Saravanan666/pypi-org huggingface dataset:
Package: openvino-optimum
OpenVINO™ Integration with Optimum*This module is an extension forOptimum*library which bringsOpenVINO™backend forHugging Face Transformers*:hugs:.This project provides multiple APIs to enable different tools:OpenVINO RuntimeNeural Network Compression Framework (NNCF)InstallInstall only runtime:pipinstallopenvino-optimumor with all dependencies (nncfandopenvino-dev):pipinstallopenvino-optimum[all]OpenVINO RuntimeThis module provides an inference API for Hugging Face models. There are options to use models with PyTorch*, TensorFlow* pretrained weights or use native OpenVINO IR format (a pair of filesov_model.xmlandov_model.bin).To use OpenVINO backend, import one of theAutoModelclasses withOVprefix. Specify a model name or local path infrom_pretrainedmethod.fromoptimum.intel.openvinoimportOVAutoModel# PyTorch trained model with OpenVINO backendmodel=OVAutoModel.from_pretrained(<name_or_path>,from_pt=True)# TensorFlow trained model with OpenVINO backendmodel=OVAutoModel.from_pretrained(<name_or_path>,from_tf=True)# Initialize a model from OpenVINO IRmodel=OVAutoModel.from_pretrained(<name_or_path>)NNCFNNCFis used for model training with applying such features like quantization, pruning. To enable NNCF in your training pipeline do the following steps:ImportNNCFAutoConfig:fromoptimum.intel.nncfimportNNCFAutoConfigNOTE:NNCFAutoConfigmust be imported beforetransformersto make magic workInitialize a config from.jsonfile:nncf_config=NNCFAutoConfig.from_json(training_args.nncf_config)Pass a config toTrainerobject. In example,model=AutoModelForQuestionAnswering.from_pretrained(<name_op_path>)...trainer=QuestionAnsweringTrainer(model=model,args=training_args,train_dataset=train_datasetiftraining_args.do_trainelseNone,eval_dataset=eval_datasetiftraining_args.do_evalelseNone,eval_examples=eval_examplesiftraining_args.do_evalelseNone,tokenizer=tokenizer,data_collator=data_collator,post_process_function=post_processing_function,compute_metrics=compute_metrics,nncf_config=nncf_config,)NNCF module is independent from the Runtime module somodelclass should not be wrapped to one ofOVAutoModelclasses.Trainingexamplescan be found in Transformers library.
NNCF configs are published inconfigfolder. Add--nncf_configwith a path to corresponding config when training your model. More command line exampleshere.python examples/pytorch/token-classification/run_ner.py --model_name_or_path bert-base-cased --dataset_name conll2003 --output_dir bert_base_cased_conll_int8 --do_train --do_eval --save_strategy epoch --evaluation_strategy epoch --nncf_config nncf_bert_config_conll.jsonTo use the NNCF component, install the package with[nncf]or[all]extras:pipinstallopenvino-optimum[nncf]See theChangelogpage for details about module development.*Other names and brands may be claimed as the property of others.
Package: openvino-python
This project moved from openvino-python toopenvino, and 2021.1 version to be removed in the default view. The specific version is still available for users depending on this exact version by using openvino-python==2021.1.OpenVINO™ ToolkitOpenVINO™ toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNNs), the toolkit extends computer vision (CV) workloads across Intel® hardware, maximizing performance. The OpenVINO™ toolkit includes the Deep Learning Deployment Toolkit (DLDT).OpenVINO™ toolkit:Enables CNN-based deep learning inference on the edgeSupports heterogeneous execution across an Intel® CPU, Intel® Integrated Graphics, Intel® FPGA,  Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ VPUsSpeeds time-to-market via an easy-to-use library of computer vision functions and pre-optimized kernelsIncludes optimized calls for computer vision standards, including OpenCV* and OpenCL™Operating Systems:Ubuntu* 20.04 long-term support (LTS), 64-bitInstall the Runtime Package Using the PyPI RepositorySet up and update pip to the highest version:python3-mpipinstall--upgradepipInstall the Intel® distribution of OpenVINO™ toolkit:pipinstallopenvino-pythonAdd PATH to environment variables.Ubuntu* and macOS*:exportLD_LIBRARY_PATH=<library_dir>:${LD_LIBRARY_PATH}Windows* 10:setPATH=<library_dir>;%PATH%How to findlibrary_dir:Ubuntu*, macOS*:standard user:echo$(python3-msite--user-base)/libroot or sudo user:/usr/local/libvirtual environments or custom Python installations (from sources or tarball):echo$(whichpython3)/../../libWindows*:standard Python:python-c"import os, sys; print((os.path.dirname(sys.executable))+'\Library\\bin')"virtual environments or custom Python installations (from sources or tarball):python-c"import os, sys; print((os.path.dirname(sys.executable))+'\..\Library\\bin')"Verify that the package is installed:python3-c"import openvino"Now you are ready to develop and run your application.
Package: openvino-telemetry
OpenVINO™ TelemetryThe implementation of the Python 3 library to send the telemetry data from the OpenVINO™ toolkit components.To send the data to Google Analytics, use the following three variables:category,action, andlabel.In thecategory, use only the name of the tool. Place all Model Optimizer (MO) topics in the 'mo' category, all Post-Training Optimization Tool (POT) topics in the 'pot' category.In theaction, send a metric or a function, such as accuracy, session, conversion, and others. For example, for MO use: version, framework, conversion_results.In thelabel, send more detailed data for your action (function). For example, send a string with the version name for the version or a string with the framework name for the framework. You can send a string with a wrapped dictionary: "{param: value, version: value, error: value}".NOTE:If you want to track the connection between data (for example, on which operating systems error '123' occurred), send the data in one event and add it to the label together. Example: telemetry.send_event("mo", "error_info", "{os:ubuntu18, error:123}"). If you send the data separately, you will not be able to identify the connection. Some data will be duplicated since the same metric/function will be sent in different events.NOTE:Sending of telemetry data requires user's consent during installation of OpenVINO™ toolkit component. In case if control file does not exist on the system or it contains a "no" answer, no data will be transmitted.TIP:To help automate the analytics, always sendallthe keys for a dictionary in thelabelvariable. If a key is empty, send 'none' as its value.
Package: openvino-ubuntu20
OpenVINO™ ToolkitOpenVINO™ toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNNs), the toolkit extends computer vision (CV) workloads across Intel® hardware, maximizing performance. The OpenVINO™ toolkit includes the Deep Learning Deployment Toolkit (DLDT).OpenVINO™ toolkit:Enables CNN-based deep learning inference on the edgeSupports heterogeneous execution across an Intel® CPU, Intel® Integrated Graphics, Intel® FPGA,  Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ VPUsSpeeds time-to-market via an easy-to-use library of computer vision functions and pre-optimized kernelsIncludes optimized calls for computer vision standards, including OpenCV* and OpenCL™Operating Systems:Ubuntu* 20.04 long-term support (LTS), 64-bitInstall the Runtime Package Using the PyPI RepositorySet up and update pip to the highest version:python3-mpipinstall--upgradepipInstall the Intel® distribution of OpenVINO™ toolkit:pipinstallopenvino-ubuntu20Add PATH to environment variables.Ubuntu* and macOS*:exportLD_LIBRARY_PATH=<library_dir>:${LD_LIBRARY_PATH}Windows* 10:setPATH=<library_dir>;%PATH%How to findlibrary_dir:Ubuntu*, macOS*:standard user:echo$(python3-msite--user-base)/libroot or sudo user:/usr/local/libvirtual environments or custom Python installations (from sources or tarball):echo$(whichpython3)/../../libWindows*:standard Python:python-c"import os, sys; print((os.path.dirname(sys.executable))+'\Library\\bin')"virtual environments or custom Python installations (from sources or tarball):python-c"import os, sys; print((os.path.dirname(sys.executable))+'\..\Library\\bin')"Verify that the package is installed:python3-c"import openvino"Now you are ready to develop and run your application.
Package: openvk-api
OpenVk-APIRaw API forOpenVKpip install openvk-apiUseOpenVk Docsto useOpenVkApiMethodQuick examplefromopenvk_apiimportOpenVkClient# Use tokenclient=OpenVkClient(token="sdflksdfkljssldkjgsdg-jill")# Use login and passwordclient=OpenVkClient()client=client.auth_with_password(login="fckptn@gg.su",password="Hydra")api=client.get_api()print(api.messages.send(user_id=10484,message="Hello, developer"))LinksDev's OpenVk.uk profile
Package: openvoid
OpenVoid Python ClientThe OpenVoid Python Client facilitates interaction with the OpenVoid AI API, which is currently in closed beta. This client enables developers to easily incorporate AI functionalities into Python applications, utilizing the OpenVoid AI platform's robust features.FeaturesEasy integration with the OpenVoid AI API in closed beta.Simplified API response handling.Comprehensive examples to jumpstart your application development.Getting StartedPrerequisitesMake sure Python 3.6 or later is installed on your system before installing the OpenVoid Python client.InstallingTo install the OpenVoid Python client via pip, execute:pipinstallopenvoidThis command installs the OpenVoid client and all necessary dependencies.Installing from SourceFor source installations,poetryis used for dependency management and packaging:pipinstallpoetryAfter installing poetry, clone the OpenVoid client repository:gitclonehttps://github.com/openvoidai/openvoid-python.gitcdopenvoid-pythonThen, install the package and its dependencies:poetryinstallPoetry will create a virtual environment and manage all dependencies for you.ConfigurationAPI Key Setup (Closed Beta)During the closed beta phase, OpenVoid AI API keys are exclusively available to beta testers.Beta testers can obtain their OpenVoid AI API key as detailed in theOpenVoid Documentation.Configure your environment with your API key:# For zsh usersecho'export OPENVOID_API_KEY=[your_key_here]'>>~/.zshenvsource~/.zshenv# For bash usersecho'export OPENVOID_API_KEY=[your_key_here]'>>~/.bashrcsource~/.bashrcUsageRunning ExamplesTheexamples/directory includes scripts that demonstrate the OpenVoid Python client's usage. Run these examples withpoetry runor within apoetry shellenvironment.Using poetry runcdexamples
poetryrunpythonchat_no_streaming.pyUsing poetry shellpoetryshellcdexamples
pythonchat_no_streaming.pyUsage ExampleHere is a simple example that demonstrates how to use the OpenVoid Python client to ask a question and receive an answer:#!/usr/bin/env pythonimportosfromopenvoid.clientimportOpenVoidClientfromopenvoid.models.chat_completionimportChatMessagedefmain():api_key=os.environ["OPENVOID_API_KEY"]model="prox"client=OpenVoidClient(api_key=api_key)chat_response=client.chat(model=model,messages=[ChatMessage(role="user",content="What is SQL Injection?")],)print(chat_response.choices[0].message.content)if__name__=="__main__":main()This script demonstrates initiating a chat session with the OpenVoid AI and asking it to explain SQL Injection. Ensure you have set yourOPENVOID_API_KEYin your environment variables to use this example.ImprovementsAPI Key Management for Closed Beta:Enhanced configuration process for closed beta participants' API keys.Expanded Examples:More detailed examples demonstrate the OpenVoid AI API's capabilities.Improved Error Handling:Better error reporting for easier debugging and issue resolution.ContributingContributions are welcome! Check the repository's contribution guidelines for more information.LicenseThis project is licensed under theMIT License. See the LICENSE file for details.
Package: openvpn2alidns
如何安装运行pip install openvpn2alidns安装软件配置 config.ini[alidns]accessKey=accessSecret=regionId=cn-hangzhouproductName=AlidnsserviceAddr=alidns.aliyuncs.comdomainName=genee.cnrrSuffix=server如果希望使用最新的AliDNS Python SDK,
可从这里查看下载安装如何使用运行openvpn2alidns-cpath/to/config.ini path/to/openvpn/client.d
Package: openvpn3-handler
# OpenVPN3 HandlerTool develop to easily handle connections with OpenVPN3 inLinux.## Installopenvpn3Go to [openvpn.net](https://openvpn.net/cloud-docs/openvpn-3-client-for-linux/) and install following the instructions## Install handler
When installing the handler is important to use be super user, thus install by running:` sudo pip install openvpn3_handler `If using an old distribution in Linux use:` sudo pip3 install openvpn3_handler `## Configure handler` sudo openvpn3_handler config `Follow the instructions, the following variables are required:
-PATH:Absolutepath to the.opvnfile
-USERNAME: Username in the credentials of the openvpn configuration
-PASSWORD: Password in the openvpn configuration
-CONFIG_NAME: Name of the current configuration (keep the default value, unless multiple configurations are needed)
-WRITE_TO: Where to store the credentials (keep the default value/root/.openvpn3.env, unless you know what you are doing)
Package: openvpn-manager
DescriptionOpenVPN Manageris a simple yet effective API-based manager for OpenVPN connections.
It’s originally written for my Synology DS209 NAS that I use as VPN box to connect my local network to my cloud network.FeaturesComplete & accessible JSON-based APIPolling service to check connection status & automatic reconnectionPassword with Certificates (TLS) authenticationAuthorsOpenVPN Manager is written and maintained byPierre-Gildas MILLON.See here for the full list of contributors.
Package: openvpn-server
[[TOC]]Project GoalsThis project provides Python package for managingOpenVPNinstances through their entire life cycle.It can be useful for setting upCTFchallenges or variouslabs (be it home or development)to provide access to separated network orVLANs.DocsAutogenerated docs can be seen athttps://openvpn-server.readthedocs.io/en/stable/Additional system requirementsInstalled OpenVPNUser needs to be able to elevate privileges when executingopenvpnbinary seamlessly, with no passwordFor example, when using sudo you can usevisudoto add this line and allow uservpnmasterdo just that.vpnmaster  ALL=(ALL) NOPASSWD: /usr/sbin/openvpn
Package: openvpn-status
openvpn-statusis a Python library. It parses OpenVPN status log and turns
it into Python data structure for you.It is compatible with Python2.7,3.6to3.10, and PyPy.Installationpipinstallopenvpn-statusDon’t forget to put it insetup.py/requirements.txt.Getting StartedYou could configure your OpenVPN server to log for client status. In usual it
could be achieved by addingstatus/path/to/openvpn-status.logline to/etc/openvpn/openvpn.conf. For example:proto udp
port 1194
dev tun0
status /var/run/openvpn-status.logOnce OpenVPN server running, the log file will be created and written. It looks
like:OpenVPN CLIENT LIST
Updated,Thu Jun 18 08:12:15 2015
Common Name,Real Address,Bytes Received,Bytes Sent,Connected Since
foo@example.com,10.10.10.10:49502,334948,1973012,Thu Jun 18 04:23:03 2015
bar@example.com,10.10.10.10:64169,1817262,28981224,Thu Jun 18 04:08:39 2015
ROUTING TABLE
Virtual Address,Common Name,Real Address,Last Ref
192.168.255.134,foo@example.com,10.10.10.10:49502,Thu Jun 18 08:12:09 2015
192.168.255.126,bar@example.com,10.10.10.10:64169,Thu Jun 18 08:11:55 2015
GLOBAL STATS
Max bcast/mcast queue length,0
ENDNow we could parse log file with this library:fromopenvpn_statusimportparse_statuswithopen('/var/run/openvpn-status.log')aslogfile:status=parse_status(logfile.read())print(status.updated_at)# datetime.datetime(2015, 6, 18, 8, 12, 15)foo_client=status.client_list['169.254.0.1']print(foo_client.common_name)# foo@example.comprint(foo_client.bytes_received)# 334.9 kBprint(foo_client.bytes_sent)# 2.0 MBprint(int(foo_client.bytes_sent))# 2097152More details are in theAPI reference.ContributingIf you want to report bugs or request features, please feel free to open
issues onGitHub.Of course, pull requests are always welcome.
Package: openvpn-status-parser
Installation:pip install openvpn-status-parseror clonethe repositoryand runpython setup.py installUsage:addstatus-version 3to openvpn server configuration. Reload/restart openvpn server.locate openvpn status file. Usually it’s under /var/run in Unix based systems.openvpn-status-parser /var/run/openvpn/openvpn.statusOr using Python:import pprint
from openvpn_status_parser import OpenVPNStatusParser

parser = OpenVPNStatusParser("/var/run/openvpn/openvpn.status")
pprint.pprint(parser.connected_clients)
pprint.pprint(parser.routing_table)
pprint.pprint(parser.details)LicenseMIT License; see LICENSE.txt for full details.
Package: openvpn-status-parser3
This is only a fork fromhttps://github.com/ojarva/openvpn-status-parserwith some small fixes.Installation:pip install openvpn-status-parseror clonethe repositoryand runpython setup.py installUsage:addstatus-version 3to openvpn server configuration. Reload/restart openvpn server.locate openvpn status file. Usually it’s under /var/run in Unix based systems.openvpn-status-parser /var/run/openvpn/openvpn.statusOr using Python:import pprint
from openvpn_status_parser import OpenVPNStatusParser

parser = OpenVPNStatusParser("/var/run/openvpn/openvpn.status")
pprint.pprint(parser.connected_clients)
pprint.pprint(parser.routing_table)
pprint.pprint(parser.details)MIT License:Copyright (C) 2019, Daniel Niecke <daniel@niecke-it.de>Permission 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.
Package: openwakeword-pruned
IntroopenWakeWord is an open-source wakeword library that can be used to create voice-enabled applications and interfaces.
It includes pre-trained models for common words & phrases that work well in real-world environments.This is forked repo. Seeoriginal repofor details.Changes in this forkThis fork prunes code related to custom model training and verification.
By avoiding importing modules related to custom models such assklearn, it saves ~33MB of memory.
It proves to be useful on memory constrained devices, such as the Raspberry Pi Zero series.InstallationRun:pip install openwakeword-prunedCheck out other configs in theoriginal repo.
Package: open-waters
Open WatersOpen Waters is the simplest method to add Hedera Hashgraph to your supply chain or software. Using theArmada Platform, Open Waters offers straight forward interactions with Hedera Hashgraph including account creation, and sending data. Open Waters can also be used to access the benefits of the Armada Platform for data organization, conditional flows and managing supply chain interactions.How to usefromopen_watersimportOpenWater# Init an open water client without api keyclient=OpenWater()# The return client exports only one function "account.create"account=client.account.create('username','email@a.a','name','company')# Re-init an open water client with api key created from accountclient=OpenWater(account['privateKey'])# Now you can access full functionsFunctions:AccountAccount functions handle account creation and management on the Armada Platform and Hedera Hashgraph.create- Creates Armada account with associated Hedera Hashgraph account. The Armada ID is used as an identifier for functions and activities on the platform.get- Calls account information by the Armada ID.my_account- Returns information about the caller's account.update- Updates account information for Armada ID.FlowFlows are data sets on the Armada Platform for supply chain processes. Each flow is represented as Hedera Consensus Topic on Hedera Hashgraph. A flow can consists of steps to create conditional logic in the supply chain.create- Creates a flow. A flow can be of two types: Track and Trace which includes the steps structure or Data Audit which serves as a transaction list.get- Get returns flow information for Flow ID.list- Returns all flows associated with caller's ID.update- Update flow information for Flow ID.DataData functions handle interactions with Armada Flows/Hedera Consensus Topics. To be used mainly with Data Audit Flows.get- Retrieves data from Armada Flow/Consensus Topic in list formatpush- Push data to Armada Flow/Consensus Topic. Option to encrypt data.StepStep functions handle creation and updates for steps in a track & trace flow.create- Create step for flow.get- Get flow.list_by_flow- Lists all stepsremove- Removes a specific step from flow.update- Updates step parameters in flow.validate- Validate is called by validator in step to approve steps completion. Flow then proceeds to next step.DatasetData sets are data structures for a specific step in a flow. This can be for example x amount of goods manufactured, or y amount of goods received.create- Create a data set.get- Get information for data set by ID.list- List all data sets.remove- Remove a data set by ID.update- Update data set by ID.DocumentDocument function allows user to upload a document to the Armada Platform which is then hashed and sent to Hedera Hashgraph.upload- Upload document.Working with the Armada PlatformIf interested in using the Armada Platform in your supply chain or company, contact us atcontact@armadachain.io.LicenseSee LICENSE for details. Armada Chain Inc 2020