Saravanan666/pypi-org huggingface dataset:
Package: apache-ensite
apache-ensiteApache 2 configuration file management a la debianWhy?Having recently started movign away from Ubuntu I found that their way of managing apache configuration
files still made sense, so I wrote this small bit of python to allow me to have a2ensite and a2dissite
available on a FreeBSD system.Huh?The basic theory is to have a sites-available directory where you create the configuration files. When you
want to actually use the configurations you enable them by creating a symlink from the sites-available
directory to the sites-enabled directory. When you have finished using the configuration file, you simply
remove the symlink. It makes things a lot easier and using these 2 small scripts make things even simpler.How?When you first install Apache on FreeBSD there are no sites-available or sites-enabled directories and the default configuration lacks the required directive, so either create the directories manually and then edit the httpd.conf file, or run$ sudo a2ensite --setupAfter creating a configuration file, e.g. blah.conf, you can enable it by$ sudo a2ensite blahWhen finished with the configuration file, e.g. blah.conf, simply run$ sudo a2dissite blahIf you can't remember the filename simply run with no name (or use the --list command) and a list will be displayed.$ a2ensite --list

*** You are not running with root privileges, so changes may not be possible.


apache24 installation in /usr/local/etc/apache24

  Configuration File                       Enabled?
  ======================================== ==========
  000-default.conf                         True
  svn.conf                                 FalseNotesMore than one file can specified at once.Matches are done using fnmatch so standard wildcards are available.
Package: apache-flink
Apache FlinkApache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.Learn more about Flink athttps://flink.apache.org/Python PackagingPyFlink is a Python API for Apache Flink that allows you to build scalable batch and streaming workloads,
such as real-time data processing pipelines, large-scale exploratory data analysis, Machine Learning (ML)
pipelines and ETL processes. If you’re already familiar with Python and libraries such as Pandas,
then PyFlink makes it simpler to leverage the full capabilities of the Flink ecosystem.
Depending on the level of abstraction you need, there are two different APIs that can be used in PyFlink: PyFlink Table API and PyFlink DataStream API.The PyFlink Table API allows you to write powerful relational queries in a way that is similar to
using SQL or working with tabular data in Python. You can find more information about it via the tutorialhttps://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/table_api_tutorial/The PyFlink DataStream API gives you lower-level control over the core building blocks of Flink,
state and time, to build more complex stream processing use cases.
Tutorial can be found athttps://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/datastream_tutorial/You can find more information via the documentation athttps://nightlies.apache.org/flink/flink-docs-stable/docs/dev/python/overview/The auto-generated Python docs can be found athttps://nightlies.apache.org/flink/flink-docs-stable/api/python/Python RequirementsApache Flink Python API depends on Py4J (currently version 0.10.9.7), CloudPickle (currently version 2.2.0), python-dateutil (currently version >=2.8.0,<3), Apache Beam (currently version >=2.43.0,<2.49.0).Development NoticesProtobuf Code GenerationProtocol buffer is used in fileflink_fn_execution_pb2.pyand the file is generated fromflink-fn-execution.proto. Wheneverflink-fn-execution.protois updated, please re-generateflink_fn_execution_pb2.pyby executing:python pyflink/gen_protos.pyPyFlink depends on the following libraries to execute the above script:grpcio-tools (>=1.29.0,<=1.48.2)setuptools (>=37.0.0)pip (>=20.3)Running Test CasesCurrently, we use conda and tox to verify the compatibility of the Flink Python API for multiple versions of Python and will integrate some useful plugins with tox, such as flake8.
We can enter the directory where this README.md file is located and run test cases by executing./dev/lint-python.shTo use your system conda environment, you can setFLINK_CONDA_HOMEvariable:exportFLINK_CONDA_HOME=$(dirname$(dirname$CONDA_EXE))Create a virtual environment:condacreate-npyflink_38python=3.8Then you can activate your environment and run tests, for example:condaactivatepyflink_38
pipinstall-r./dev/dev-requirements.txt
./dev/lint-python.sh
Package: apache-flink-ml
Flink ML is a library which provides machine learning (ML) APIs and infrastructures that simplify the building of ML pipelines. Users can implement ML algorithms with the standard ML APIs and further use these infrastructures to build ML pipelines for both training and inference jobs.Flink ML is developed under the umbrella ofApache Flink.Python PackagingPrerequisites for building apache-flink-ml:Unix-like environment (we use Linux, Mac OS X)Python version(3.7 or 3.8) is requiredThen go to the root directory of flink-ml-python source code and run this command to build the sdist package of apache-flink-ml:cdflink-ml-python;pythonsetup.pysdist;The sdist package of apache-flink-ml will be found under ./flink-ml-python/dist/. It could be used for installation, such as:python-mpipinstalldist/*.tar.gz
Package: apache-libcloud
Apache Libcloud - a unified interface for the cloudApache Libcloud is a Python library which hides differences between different
cloud provider APIs and allows you to manage different cloud resources
through a unified and easy to use API.Code:https://github.com/apache/libcloudLicense:Apache 2.0; see LICENSE fileIssues:https://issues.apache.org/jira/projects/LIBCLOUD/issuesWebsite:https://libcloud.apache.org/Documentation:https://libcloud.readthedocs.ioSupported Python Versions:Python >= 3.7, PyPy >= 3.7, Python 3.10 + Pyjion
(Python 2.7 and Python 3.4 is supported by the
v2.8.x release series, last version which supports
Python 3.5 is v3.4.0 and v3.6.x for Python 3.6)Resources you can manage with Libcloud are divided into the following categories:Compute- Cloud Servers and Block Storage - services such as Amazon EC2 and Rackspace
Cloud Servers (libcloud.compute.*)Storage- Cloud Object Storage and CDN  - services such as Amazon S3 and Rackspace
CloudFiles (libcloud.storage.*)Load Balancers- Load Balancers as a Service, LBaaS (libcloud.loadbalancer.*)DNS- DNS as a Service, DNSaaS (libcloud.dns.*)Container- Container virtualization services (libcloud.container.*)Apache Libcloud is an Apache project, see <http://libcloud.apache.org> for
more information.DocumentationDocumentation can be found at <https://libcloud.readthedocs.org>.Note on Python Version CompatibilityLibcloud supports Python >= 3.7 and PyPy >= 3.7.Support for Python 3.6 has been dropped in v3.7.0 release.
Last release series which supports Python 3.6 is v3.6.x.Support for Python 3.5 has been dropped in v3.5.0 release.Last release series which supports Python 3.5 is v3.4.x.Support for Python 2.7 and 3.4 has been dropped in Libcloud v3.0.0 (last
release series which support Python 2.7 and Python 3.4 is v2.8.x).FeedbackPlease send feedback to the mailing list at <dev@libcloud.apache.org>,
or Github repo at <https://github.com/apache/libcloud/issues>.ContributingFor information on how to contribute, please see the Contributing
chapter in our documentation
<https://libcloud.readthedocs.org/en/latest/development.html#contributing>.WebsiteSource code for the website is available at
<https://github.com/apache/libcloud-site>.LicenseApache Libcloud is licensed under the Apache 2.0 license. For more information,
please seeLICENSEandNOTICEfile.SecurityThis is a project of theApache Software Foundationand
follows the ASFvulnerability handling process.Reporting a VulnerabilityTo report a new vulnerability you have discovered please follow theASF vulnerability reporting process.
Package: apache-license-check
A command line tool for checking if the source files of a Python project have
Apache License headers.Installpipinstallapache-license-checkUsageTo check all Python files recursively under the current directory:apache-license-checkOptionally, pass one or more files or directories to check:apache-license-checksetup.pymyproject/You can also check that files include a copyright statement with the--copyrightflag. Pass a substring (such as the name of the copyright
holder) to check:apache-license-checksetup.py--copyright"Faculty Science Limited"
Package: apachelogs
GitHub|PyPI|Documentation|Issues|Changelogapachelogsparses Apache access log files.  Pass it alog format stringand get back a
parser for logfile entries in that format.apachelogseven takes care of
decoding escape sequences and converting things like timestamps, integers, and
bare hyphens todatetimevalues,ints, andNones.Installationapachelogsrequires Python 3.5 or higher.  Just usepipfor Python 3 (You have pip, right?) to installapachelogsand its dependencies:python3 -m pip install apachelogsExamplesParse a single log entry:>>> from apachelogs import LogParser
>>> parser = LogParser("%h %l %u %t \"%r\" %>s %b \"%{Referer}i\" \"%{User-Agent}i\"")
>>> # The above log format is also available as the constant `apachelogs.COMBINED`.
>>> entry = parser.parse('209.126.136.4 - - [01/Nov/2017:07:28:29 +0000] "GET / HTTP/1.1" 301 521 "-" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36"\n')
>>> entry.remote_host
'209.126.136.4'
>>> entry.request_time
datetime.datetime(2017, 11, 1, 7, 28, 29, tzinfo=datetime.timezone.utc)
>>> entry.request_line
'GET / HTTP/1.1'
>>> entry.final_status
301
>>> entry.bytes_sent
521
>>> entry.headers_in["Referer"] is None
True
>>> entry.headers_in["User-Agent"]
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36'
>>> # Log entry components can also be looked up by directive:
>>> entry.directives["%r"]
'GET / HTTP/1.1'
>>> entry.directives["%>s"]
301
>>> entry.directives["%t"]
datetime.datetime(2017, 11, 1, 7, 28, 29, tzinfo=datetime.timezone.utc)Parse a file full of log entries:>>> with open('/var/log/apache2/access.log') as fp:  # doctest: +SKIP
...     for entry in parser.parse_lines(fp):
...         print(str(entry.request_time), entry.request_line)
...
2019-01-01 12:34:56-05:00 GET / HTTP/1.1
2019-01-01 12:34:57-05:00 GET /favicon.ico HTTP/1.1
2019-01-01 12:34:57-05:00 GET /styles.css HTTP/1.1
# etc.
Package: apachemiddleware
ContentsSummaryGet StartedAuthorChanges0.1.10.1.0LicenseDownloadSummaryUseful Python middleware for use with mod_wsgi deploymentsGet StartedDownload and install from sourceAuthorJames GardnerChanges0.1.12011-11-02Changed package name to lowercaseapachemiddlewareLicense change to MITDocumentation fixesMoved to git0.1.02009-12-23First versionLicenseApacheMiddleware - Useful Python middleware for use with mod_wsgi deployments
Copyright (C) 2009 James GardnerPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.Download
Package: apache-replay
apache-replay python packageSummaryMany pieces of software exist that can take an existing Apache httpd (or other)
logs in the Common Log format or Combined log format, parse them, and then do
something with them.   I find it convenient to be able to do this with pure
Python, using a package that can be installed from pip.FeaturesReplay requests against a new serverCount log entriesFilter based on start and end datesPrint logs with a new canonical server URLInstallationInstallation is normal:pip install apache-replayUsageGenerate usage with:apache-replay --helpExamplesCount the number of requests in November of 2018:apache-replay --player count https://site.com/ /var/logs/httpd/access_log.2018-11*Replay those same logs (only GET, HEAD, OPTIONS) against qa-mysite.com:apache-replay https://qasite.com/ /var/logs/httpd/access_log.2018-11*Only replay 2000 log entries from that fileapache-replay --count 2000 https://qasite.com/ /var/logs/httpd/access_log.2018-11*LicenseMIT License
Package: apache-sedona
Apache SedonaThis library is the Python wrapper for Apache Sedona.Apache Sedona is a cluster computing system for processing large-scale spatial data.
Apache Sedona extends Apache Spark / SparkSQL with a set of out-of-the-box Spatial Resilient Distributed Datasets (SRDDs)/ SpatialSQL that efficiently load, process, and analyze large-scale spatial data across machines.Please visit the official Apache Sedona website:https://sedona.apache.org/Development environment setup docs:https://sedona.apache.org/latest-snapshot/setup/compile/#run-python-test
Package: apache-sedona-beta
Apache SedonaThis library is the Python wrapper for Apache Sedona.Apache Sedona is a cluster computing system for processing large-scale spatial data.
Apache Sedona extends Apache Spark / SparkSQL with a set of out-of-the-box Spatial Resilient Distributed Datasets (SRDDs)/ SpatialSQL that efficiently load, process, and analyze large-scale spatial data across machines.Please visit the official Apache Sedona website:https://sedona.apache.org/Apache Sedona is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
Package: apache-skywalking
SkyWalking Python AgentSkyWalking-Python: The Python Agent for Apache SkyWalking provides the native tracing/metrics/logging/profiling abilities for Python projects.SkyWalking: Application performance monitor tool for distributed systems, especially designed for microservices, cloud native and container-based (Kubernetes) architectures.DocumentationOfficial documentationBlogabout the Python Agent Profiling FeatureCapabilitiesReporterSupported?DetailsTrace✅ (default: ON)Automatic instrumentation + Manual SDKLog✅ (default: ON)Direct reporter only. (Tracing context in log planned)Meter✅ (default: ON)Meter API + Automatic PVM metricsEvent❌ (Planned)Report lifecycle events of your awesome Python applicationProfiling✅ (default: ON)Threading and Greenlet ProfilerInstallation RequirementsSkyWalking Python Agent requiresApache SkyWalking 8.0+and Python 3.7+.If you would like to try out the latest features that are not released yet, please refer to thisguideto build from sources.Live DemoFind thelive demowith Python agent on our website.Follow theshowcaseto set up preview deployment quickly.ContributingBefore submitting a pull request or pushing a commit, please read ourcontributinganddeveloper guide.Contact UsMail list:dev@skywalking.apache.org. Mail todev-subscribe@skywalking.apache.org, follow the reply to subscribe the mail list.SendRequest to join SkyWalking slackmail to the mail list(dev@skywalking.apache.org), we will invite you in.Twitter,ASFSkyWalkingQQ Group: 901167865, 392443393bilibili B站 视频LicenseApache 2.0
Package: apache-submarine
PySubmarinePySubmarine is aiming to ease the ML engineer's life by providing a set of libraries.It includes a high-level out-of-box ML library like deepFM, FM, etc.
low-level library to interact with submarine like creating experiment,
tracking experiment metrics, parameters.Package setupInstall latest version of pysubmarinegitclonehttps://github.com/apache/submarine.gitcdsubmarine/submarine-sdk/pysubmarine
pipinstall.# install extras_require## bashpipinstall-e.[tf2,pytorch]## zshpipinstall-e".[tf2,pytorch]"Install package from pypipipinstallapache-submarineEasy-to-use model trainersFMDeepFMSubmarine experiment managementMakes it easy to run distributed or non-distributed TensorFlow, PyTorch experiments on Kubernetes.mnist exampleDevelopmentSeePython Developmentin the documentation subproject.
Package: apache-superset-11680
SupersetA modern, enterprise-ready business intelligence web application.Why Superset?|Supported Databases|Installation and Configuration|Release Notes|Get Involved|Contributor Guide|Resources|Organizations Using SupersetScreenshots & GifsGalleryView DashboardsSlice & dice your dataQuery and visualize your data with SQL LabVisualize geospatial data with deck.glChoose from a wide array of visualizationsWhy Superset?Superset provides:An intuitive interface for visualizing datasets and
crafting interactive dashboardsA wide array of beautiful visualizations to showcase your dataCode-free visualization builder to extract and present datasetsA world-class SQL IDE for preparing data for visualization, including a rich metadata browserA lightweight semantic layer which empowers data analysts to quickly define custom dimensions and metricsOut-of-the-box support for most SQL-speaking databasesSeamless, in-memory asynchronous caching and queriesAn extensible security model that allows configuration of very intricate rules
on who can access which product features and datasets.Integration with major
authentication backends (database, OpenID, LDAP, OAuth, REMOTE_USER, etc)The ability to add custom visualization pluginsAn API for programmatic customizationA cloud-native architecture designed from the ground up for scaleSupported DatabasesSuperset can query data from any SQL-speaking datastore or data engine (e.g. Presto or Athena) that has a Python DB-API driver and a SQLAlchemy dialect.Here are some of the major database solutions that are supported:A more comprehensive list of supported databasesalong with the configuration instructions can be foundhere.Want to add support for your datastore or data engine? Read morehereabout the technical requirements.Installation and ConfigurationExtended documentation for SupersetGet InvolvedAsk and answer questions onStackOverflowusing theapache-supersettagJoin our community's Slackand please read ourSlack Community GuidelinesJoin our dev@superset.apache.org Mailing listContributor GuideInterested in contributing? Check out ourCONTRIBUTING.mdto find resources around contributing along with a detailed guide on
how to set up a development environment.ResourcesSuperset 1.0Superset 1.0 MilestoneSuperset 1.0 Release NotesGetting Started with SupersetSuperset in 2 Minutes using Docker ComposeInstalling Database DriversBuilding New Database ConnectorsCreate Your First DashboardDocumentation for Superset End-Users (by Preset)Deploying SupersetOfficial Docker imageHelm ChartRecordings of Past Community EventsMeetup: Superset 1.0Live Demo: Interactive Time-series Analysis with Druid and SupersetLive Demo: Visualizing MongoDB and Pinot Data using TrinoUpcoming Superset EventsSuperset + Star Wars: May the 4th Be With YouMeetup - Developing and Deploying Custom Visualization Plugins in SupersetVisualize Your Data Lake Using Athena and SupersetVisualizationsBuilding Custom Viz PluginsManaging and Deploying Custom Viz PluginsWhy Apache Superset is Betting on Apache EChartsSuperset API
Package: apache-superset-11680-1000
SupersetA modern, enterprise-ready business intelligence web application.Why Superset?|Supported Databases|Installation and Configuration|Release Notes|Get Involved|Contributor Guide|Resources|Organizations Using SupersetScreenshots & GifsGalleryView DashboardsSlice & dice your dataQuery and visualize your data with SQL LabVisualize geospatial data with deck.glChoose from a wide array of visualizationsWhy Superset?Superset provides:An intuitive interface for visualizing datasets and
crafting interactive dashboardsA wide array of beautiful visualizations to showcase your dataCode-free visualization builder to extract and present datasetsA world-class SQL IDE for preparing data for visualization, including a rich metadata browserA lightweight semantic layer which empowers data analysts to quickly define custom dimensions and metricsOut-of-the-box support for most SQL-speaking databasesSeamless, in-memory asynchronous caching and queriesAn extensible security model that allows configuration of very intricate rules
on who can access which product features and datasets.Integration with major
authentication backends (database, OpenID, LDAP, OAuth, REMOTE_USER, etc)The ability to add custom visualization pluginsAn API for programmatic customizationA cloud-native architecture designed from the ground up for scaleSupported DatabasesSuperset can query data from any SQL-speaking datastore or data engine (e.g. Presto or Athena) that has a Python DB-API driver and a SQLAlchemy dialect.Here are some of the major database solutions that are supported:A more comprehensive list of supported databasesalong with the configuration instructions can be foundhere.Want to add support for your datastore or data engine? Read morehereabout the technical requirements.Installation and ConfigurationExtended documentation for SupersetGet InvolvedAsk and answer questions onStackOverflowusing theapache-supersettagJoin our community's Slackand please read ourSlack Community GuidelinesJoin our dev@superset.apache.org Mailing listContributor GuideInterested in contributing? Check out ourCONTRIBUTING.mdto find resources around contributing along with a detailed guide on
how to set up a development environment.ResourcesSuperset 1.0Superset 1.0 MilestoneSuperset 1.0 Release NotesGetting Started with SupersetSuperset in 2 Minutes using Docker ComposeInstalling Database DriversBuilding New Database ConnectorsCreate Your First DashboardDocumentation for Superset End-Users (by Preset)Deploying SupersetOfficial Docker imageHelm ChartRecordings of Past Community EventsMeetup: Superset 1.0Live Demo: Interactive Time-series Analysis with Druid and SupersetLive Demo: Visualizing MongoDB and Pinot Data using TrinoUpcoming Superset EventsSuperset + Star Wars: May the 4th Be With YouMeetup - Developing and Deploying Custom Visualization Plugins in SupersetVisualize Your Data Lake Using Athena and SupersetVisualizationsBuilding Custom Viz PluginsManaging and Deploying Custom Viz PluginsWhy Apache Superset is Betting on Apache EChartsSuperset API