Saravanan666/pypi-org huggingface dataset:
Package: taskbeat
taskbeatIn memory task schedule support cron tasks, interval tasks and onetime tasks. Using python's asyncio.Installpip install taskbeatImportantMore than 1,000,000 60-SECOND-CYCLE tasks may cuase task delay sometimes. The limit is depends on your CPU mostly, you'd better to test to find the limitation on you server before you use it in production.Python CompatibilityCompatible with python3.7 and above.Tested with python3.7, python3.8, python3.9, python3.10, python3.11 and python3.12.Example 1importtimeimportloggingimportasynciofromtaskbeatimportTaskBeat_logger=logging.getLogger(__name__)asyncdefreport(beat):"""It should be showing report every second.If it is not showing report every second,then your tasks mostly are not fired on time."""last_counter=0whileTrue:delta=beat.event_counter-last_counterlast_counter=beat.event_counterwithopen("stats.txt","a",encoding="utf-8")asfobj:fobj.write(str(time.time())+" "+str(delta)+"\n")awaitasyncio.sleep(1)asyncdefbeat_server():beat=TaskBeat()task=asyncio.create_task(report(beat))foriinrange(800*1000):event_id="cron_{}".format(i)awaitbeat.update_cron_task(event_id,"* * * * *")whileTrue:event_id=awaitbeat.get_event(5)ifevent_id=="cron_1":# should be showing every minute# see how many seconds a task may daley here_logger.info("cron_1=%s",time.time())if__name__=="__main__":logging.basicConfig(level=logging.INFO)asyncio.run(beat_server())Releasesv0.1.0First release.v0.1.1Fix delete_task problem.
Package: taskbot
TaskBotTaskBot是一个基于深度学习、机器学习、规则系统的任务驱动型多轮对话框架，欢迎star、contribute。100行代码，实现你的chatbot。特性多轮跨场景语义继承模块化组件，快速搭建你的业务chatbot微信接口支持案例待施工安装待施工LICENSETaskBot is licensed under theGNU GENERAL PUBLIC LICENSE
Package: taskbots
Mask-PredictDownload modelDescriptionDatasetModelMASK-PREDICT[WMT14 English-German]download (.tar.bz2)MASK-PREDICT[WMT14 German-English]download (.tar.bz2)MASK-PREDICT[WMT16 English-Romanian]download (.tar.bz2)MASK-PREDICT[WMT16 Romanian-English]download (.tar.bz2)MASK-PREDICT[WMT17 English-Chinese]download (.tar.bz2)MASK-PREDICT[WMT17 Chinese-English]download (.tar.bz2)Preprocesstext=PATH_YOUR_DATAoutput_dir=PATH_YOUR_OUTPUTsrc=source_languagetgt=target_languagemodel_path=PATH_TO_MASKPREDICT_MODEL_DIRpython preprocess.py --source-lang ${src} --target-lang ${tgt} --trainpref $text/train --validpref $text/valid --testpref $text/test  --destdir ${output_dir}/data-bin  --workers 60  --srcdict ${model_path}/maskPredict_${src}${tgt}/dict.${src}.txt --tgtdict ${model_path}/maskPredict${src}_${tgt}/dict.${tgt}.txtTrainmodel_dir=PLACE_TO_SAVE_YOUR_MODELpython train.py ${output_dir}/data-bin --arch bert_transformer_seq2seq --share-all-embeddings --criterion label_smoothed_length_cross_entropy --label-smoothing 0.1 --lr 5e-4 --warmup-init-lr 1e-7 --min-lr 1e-9 --lr-scheduler inverse_sqrt --warmup-updates 10000 --optimizer adam --adam-betas '(0.9, 0.999)' --adam-eps 1e-6 --task translation_self --max-tokens 8192 --weight-decay 0.01 --dropout 0.3 --encoder-layers 6 --encoder-embed-dim 512 --decoder-layers 6 --decoder-embed-dim 512  --fp16 --max-source-positions 10000 --max-target-positions 10000 --max-update 300000 --seed 0 --save-dir ${model_dir}Evaluationpython generate_cmlm.py ${output_dir}/data-bin  --path ${model_dir}/checkpoint_best_average.pt  --task translation_self --remove-bpe --max-sentences 20 --decoding-iterations 10  --decoding-strategy mask_predictLicenseMASK-PREDICT is CC-BY-NC 4.0.
The license applies to the pre-trained models as well.CitationPlease cite as:@inproceedings{ghazvininejad2019MaskPredict,title={Mask-Predict: Parallel Decoding of Conditional Masked Language Models},author={Marjan Ghazvininejad, Omer Levy, Yinhan Liu, Luke Zettlemoyer},booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},year={2019},}
Package: taskcat
What is TaskCat?TaskCatis a tool that tests AWS CloudFormation templates. It deploys your AWS
CloudFormation template in multiple AWS Regions and generates a report with a pass/fail
grade for each region. You can specify the regions and number of Availability Zones you
want to include in the test, and pass in parameter values from your AWS CloudFormation
template. TaskCat is implemented as a Python class that you import, instantiate, and run.TaskCat was developed by the aws-ia team to test AWS CloudFormation templates
that automatically deploy workloads on AWS. We’re pleased to make the tool available to
all developers who want to validate their custom AWS CloudFormation templates across
AWS RegionsSeeTaskCat documentation.SupportGitHubPyPi
Package: taskchain
TaskChainDocumentationInstallpipinstalltaskchainFrom sourcegitclonehttps://github.com/flowerchecker/taskchaincdtaskchain
poetryinstallChangelogunpublishedallow to define multiple input task using regexp1.2.1fixes1.2.0remove redundant moduletaskchain.taskadd support for task exclusion, just useexluded_tasksin your configadd tools for testing, checktaskchain.utils.testingfinish documentationremove some redundant methods1.1.1improve chain representation in jupyteraddtasks_dfparameter to chainsadd support forusesin contexts (same syntax as in configs)improve create_readable_filenamesuse config name as default namebetter verbose modeforcemethod of both Chain and Task now supportsdelete_dataparameter which delete persisted datait defaults toFalsebe careful with thisaddMakefile1.1.0release to PIP1.0.3more types can be used forrunmethod, e.g.dictorDict[str, int]forbid some names of parameters with special meaning in configs (uses,tasks, ...)you should import fromtaskchaininstead oftaskchain.taks, later is deprecated and will be removedusefrom taskchain import Task, Config, Chainorimport taskchain as tc; tc.TaskMultiChain are now more robust, you can use them with configs with context, and it will work correctlyDevelopmentRelease new version to PIPmakeversion-patch# ORmakeversion-minor

makepublishDevelop docsrun server which dynamically serves docs web.makedocs-developBuild docsCreate documentation as static files.makedocs-buildBuild docsBuilds documentation and deploys it to GitHub Pagesmakemakedocs-publish
Package: taskcluster-taskgraph
TaskgraphTaskgraph is a Python library to generate graphs of tasks for theTaskcluster
CIservice. It is the recommended approach for configuring tasks once your
project outgrows a single.taskcluster.ymlfile and is what powers the over
30,000 tasks and counting that make up Firefox’s CI.For more information and usage instructions,see the docs.How It WorksTaskgraph leverages the fact that Taskcluster is a generic task execution
platform. This means that tasks can be scheduled via itscomprehensive API,
and aren’t limited to being triggered in response to supported events.Taskgraph leverages this execution platform to allow CI systems to scale to any
size or complexity.Adecision taskis created via Taskcluster’s normal.taskcluster.ymlfile. This task invokestaskgraph.Taskgraph evaluates a series of yaml based task definitions (similar to
those other CI offerings provide).Taskgraph applies transforms on top of these task definitions. Transforms
are Python functions that can programmatically alter or even clone a task
definition.Taskgraph applies some optional optimization logic to remove unnecessary
tasks.Taskgraph submits the resultingtask graphto Taskcluster via its API.Taskgraph’s combination of declarative task configuration combined with
programmatic alteration are what allow it to support CI systems of any scale.
Taskgraph is the library that powers the 30,000+ tasks making upFirefox’s
CI.InstallationTaskgraph supports Python 3.8 and up, and can be installed from Pypi:pip install taskcluster-taskgraphAlternatively, the repo can be cloned and installed directly:git clone https://github.com/taskcluster/taskgraph
cd taskgraph
python setup.py installIn both cases, it’s recommended to use a Pythonvirtual environment.Get InvolvedIf you’d like to get involved, please see ourcontributing docs!
Package: taskcontext
DevelopingInstall extras:python -m pip install .[test] .[lint]Testinghttps://pypi.org/project/tox-pyenv/IdeasParallel StatesTo model a parallel substate, for example you have aMenuMachineand only in
the specific menuSFTPStateit is possible to change theSFTPState. TheSFTPStatewould be a substate which lives indipendently (parallel) from the
mainMenuMachinestate, however it can only be changed when being when theMenuMachineis in theSFTPMenustate.It could look something like:class SFTPMenu(MenuMachine):
    def enable_sftp(self) -> Prallel(SFTPEnabled):
        pass

    def disable_sftp(self) -> Prallel(SFTPDisabled):
        passcallingenable_sftpordisable_sftpwould not change the main menu state,
which would still be inSFTPMenubut the substate would change.The object carrying the state would probably have multiple attribs, likemenustateandsftpstate.Automatic TransitionsI created this module for automatic transitions, it however only works
transparently if there is only one transitions to each state, my current
solution is:class MyState(MyMachine):
    def transition_which_is_not_normal_path(self):
        self.state(MyState,NewState)which works but is not transparent, better would be something like:class MyState(MyMachine):
    def transition_which_is_not_normal_path(self) -> NoAutoTrans(NewState):
        self.state(MyState,NewState)And to have an easier auto transition it would be great to maybe set some key
in the Machine to do auto transitions if possible and then:MyState(stateobj)would automaticallly transition if it wouldn’t be in the correct state.
Package: task-core
Installpip install .Example invocation (tests framework)task-core-exampleExample invocation (from folder)task-core --services-dir examples/directord/services \
          --inventory-file examples/directord/inventory.yaml \
          --roles-file examples/directord/roles.yaml \
          --debugExample directord executionThis is an example that uses [https://github.com/cloudnull/directord] to setup
an instance of keystone. The below bit of code assumes 4 nodes available
with a stack user that can been connected to via ssh from the user running
the bash script.sudo dnf install python3-virtualenv git -y

virtualenv ~/test-venv
source ~/test-venv/bin/activate

git clone https://github.com/Directord/task-core

pip install directord

pushd task-core
pip install -r requirements.txt
pip install .
popd

ssh-keygen -t rsa -f ~/.ssh/id_rsa -q -N ""
for H in 2 3 4 5; do
  ssh-keyscan -H -t rsa 192.168.24.$H >> ~/.ssh/known_hosts
  ssh-copy-id 192.168.24.$H
done

cat > ~/catalog.yaml <<EOF
directord_server:
  targets:
  - host: 192.168.24.2
    port: 22
    username: stack

directord_clients:
  args:
    port: 22
    username: stack
  targets:
  - host: 192.168.24.3
  - host: 192.168.24.4
  - host: 192.168.24.5
EOF

directord bootstrap --catalog $HOME/catalog.yaml --catalog ~/test-venv/share/directord/tools/directord-bootstrap-catalog.yaml

sudo chmod a+w /var/run/directord.sock

cat > ~/inventory.yaml <<EOF
hosts:
  standalone-1:
    role: keystone
  standalone-2:
    role: basic
  standalone-3:
    role: basic
EOF

task-core \
  -s task-core/examples/directord/services/ \
  -i $HOME/inventory.yaml \
  -r task-core/examples/directord/roles.yaml

ssh standalone-1 openstack --os-auth-url http://standalone-1:5000/v3 --os-user-domain-name default --os-username admin --os-password keystone token issue
Package: taskdb
TaskDB runs common methods in a single threadMotivationThreads sometimes need to run compute-heavy or network-heavy methods
that are expected to return a similar output. Webcrawlers and bots
acting on those crawlers may wish to decrease network load by limiting
requests to a single thread. TaskDB allows multiple threads to ask for
the result of a common function without requiring each thread to execute
the function.InstallationInstall this package through pip:$ pip install taskdbBasic UsagefromtaskdbimportTaskDBdefsome_intesive_method(some_argument,some_other_argument):# Do somethingtask_db=TaskDB()result=task_db.monitor('SOME_TASK_ID',some_intensive_method,(15,20))print(result)
Package: task-dispatcher
Version:1.4.5Status:Production/StableAuthor:José Antonio Perdiguero LópezLibrary that provides a system to generate tasks producers and consumers with ease.Celery is used as backend, so it’s necessary to configure it in the application where this library will be used.To achieve a producer-consumer behavior this library provides an easy to use script for running all necessary processes:Producer: Tasks in charge of generating processing units.Consumer: Tasks that handle and run processing units.Scheduler: Manager that runs producer tasks according to specified dates or regularity.Quick startInstall this package using pip:pipinstalltask-dispatcherDecorate your functions as producer and consumer tasks:fromtask_dispatcherimportconsumer,producer@consumerdefsquare(x):returnx**2@producerdefprod_function(n):foriinrange(n):square.delay(i)Run producer, consumer and scheduler processes:pythontask-dispatcherproducerpythontask-dispatcherconsumerpythontask-dispatcherschedulerConsumer and ProducerThis library provides convenient decorators for generating a task dispatcher system based on producer-consumer pattern.
Decorated functions or methods acts as Celery tasks and can be called using his own syntax:Calling celery tasks. Also, it’s possible to compose these tasks usingCelery Canvas.RegisterConsumer and producer tasks are registered to ease the way of access them. There is a register module that contains the
task register where all tasks can be found:fromtask_dispatcherimportregister# Get consumersregister.consumers# Get producesregister.producersAlso, this register provides a set of utilities, such as convert it into JSON or YAML format:fromtask_dispatcherimportregisteryaml_register=register.to_yaml()json_register=register.to_json()Command Line InterfaceThere is a script that can be called directly through executing the task_dispatcher package itself or the command
located in commands module. To show command help:pythontask-dispatcher-hThis script also gives a friendly way of list all tasks registered:pythontask-dispatcherlistDjangoThis library can be imported and used as a Django application instead of a plain library, so that the CLI script also
acts as a Django command.SettingsCelery settings can be specified throughTASK_DISPATCHER_SETTINGSvariable using path format indicated inCelery application configuration.
Package: taskengine
Library to handle exposing tasks to a ProcessEngineGetting startedpip install taskengineAdd to installed appsConfgure your task modulesIn settings.py:INSTALLED_APPS = [
    ..
    'taskengine',
    ..
]Tell taskengine where to find tasksIn settings.py:ALLOWED_TASK_MODULES = {
    'taskengine.tasks', # these are default tasks provided by taskengine
    .., # <- your module/s here
}

# optional
TASKENGINE_API_KEY = '...'Add API to urlsin urls.pyfrom taskengine.api import router as task_router
..

urlpatterns = [
    ..
    path(r'', include(task_router.urls)),
    ..
]Register your servicepython manage.py registerUpading pip library# update version in setup.py
python3 setup.py sdist bdist_wheel
twine upload dist/*todo: dockerize this
Package: tasker
Tasker is a way to organize your scripts. If you have dozens of small scripts and you keep forgetting what they do and what parameters they take,taskeris for you. Instead of scripts you write functions, put them in a file or a couple of them, and then run the main tasker script giving them the task name and the parameters of the task like this:run_task <task-name> [<task-parameter> ...]The built-inhelptask will scan your tasks package and give you the call signatures and docstrings of your tasks (see example below).To be considered a task, the function name should end with_task. The task name is the function name less the suffix. Suppose we have the following file stucture:run_task
tasks/
    __init__.py
    module1.py__init__.pydeftaskone_task(p1):print('in taskone_task, parameters:',p1)deftasktwo_task(p1,p2):print('in tasktwo_task, parameters:',p1,p2)andmodule1.pydeft1_task(p1):"""This is the task's documentation string.

    Continuation of the docstring
    """print('in t1_task, parameters:',p1)deft2_task(p1,p2=None):print('in t2_task, parameters:',p1,p2)To generate therun_taskscript in the current directory run:python -m tasker <fully-qualified-name-of-tasks-package>  # default = "tasks"A run of the script with the built-inhelptask produces the following:./run_task help
Usage: run_task <task-name> [parameter, ...]

All available tasks:
    help      # print help screen

    In module tasks
        taskone :  (p1)
        tasktwo :  (p1, p2)

    In module tasks.module1
        module1.t1 :  (p1)
            This is the task's documentation string.
        module1.t2 :  (p1, p2=None)We see the call signatures of the tasks.
If a task function contains a docstring the first line of the docstring will also be included.
To print the complete docstring of a task run:./run_task help <task-name>IMPORTANT: All task parameters are strings.With the above setup we can run the following examples in the current directory
(which just print the parameters):./run_task taskone foo          # prints foo
./run_task tasktwo foo baz      # prints foo, baz
./run_task tasktwo foo          # fails, not enough parameters
./run_task module1.t1 spam      # prints spam
./run_task module1.t2 spam ham  # prints spam, ham
./run_task module1.t2 spam      # works too because of default value of the second parameterYou will want to put something meaningful in your tasks.You can include tasks with distribution of your project and run them all with a single installed script. Suppose your project looks like this:myproject/
    __init__.py
    somestuff.py
    tasks/Then you can include the following snippet in__init__.py:defrun_task():importtaskertasker.main('tasks')then include the following in your setuptools-basedsetup.py:entry_points={
    'console_scripts': [
        'myproject_run_task = myproject:run_task',
        ],
    }This setup will create scriptmyproject_run_task, which will run your tasks.Installationpip install taskerThe current version is Python 3 only. Use version 0.1.2 for Python 2.7.
Package: tasker-bot
tasker_botComandos de Actualización del Código####Borramos la carpeta base de distribución
rm -rf dist/####Generamos nuevamente losarchivos de la versión
python3 setup.py sdist bdist_wheel####Enviamos la actualización
twine upload --repository-urlhttps://upload.pypi.org/legacy/dist/*Comando de control y gestión de tareas.
Package: tasker-python
TaskerEncapsulate function calls and shell commands as tasks with scheduling (powered byRocketry) and notifications (powered byApprise).InstallationFrom PyPipip install tasker-pythonFrom sourcepip install git+https://github.com/zigai/tasker.gitExamplefromtaskerimportChannel,CommandLineTaskdiscord_webhook="discord://..."task=CommandLineTask(name="hello-world",command="echo 'Hello World!'",notification_channels=[Channel(discord_webhook,events=["start","success","info","fail"]),],stdout=True,# display stdout in notifications)task.exec()# run oncefromtasker.schedulerimportTaskScheduler,everyscheduler=TaskScheduler()scheduler.schedule_task(task,every("10 seconds"))# run every 10 secondsscheduler.run()LicenseMIT License
Package: task-estimator
EstimatorA Python script for automatically estimating work hours in Jira using supervised learning.InstallationInstall the Python 3 package:pip install estimatorUsageFill out the login credentials in yourconfig.yaml. A sample file to pull data from Jira would look like:source: jira
server: https://myname.atlassian.net
username: myusername
password: mypassword
projects: MYPROJECTKEY
regressor:
    cls: KernelRidge
    stop_words: ''
    ngram_range: [1, 6]
    analyzer: char
    min_df: 0.0
minimum_estimate_minutes: 15
hour_update_fields:
-   Story PointsRetrieve training data:estimator config.yaml retrieveGenerate all possible algorithm and setting combinations:estimator config.yaml generation-combinationsTest all combinations to find which algorithm works best:estimator config.yaml test-combinationsFill in theregressorsettings in yourconfig.yaml, then train your final regressor:estimator config.yaml trainFind the algorithm's accuracy:estimator config.yaml testDryrun the application of the regressor to unestimated tickets:estimator config.yaml applySave these estimates with:estimator config.yaml apply --saveTo combine the above retrieve, train and apply steps into a single command, just add--retrainto the apply command:estimator config.yaml apply --save --retrainDevelopmentRun tests locally with:toxTo run tests for a specific environment (e.g. Python 3.7):tox -e py37To run a specific test:export TESTNAME=.test_learning; tox -e py37
Package: taskfarm
taskfarmThis package solves the problem of managing a loosely coupled taskfarm where
there are many tasks and the workers are entitrly independent of each other.
Instead of using a farmer process a database is used to hand out new tasks to
the workers. The workers contact a web application via http(s) to get a new
task.You can use thetaskfarm-workerto connect to the taskfarm service.See thedocumentationfor installation instructions.
Package: taskfarm-worker
taskfarm-workerThis package is the client code to work with thetaskfarmweb application.You can use the manageTF script to manage the task farm.Find the full documentation onhttps://taskfarm-worker.readthedocs.io/ExamplesHave a look at theexample scriptto see how to
use the module. Thestress/stress.py scriptstresses the taskfarm server by spawning multiple clients. You can use thestress/plot.py scriptto plot the csv files
produced by the stress script.
Package: taskflow
TaskFlowA library to do [jobs, tasks, flows] in a highly available, easy to understand
and declarative manner (and more!) to be used with OpenStack and other
projects.Free software: Apache licenseDocumentation:https://docs.openstack.org/taskflow/latest/Source:https://opendev.org/openstack/taskflowBugs:https://bugs.launchpad.net/taskflow/Release notes:https://docs.openstack.org/releasenotes/taskflow/Join ushttps://launchpad.net/taskflowTesting and requirementsRequirementsBecause this project has many optional (pluggable) parts like persistence
backends and engines, we decided to split our requirements into two
parts: - things that are absolutely required (you can’t use the project
without them) are put intorequirements.txt. The requirements
that are required by some optional part of this project (you can use the
project without them) are put into ourtest-requirements.txtfile (so
that we can still test the optional functionality works as expected). If
you want to use the feature in question (eventletor the worker based engine
that useskombuor thesqlalchemypersistence backend or jobboards which
have an implementation built usingkazoo…), you should add
that requirement(s) to your project or environment.Tox.iniOurtox.inifile describes several test environments that allow to test
TaskFlow with different python versions and sets of requirements installed.
Please refer to thetoxdocumentation to understand how to make these test
environments work for you.Developer documentationWe also have sphinx documentation indocs/source.To build it, run:$ python setup.py build_sphinx