.10.1145/3437359.3465571ReferencesGeorge Sugihara, Robert May, "Nonlinear forecasting as a way of
distinguishing chaos from measurement error in time series," Nature, vol.
344, pp. 734–741,  1990.10.1038/344734a0George Sugihara, "Nonlinear forecasting for the classification of natural
time series. Philosophical Transactions," Physical Sciences and Engineering,
vol. 348, no. 1688, pp. 477–495, 1994.10.1098/rsta.1994.0106George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael
Fogarty, Stephan Munch, "Detecting Causality in Complex Ecosystems,"
Science, vol. 338, pp. 496–500, 2012.10.1126/science.1227079
Package: kedm-cuda11x
kEDMkEDM (Kokkos-EDM) is a high-performance implementation of theEmpirical
Dynamical Modeling (EDM)framework. The goal of kEDM is to provide an optimized and parallelized
implementation of EDM algorithms for high-end CPUs and GPUs, while ensuring
compatibility with the original reference implementation
(cppEDM).Following EDM algorithms are currently implemented in kEDM:Simplex projection [1]Sequential Locally Weighted Global Linear Maps (S-Map) [2]Convergent Cross Mapping (CCM) [3]InstallationCPU (Linux and macOS)pip3 install kedmNVIDIA GPU (CUDA 11.2 or later)pip3 install kedm-11xNVIDIA GPU (CUDA 12.0 or later)pip3 install kedm-12xCitingPlease cite the following papers if you find kEDM useful:Keichi Takahashi, Kohei Ichikawa, Joseph Park, Gerald M. Pao, “Scalable Empirical Dynamic Modeling
with Parallel Computing and Approximate k-NN Search,” IEEE Access, vol. 11, pp. 68171–68183,
Jun. 2023.10.1109/ACCESS.2023.3289836Keichi Takahashi, Wassapon Watanakeesuntorn, Kohei Ichikawa, Joseph Park,
Ryousei Takano, Jason Haga, George Sugihara, Gerald M. Pao, "kEDM: A
Performance-portable Implementation of Empirical Dynamical Modeling," Practice
& Experience in Advanced Research Computing (PEARC 2021), Jul. 2021.10.1145/3437359.3465571ReferencesGeorge Sugihara, Robert May, "Nonlinear forecasting as a way of
distinguishing chaos from measurement error in time series," Nature, vol.
344, pp. 734–741,  1990.10.1038/344734a0George Sugihara, "Nonlinear forecasting for the classification of natural
time series. Philosophical Transactions," Physical Sciences and Engineering,
vol. 348, no. 1688, pp. 477–495, 1994.10.1098/rsta.1994.0106George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael
Fogarty, Stephan Munch, "Detecting Causality in Complex Ecosystems,"
Science, vol. 338, pp. 496–500, 2012.10.1126/science.1227079
Package: kedm-cuda12x
kEDMkEDM (Kokkos-EDM) is a high-performance implementation of theEmpirical
Dynamical Modeling (EDM)framework. The goal of kEDM is to provide an optimized and parallelized
implementation of EDM algorithms for high-end CPUs and GPUs, while ensuring
compatibility with the original reference implementation
(cppEDM).Following EDM algorithms are currently implemented in kEDM:Simplex projection [1]Sequential Locally Weighted Global Linear Maps (S-Map) [2]Convergent Cross Mapping (CCM) [3]InstallationCPU (Linux and macOS)pip3 install kedmNVIDIA GPU (CUDA 11.2 or later)pip3 install kedm-11xNVIDIA GPU (CUDA 12.0 or later)pip3 install kedm-12xCitingPlease cite the following papers if you find kEDM useful:Keichi Takahashi, Kohei Ichikawa, Joseph Park, Gerald M. Pao, “Scalable Empirical Dynamic Modeling
with Parallel Computing and Approximate k-NN Search,” IEEE Access, vol. 11, pp. 68171–68183,
Jun. 2023.10.1109/ACCESS.2023.3289836Keichi Takahashi, Wassapon Watanakeesuntorn, Kohei Ichikawa, Joseph Park,
Ryousei Takano, Jason Haga, George Sugihara, Gerald M. Pao, "kEDM: A
Performance-portable Implementation of Empirical Dynamical Modeling," Practice
& Experience in Advanced Research Computing (PEARC 2021), Jul. 2021.10.1145/3437359.3465571ReferencesGeorge Sugihara, Robert May, "Nonlinear forecasting as a way of
distinguishing chaos from measurement error in time series," Nature, vol.
344, pp. 734–741,  1990.10.1038/344734a0George Sugihara, "Nonlinear forecasting for the classification of natural
time series. Philosophical Transactions," Physical Sciences and Engineering,
vol. 348, no. 1688, pp. 477–495, 1994.10.1098/rsta.1994.0106George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael
Fogarty, Stephan Munch, "Detecting Causality in Complex Ecosystems,"
Science, vol. 338, pp. 496–500, 2012.10.1126/science.1227079
Package: kedo-facebook-sdk
This client library is designed to support theFacebook Graph APIand the
officialFacebook JavaScript SDK, which is the canonical way to implement
Facebook authentication. You can read more about the Graph API by accessing itsofficial documentation.LicensingThis library uses theApache License, version 2.0. Please see the library’s
individual files for more information.Reporting IssuesIf you have bugs or other issues specifically pertaining to this library, file
themhere. Bugs with the Graph API should be filed onFacebook’s
bugtracker.Support & DiscussionDocumentation is available athttps://facebook-sdk.readthedocs.org/en/latest/.Have a question? Need help? Visit the library’sGoogle Group.
Package: kedo-oauthlib
A generic, spec-compliant, thorough implementation of the OAuth request-signing
logic for pythonOAuth often seems complicated and difficult-to-implement. There are several
prominent libraries for handling OAuth requests, but they all suffer from one or
both of the following:They predate theOAuth 1.0 spec, AKA RFC 5849.They predate theOAuth 2.0 spec, AKA RFC 6749.They assume the usage of a specific HTTP request library.OAuthLib is a generic utility which implements the logic of OAuth without
assuming a specific HTTP request object or web framework. Use it to graft OAuth
client support onto your favorite HTTP library, or provide support onto your
favourite web framework. If you’re a maintainer of such a library, write a thin
veneer on top of OAuthLib and get OAuth support for very little effort.DocumentationFull documentation is available onRead the Docs. All contributions are very
welcome! The documentation is still quite sparse, please open an issue for what
you’d like to know, or discuss it in ourG+ community, or even better, send a
pull request!Interested in making OAuth requests?Then you might be more interested in usingrequestswhich has OAuthLib
powered OAuth support provided by therequests-oauthliblibrary.Which web frameworks are supported?The following packages provide OAuth support using OAuthLib.For Django there isdjango-oauth-toolkit, which includesDjango REST frameworksupport.For Flask there isflask-oauthlibandFlask-Dance.For Pyramid there ispyramid-oauthlib.If you have written an OAuthLib package that supports your favorite framework,
please open a Pull Request, updating the documentation.Using OAuthLib? Please get in touch!Patching OAuth support onto an http request framework? Creating an OAuth
provider extension for a web framework? Simply using OAuthLib to Get Things Done
or to learn?No matter which we’d love to hear from you in ourG+ communityor if you have
anything in particular you would like to have, change or comment on don’t
hesitate for a second to send a pull request or open an issue. We might be quite
busy and therefore slow to reply but we love feedback!Chances are you have run into something annoying that you wish there was
documentation for, if you wish to gain eternal fame and glory, and a drink if we
have the pleasure to run into eachother, please send a docs pull request =)LicenseOAuthLib is yours to use and abuse according to the terms of the BSD license.
Check the LICENSE file for full details.ChangelogOAuthLib is in active development, with the core of both OAuth 1 and 2
completed, for providers as well as clients.Seesupported featuresfor
details.For a full changelog seeCHANGELOG.rst.
Package: kedro-accelerator
Kedro-AcceleratorKedro pipelines consist of nodes, where an output from one nodeAcan be an input to another nodeB. The Data Catalog defines where and how Kedro loads and saves these inputs and outputs, respectively. By default, a sequential Kedro pipeline:runs nodeApersists the output ofA, often to remote storage like Amazon S3potentially runs other nodesfetches the output ofA, loading it back into memoryruns nodeBPersisting intermediate data sets enables partial pipeline runs (e.g. running nodeBwithout rerunning nodeA) and analysis/debugging of these data sets. However, the I/O in steps 2 and 4 above was not necessary to run nodeB, given the requisite data was already in memory after step 1. Kedro-Accelerator speeds up pipelines by parallelizing this I/O in the background.How do I install Kedro-Accelerator?Kedro-Accelerator is a Python plugin. To install it:pipinstallkedro-acceleratorHow do I use Kedro-Accelerator?As of Kedro 0.16.4,TeePlugin—the core of Kedro-Accelerator—will be auto-discovered uponinstallation. In older versions,hook implementations should be registered with Kedro through theProjectContext. Hooks were introduced in Kedro 0.16.0.PrerequisitesThe following conditions must be true for Kedro-Accelerator to speed up your pipeline:Your project must use eitherSequentialRunnerorParallelRunner.ExampleThe Kedro-Accelerator repository includes the Iris data set example pipeline generated using Kedro 0.16.1. Intermediate data sets have been replaced with customSlowDataSetinstances to simulate a slow filesystem. You can try different load and save delays by modifyingcatalog.yml.To get started,create and activate a new virtual environment. Then, clone the repository and pip install requirements:gitclonehttps://github.com/deepyaman/kedro-accelerator.gitcdkedro-acceleratorKEDRO_VERSION=0.17.4pipinstall-rsrc/requirements.txt# Specify your desired Kedro version.You can compare pipeline execution times with and withoutTeePlugin. Kedro-Accelerator also providesCachePluginso that you can test performance usingCachedDataSetin asynchronous mode. Assuming parametrized load and save delays of 10 seconds for intermediate datasets, you should see the following results:StrategyCommandTotal timeLogBaseline (i.e. no caching/plugins)kedro run2 minutesLogTeePluginkedro run --hooks kedro_accelerator.plugins.TeePlugin10 seconds (saving all outputs)LogCachePlugin(i.e.CachedDataSet) withis_async=Truekedro run --async --hooks kedro_accelerator.plugins.CachePlugin30 seconds (savingsplit_data,train_model, andpredictnode outputs)LogPrior to Kedro version 0.17.0, prefix extra hooks passed tokedro runwithsrc.(e.g.src.kedro_accelerator.plugins.TeePlugin).For a more complete discussion of the above benchmarks, seequantumblacklabs/kedro#420 (comment).What license do you use?Kedro-Accelerator is licensed under theMITLicense.
Package: kedro-aim
Kedro Aim📝 Descriptionkedro-aimis akedro-pluginthat enables tracking of metrics and parameters withAimfrom within Kedro.
Kedro is a great tool for data engineering and data science, but it lacks a clear way to track metrics and parameters.
Aim is a great tool for tracking metrics and parameters, but it lacks a clear way to integrate with Kedro.
This plugin aims to solve both problems.🎖 FeaturesAutomatic Registration of AimRunin Data CatalogTracking of Artifact with Aim DataSetConfiguration overaim.yml⚙️ InstallationInstall the package withpip:pipinstallkedro-aim💡 Usage ExamplesThe plugin automatically registers aRuninstance in the DataCatalog under the namerunwhich can be accessed by all nodes to log metrics and parameters.
This run instance can be used track metrics and parameters in the same way as in any otherpython projectFirst you need to initilize theaim.ymlconfig file inside your pre-existing Kedro project.
This can be done by running the following command:kedroaiminitIn order to useaiminside a node you need to pass the run object as a argument of the function.
Inside the function you can access the run object and use it to log metrics and parameters.# nodes.pyimportpandasaspdfromaimimportRundeflogging_in_node(run:Run,data:pd.DataFrame)->None:# track metricrun.track(0.5,"score")# track parameterrun["parameter"]="abc"When defining the pipeline, you need to pass therundataset as a input to the node.
Therundataset will be automatically created bykedro-aimand added to the DataCatalog.
As a result, therundataset will be passed to the node as an argument.# pipeline.pyfromkedro.pipelineimportnode,Pipelinefromkedro.pipeline.modular_pipelineimportpipelinedefcreate_pipeline(**kwargs)->Pipeline:returnpipeline([node(func=logging_in_node,inputs=["run","input_data"],outputs=None,name="logging_in_node",)])🧰 Config FileThe module is configured via theaim.ymlfile which should be placed inside theconf/basefolder.
A default config file can be generated using thekedro aim initcommand from the shell.You can enable the schema validation in your VSCode IDE to enable real-time validation, autocompletion and see information about the different fields in your catalog as you write it. To enable this, make sure you have theYAML plugininstalled.
Then enter the following in yoursettings.jsonfile:{"yaml.schemas":{"https://raw.githubusercontent.com/AnH0ang/kedro-aim/master/static/jsonschema/kedro_aim_schema.json":"**/*aim*.yml"}}🙏 AcknowledgementThis project was inspired by the work ofkedro-mlflowwhich is a plugin for Kedro that enables tracking of metrics and parameters withMLflowfrom within Kedro.
Package: kedro-airflow-k8s
Kedro Airflow K8S PluginAboutThe main purpose of this plugin is to enable running kedro pipeline with Airflow on Kubernetes Cluster. In difference tokedro-airflowthis plugin does not require additional libraries installed
in airflow runtime, it uses K8S infrastructure instead. It supports translation
from Kedro pipeline DSL toairflow(python API)
and generation of airflowDAGs.The plugin can be used together withkedro-dockerto simplify preparation of docker image for pipeline execution.DocumentationFor detailed documentation refer tohttps://kedro-airflow-k8s.readthedocs.io/Usage guideUsage: kedro airflow-k8s [OPTIONS] COMMAND [ARGS]...
 
Options:
  -e, --env TEXT  Environment to use.
  -p, --pipeline TEXT  Pipeline name to pick.
  -h, --help      Show this message and exit.

Commands:
  compile          Create an Airflow DAG for a project
  init             Initializes configuration for the plugin
  list-pipelines   List pipelines generated by this plugin
  run-once         Uploads pipeline to Airflow and runs once
  schedule         Uploads pipeline to Airflow with given schedule
  ui               Open Apache Airflow UI in new browser tab
  upload-pipeline  Uploads pipeline to Airflow DAG location
Package: kedro-alpiq
kedro-alpiqkedro-alpiq is a part ofAlpiq.
This public copy of the package
is a stub published on pypi.org designed to shield Alpiq users fromdependency confusion,
a remote code execution attack publicly disclosed in 2021 by Alex Birsan.If you are a Alpiq user, you will only see this public package if your
app is not properly configured to find the local package repository built into
Alpiq. Visit the docs for your Alpiq installation for details.If you never see this public package, it's still doing its job of protecting you
from the dependency confusion attack!
Package: kedro-argo
Converting kedro pipelines to argo pipelines.Free software: BSD 3-Clause LicenseInstallationpip install kedro-argoYou can also install the in-development version with:pip install https://github.com/nraw/kedro-argo/archive/master.zipRequirementsTo be used with Kedro, so it’s assumed this package is used with a Kedro project.Argo CLI is needed for the deployment step. It’s also assumed that Argo is already installed on your kuberentes instance.You must specify an image name as a parameter. You can generate the image using Kedro-docker.As the workflow will be in Argo, which means every step will run in its own container. Therefore, all datasets should be somehow passed between containers or else the pipeline will fail. This means either all datasets should be saved externally (S3, Azure, etc.) or in a shared folder that your deployment would have access to.UsageWhen installed, argo should be visible under your kedro commands, if you’re in a kedro projectkedroThen you have two options for obtaining the yaml file, namely via Helm or via ytt.Helmkedro argo IMAGE_NAMEAdd this repository to your helm charts:helm repo add kedro-argo https://nraw.github.io/kedro-argo-helm/Then either directly install it by passing the kedro.yaml for input valueshelm install -f templates/kedro.yaml kedro-argo kedro-argo/kedro-argoOr clone it to your repository and change anything that you would still need:helm pull kedro-argo/kedro-argo --untaryttGet the kedro.yaml file by runningkedro argo --ytt IMAGE_NAMEYou can now run:ytt -f templates > argo.yamlor if you prefer in Docker:docker run --rm -it --name ytt -v $(pwd)/templates:/templates gerritk/ytt:latest -f /templates > argo.yamland finallyargo submit --watch argo.yamlDocumentationhttps://kedro-argo.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.8 (2020-04-27)Changed the ytt option to be a flag0.0.7 (2020-03-27)Changed the default templating option to be Helm instead of ytt0.0.5 (2020-03-08)Dirty names are now transformed to make less likely that symbols break Argo0.0.4 (2020-03-07)Refactoring and initial adaptationInclusion of tests0.0.0 (2020-03-07)First release on PyPI.
Package: kedro-auto-catalog
Kedro Auto CatalogA configurable version of the built inkedro catalog createcli. Default
types can be configured in the projects settings.py, to get these types rather
thanMemoryDataSets.Table of ContentsInstallationLicenseInstallationpip install kedro-auto-catalogConfigurationConfigure the project defaults insrc/<project_name>/settings.pywith this
dict.AUTO_CATALOG={"directory":"data","subdirs":["raw","intermediate","primary"],"layers":["raw","intermediate","primary"],"default_extension":"parquet","default_type":"pandas.ParquetDataSet",}UsageTo auto create catalog entries for the__default__pipeline, run this from the command line.kedroauto-catalog-p__default__If you want a reminder of what to do, use the--help.❯kedroauto-catalog--help❯
Usage:kedroauto-catalog[OPTIONS]CreateDataCatalogYAMLconfigurationwithmissingdatasets.AddconfigurabledatasetstoDataCatalogYAMLconfigurationfileforeachdatasetinaregisteredpipelineifitismissingfromthe`DataCatalog`.Thecatalogconfigurationwillbesavedto`<conf_source>/<env>/catalog/<pipeline_name>.yml`file.Configuretheprojectdefaultsin`src/<project_name>/settings.py`withthisdict.

Options:-e,--envTEXTEnvironmenttocreateDataCatalogYAMLfilein.Defaultsto`base`.-p,--pipelineTEXTNameofapipeline.[required]-h,--helpShowthismessageandexit.ExampleUsing thekedro-spaceflightsexample, runningkedro auto-catalog -p __default__yields the following
catalog inconf/base/catalog/__default__.ymlX_test:filepath:data/X_test.pqtype:pandas.ParquetDataSetX_train:filepath:data/X_train.pqtype:pandas.ParquetDataSety_test:filepath:data/y_test.parquettype:pandas.ParquetDataSety_train:filepath:data/y_train.parquettype:pandas.ParquetDataSetsubdirs and layersIf we use the example configuration with"subdirs": ["raw", "intermediate", "primary"]and"layers": ["raw", "intermediate", "primary"], it will convert
any leading subdir/layer in your dataset name into a directory. If we change y_test
toraw_y_test, it will puty_test.parquetin therawdirectory, and in the raw layer.raw_y_test:
  filepath: data/raw/y_test.parquet
  layer: raw
  type: pandas.ParquetDataSetLicensekedro-auto-catalogis distributed under the terms of theMITlicense.
Package: kedro-azureml
Kedro Azure ML Pipelines pluginWe help companies turn their data into assetsAboutFollowing plugin enables running Kedro pipelines on Azure ML Pipelines service.We support 2 native Azure Machine Learning types of workflows:For Data Scientists: fast, iterative development with code uploadFor MLOps: stable, repeatable workflows with DockerDocumentationFor detailed documentation refer tohttps://kedro-azureml.readthedocs.io/Usage guideUsage: kedro azureml [OPTIONS] COMMAND [ARGS]...

Options:
  -e, --env TEXT  Environment to use.
  -h, --help      Show this message and exit.

Commands:
  compile  Compiles the pipeline into YAML format
  init     Creates basic configuration for Kedro AzureML plugin
  run      Runs the specified pipeline in Azure ML PipelinesQuickstartFollowquickstartsection onkedro-azureml.readthedocs.ioto get up to speed with plugin usage or watch the video below