Saravanan666/pypi-org huggingface dataset:
Package: autoreview
Jason Portenoy
2018This is code and sample data accompanying the paper:Supervised Learning for Automated Literature Reviewpublished in the proceedings of the 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019)
co-located with the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019)Starting with a list of seed papers, get candidate papers by following in- and out-citations (2 degrees).
Then, train a classifier to rank the candidate papers.
Repeat this a number of times to get an aggregate ranking for many candidate papers.Example script inscripts/run_autoreview.pyInputs:List of paper IDs for the seed set.Data for paper citations.Paper data to be used as features for the classifiers (e.g., clusters, eigenfactor, titles, etc.)Parameters:Size of the initial splitNumber of times to perform the overall process of collecting candidate papers and training a classifierOutput:List of papers not in the seed set, ordered descending by relevance score.InstallationInstall via PyPI:pip install autoreviewExampleApache Spark (https://spark.apache.org/downloads.html) must be installed to run the example.The environment variableSPARK_HOMEmust be set (preferably in a.envfile) with the path to Spark.Java version 8 is required to be used with Spark. Make sure Java 8 is installed and point to its path with the environment variableJAVA_HOME.Example.envfile:SPARK_HOME=/home/spark-2.4.0-bin-hadoop2.7
JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64Create a virtual environment and install the required libraries:virtualenv venv
source venv/bin/activate
pip install -r requirements.txtRun the full autoreview pipeline using sample data:python scripts/run_autoreview.py --id-list sample_data/sample_IDs_MAG.txt --citations sample_data/MAG_citations_sample --papers sample_data/MAG_papers_sample --sample-size 15 --random-seed 999 --id-colname Paper_ID --cited-colname Paper_Reference_ID --outdir sample_data/sample_output --debugThis is just meant to show how the system operates. It will not provide meaningful results with such a small sample of paper and citation data.It will output the top predictions insample_data/sample_output/predictions.tsv.DevelopmentFor new releases:# increment the version number
bump2version patchReplacepatchwithminorormajoras needed. Then:# push new release to github
git push --tags

# build and upload to PyPI
python setup.py sdist bdist_wheel
twine check dist/*
twine upload dist/*
Package: autorok
AutorokBrief overviewAutorok is a python wrapper around sigrok-cli and libsigrok/libsigrok4DSL, which provides all common functionalities
from those binaries/executables.Allows for easier sigrok automation using Python language, for tasks such as HW testing.Supported interfacesCurrently supported interfaces areSigrokCLIPlanned interfaces to support are:LibsigrokLibsigrok4DSLUsed methodology to create this projectThis project is made with technique called "AMDD", which a short form of term 'Agile Model Driven Development'In summary:Model a thing you want to codeWrite a test to this modelWrite implementation against written test which was based on a modelThis is basically extension of TDD (Test Driven Development) with architecture/modelling SW stuff. My modelling skills still sucks, but I will get better soon (I hope :) )Link to AMDD:Agile Model Driven Development (AMDD): The Key to Scaling Agile Software DevelopmentCorresponding Models folder is availablehereWingware donationI'm thankful to Wingware for generous donation of their great Wing IDE Pro license for such small project.Those guys really works their hats off to provide really great experience :)I highly recommend checking them out!Wingware
Package: autoroutes
AutoroutesRoutes for speed.Installpip install autoroutesAPI# Create a Routes instancefromautoroutesimportRoutesroutes=Routes()# Register a new routeroutes.add('path/to/resource/{id}',something='value',anything='else')# Try to match a routeroutes.match('path/to/resource/1234')>({'something':'value','anything':'else'},{'id':'1234'})Note: the order the routes are registered matters. At match time, routes will be
tried in that order.PlaceholdersPlaceholders are defined by a curly brace pair:path/{var}. By default, this
will match any character but the slash ('/').It's possible to control the placeholder type, either by:using a named type:alnum,digit,alpha,path(matches everything),any(matches everything, including empty string),string(default):path/to/{var:digit}
  path/to/{var:string}  # Same as path/to/{var}using a normal regex (slower; also note that regex containing curly braces is
not yet supported)path/to/{var:\d\d\d}Placeholders can appear anywhere in the pathpath/to/file.{ext}
path/to/{name}.{ext}Building from sourcepip install cython
make compile
python setup.py developTestsmake testBenchmarkSeeBenchmarkfor more
details.AcknowledgementsThis package has been first made as a Cython port of theR3C router.
See alsopython-r3, which was a first
attempt to wrap R3. I was unhappy with the stability, and more curious about
Cython, so I tried to make a first POC port, and was happy with it.
Package: autoRSA
autoRSAautoRSA是一个半自动预测RSA术式中入钉角度，导板形状的Python库。依赖库一览autoRSA依赖于以下Python库：open3D，通过以下代码安装：pip install open3dnumpy，通过以下代码安装：pip install numpytqdm，通过以下代码安装：pip install tqdmpyvista，通过以下代码安装：pip install pyvistatorch和sko，可选（find_nail2()函数所需）。借助pip install autoRSA可以自动安装依赖库。计算流程一览函数初始化。filename是肩胛骨模型文件的位置，建议使用已计算法向量和三角面片的.stl格式文件。s=scapula(filename)函数选点。选点采用的是最佳拟合圆的方式，用户选点应先选取关节盂下缘的点，再选取关节盂前后缘的点。select_points1()函数提供了一个基于点云选取所需点的窗口。select_points2()函数需要用户或其他程序自己输入选取的点在文件中的索引。picked_id_pcd应是一个列表，其中储存了选择点的索引。s.select_points1()s.select_points2(picked_id_pcd)计算最佳拟合圆圆心。s.computer_circle()移动模型以便于计算。s.move_center_to_O()s.find_vector()钉长计算。其中theta1，theta2，num_point这三个参数是可选的，默认为5/8度，360/40度，400点。s.find_nail(theta1=...,theta2=...,num_point=...)print(s.location)# 输出举例：[array([38.44197149]), [], array([ 13.74876639, 205.71172213])]# 输出解释：第一个数字代表求的钉长，第二个参数目前无用，第三个参数的两个值代表钉和法向量夹角以及沿着法向量旋转的角度。计算把手位置。filename代表把手文件的位置，建议使用.stl格式。我们提供了一个把手文件。用户也可以自己绘制。s.find_handle(filename)计算导板形状。s.find_guide()放置基座。filename代表基座文件的位置，建议使用.stl格式。我们提供了一个基座文件。用户也可以自己绘制。s.find_jizuo(filename)将步骤4中移动所带来的变化复原。s.go_back()保存文件。输出包括肩胛骨模型文件mesh.stl，把手文件handle.stl，导板文件guide.stl，钉子文件nail.stl，基座文件jizuo.stl。其他程序可以读入这些文件进行后续操作，用户也可以借助Mimics或其他软件进行后续处理。s.save()效果参考
Package: autort-swarms
AutoRTImplementation of AutoRT: "AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents". This repo will implement the multi agent system that transforms a scene into a list of ranked and priortized tasks for an robotic action model to execute. This is an very effective setup that I personally believe is the future for swarming robotic foundation models!This project will be implemented using Swarms, for the various llms and use the official RT-1 as the robotic action model.PAPER LINKInstall$ pip3 install autort-swarmsUsageAutoRTAgentA single AutoRT agent that: analyzes a scene using visual COT -> generate tasks -> filter tasks -> execute it with a robotic transformer.# Import necessary modulesimportosfromautortimportAutoRTSwarm,AutoRTAgent# Set the OpenAI API keyopenai_api_key=os.getenv("OPENAI_API_KEY")# Define a list of AutoRTAgent instancesagents=[AutoRTAgent(openai_api_key,max_tokens=1000),AutoRTAgent(openai_api_key,max_tokens=1000),]# Create an instance of AutoRTSwarm with the agents and datastoreautort_swarm=AutoRTSwarm(agents)# Run the AutoRTSwarm with the given inputsautort_swarm.run("There is a bottle on the table.","https://i.imgur.com/2qY9f8U.png",)AutoRTSwarmA team of AutoRT agents where you can plug in and play any number ofAutoRTAgentswith customization. First, the task will be routed to each agent and then all of their outputs will be saved.# Import necessary modulesimportosfromautortimportAutoRTSwarm,AutoRTAgent# Set the OpenAI API keyopenai_api_key=os.getenv("OPENAI_API_KEY")# Define a list of AutoRTAgent instancesagents=[AutoRTAgent(openai_api_key,max_tokens=1000),AutoRTAgent(openai_api_key,max_tokens=1000),]# Create an instance of AutoRTSwarm with the agents and datastoreautort_swarm=AutoRTSwarm(agents)# Run the AutoRTSwarm with the given inputsautort_swarm.run("There is a bottle on the table.","https://i.imgur.com/2qY9f8U.png",)Citation@inproceedings{anonymous2023autort,title={Auto{RT}: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents},author={Anonymous},booktitle={Submitted to The Twelfth International Conference on Learning Representations},year={2023},url={https://openreview.net/forum?id=xVlcbh0poD},note={under review}}LicenseMITTodoImplement a run method intoAutoRTSwarmthat runs all the agents with APIs.Make it able to send commands to a certain agent using the swarm network.Send a task to all agents in the swarm network
Package: autorun-inf-deobfuscator
autorun.inf DeobfuscatorA cli script to deobfuscate obfuscatedautorun.inffiles as used by the Conficker / Downadup malware for example.Such anautorun.inffile can be quite big since the malware authors can add junk to the configfile which will not be evaluated by Windows.
This scripts only shows the important parts of the config which will be evaluated by Windows.
More information aboutautorun.inffiles you can find onWikipedia.InstallationInstall the package with pippip install autorun-inf-deobfuscatororpip install git+https://github.com/wahlflo/AutorunInfDeobfuscatorFeaturesIt removes all non ASCII charactersIt removes empty linesIt removes commentsIt adds missing brackets to section declarationsIt removes not junk sections which are meaningless in an autorun.inf fileUsageTypedeobfuscate-autorun-inf --helpto view the help.usage: deobfuscate-autorun-inf [OPTION]... -i FILE

A cli script to deobfuscate obfuscated autorun.inf files as used by the Conficker / Downadup malware for example.

options:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        path to the eml-file (is required)
  --no-deobfuscation    No deobfuscation
  --remove-comments     Remove comments
  --remove-empty-lines  Remove empty lines
  --fix-missing-brackets
                        Fix missing section brackets
  --remove-junk-sections
                        Remove junk sections by filtering on the legitimate sections of an autorun.inf file
  --show-sections       Prints out only the name of the sections contained in the file
  -o OUTPUT, --output OUTPUT
                        Writes the obfuscated file to the given fileExample deobfuscation of an autorun.inf fileexcerpt of an obfuscated autorun.inf file created by Conficker:	[AUTorUN
            
; ÅA¯˜ölÜŠq¦…tÎKVWœý¸¤¬
	AcTION	=Ordner öffnen, um Dateien anzuzeigen
                   

              
             

                 
 

		icon =%syStEmrOot%\sySTEM32\sHELL32.Dll         ,4


;­Pr×SoàDWWCfDnhTvVQyažã¾
;«GáÊ	 

;qTJ¥·r€ÕoÍgwDqçÚJûKEí´û
  
	shelLExECUte=RuNdLl32.EXE      .\RECYCLER\S-5-3-42-2819952290-8240758988-879315005-3665\jwgkvsq.vmx,ahaezedrn
;zD¾pl¿›cà½ÂuDbËyF½žÚG	
                       
                            
;f›yÊlÌÃèŠdGµBwAsUmF
; »Ÿobz²q•GEìªiSøµväF˜Ø¤ò¼fîNŒDs±
                       
   
                   
useAuTopLAY=	1   
; Fª†g•¿úoÖMÊc°­¹tYcÈìkdQeæØnD§äâÙrˆe…C¿ùlÝ„ôC	
 	[oiw]	
deobfuscation with thedeobfuscate-autorun-infscript:$deobfuscate-autorun-inf -i conficker_autorun_sample.ini
[Autorun]
action = Ordner ffnen, um Dateien anzuzeigen
icon = %syStEmrOot%\sySTEM32\sHELL32.Dll         ,4
shellexecute = RuNdLl32.EXE      .\RECYCLER\S-5-3-42-2819952290-8240758988-879315005-3665\jwgkvsq.vmx,ahaezedrn
useautoplay = 1
Package: auto-run-on-remote
1 安装pip install auto_run_on_remote2 auto_run_on_remote 介绍全名字含义应该是 auto_run_current_python_script_on_remote_server

在本机运行脚本自动在远程机器上运行。

自动上传文件夹 自动创建文件夹 自动远程机器上运行python脚本。
原理是 run_current_script_on_remote() 函数中自动利用 sys._getframe(1).f_code.co_filename 获取当前文件位置，所以不用传参告诉函数当前脚本的位置。

比pycahrm专业版复杂的配置调用远程python解释器更方便，因为pycahrm专业版如果设置了远程解释器启动脚本时候很卡，
启动ide界面更卡，因为ide会去读远程解释器的所有安装的python包，真的是太卡了。
而这个在远程运行启动速度就很快，丝毫不会造成ide卡顿。
还有就是破解最新版pycahrm麻烦老失效，用社区版就好了3 用法如下在项目的任意目录层级下的脚本中运行 run_current_script_on_remote(),则脚本会自动运行在远程机器。
当第一次运行脚本时候，会自动在你当项目的根目录生成 auto_run_on_remote_config.py 配置文件，然后，你自己按需修改其中的值。
以后运行run_current_script_on_remote()会自动读取到 auto_run_on_remote_config.py  中的配置。importtimeimportsysfromauto_run_on_remoteimportrun_current_script_on_remoterun_current_script_on_remote()# 以下的代码的print都不是在当前机器打印的，是在远程机器上打印的。foriinrange(10):print(f'嘻嘻{i},通过文件路径和python解释器路径，可以发现这句话是在远程机器打印出来的,{__file__},{sys.executable}')time.sleep(1)代码运行截图，我本机是win10，可以看到代码是运行在linux的。4 配置文件 auto_run_on_remote_config.py 介绍"""这个配置文件是自动生成到你的项目根目录的。"""importsys# 项目根目录文件夹，这个一般不需要改，会根据PYTHONPATH智能获取。# pycahrm自动添加了项目根目录到第一个PYTHONPATH，如果是cmd命令启动这先设置PYTHONPATH环境变量。# windows设置  set PYTHONPATH=你当前python项目根目录,然后敲击你的python运行命令# linux设置    export PYTHONPATH=你当前python项目根目录,然后敲击你的python运行命令PYTHON_PROJ_DIR_LOCAL=sys.path[1]# 这是远程机器的账号密码配置。把这个配置文件加到gitignore就不会泄漏了。HOST='192.168.6.133'PORT=22USER='ydf'PASSWORD='123456'PYTHON_INTERPRETER='python3'# 如果你安装了四五个python环境，可以直接指定远程解释器的绝对路径  例如 /opt/minicondadir/ens/env35/pythonFORBID_DEPLOY_FROM_LINUX=True# 一般生产机器是linux，是否禁止从linux部署到别的机器，这样可以防止你从生产环境远程到测试环境，配置后，即使生产环境的代码有远程部署，也不会执行远程部署而是直接运行。# 上传文件夹的配置，具体可以看paramiko_util.py里面的代码。PATH_PATTERN_EXLUDED_TUPLE=('/.git/','/.idea/','/dist/','/build/')# 路径中如果有这些就自动过滤不上传FILE_SUFFIX_TUPLE_EXLUDED=('.pyc','.log','.gz')# 这些后缀的文件不上传ONLY_UPLOAD_WITHIN_THE_LAST_MODIFY_TIME=3650*24*60*60# 只有在这个时间之内修改的文件才上传。如果项目比较大，可以第一次完整上传，之后再把这个时间改小。FILE_VOLUME_LIMIT=1000*1000# 大于这个体积的文件不上传，单位b。SFTP_LOG_LEVEL=20# 文件夹上传时候的日志级别。10 logging.DEBUG ,20 logging.INFO 30 logging.WaRNING,如果要看为什么某个文件上传失败，可以设置debug级别。EXTRA_SHELL_STR=''# 远程执行命令之前，可以自定义执行的shell语句，一般例如可以设置啥环境变量什么的。
Package: autorunx
AutoRunX-PythonAutoRunX is an open source low code framework. The user only needs to run the script by entering the program flowchart configuration file.If you are a company or organization, you can extend your code base based on this framework with your own products or use cases.UsageInstallpipinstallautorunxUsed in Pythonimportautorunxasarxarx.run("config.json")Used in Linux Commandautorunx-c"config.json"orarx-c"config.json"Build Release FileBuild source distribution packagesUsed to distribute a 'Python' module or project, packaging the source code into a 'tar.gz' or 'zip' packagepythonsetup.pysdist# package, default tar.gzpythonsetup.pysdist--formats=gztar,zip# package, specifying the compression formatBuild a binary distribution packageMulti-platform packagingIf your project needs to install multiple platforms, both Windows and Linux, according to the above method, a variety of formats we have to execute multiple commands, for convenience, you can step in place, execute the following command, you can generate multiple formats of the systempythonsetup.pybdistBuild wheel package# pip install wheelpythonsetup.pysdistbdist_wheelInstall By Setup.pyUse setup.py to install the packagepythonsetup.pyinstallIf your project is still in development and needs frequent updates, you can use the following command to install it. This method does not actually install the package, but creates a soft link in the system environment to the actual directory where the package is located. After modifying the package, it can take effect without installing it again, which is easy to debug.pythonsetup.pydevelopRelease To PyPiRegister PyPi account and get the api token.Create~/.pypirc(orC:/User/${yourname}/.pypirc) file and edit:[distutils]index-servers=pypi[pypi]username=__token__password=your_token_input_hereUploadBefore uploading, you need to biuld first.twine upload dist/*   # upload all files under dist folder
Package: autosar
AUTOSARA set of Python modules for working withAUTOSARXML files.Allows individuals and teams to incrementally develop and maintain AUTOSAR SWC models using Python code.Executing a Python script can quickly regenerate the same ARXML file(s) (no real need to store generated ARXML files in version control).It is recommended that you use a commercial AUTOSAR toolchain to integrate generated SWCs into your ECU.AUTOSAR version supportAUTOSAR 3.0AUTOSAR 4.2The intent is to add support for AUTOSAR 4.3 and 4.4 in the future. (Timeplan is unknown since I do most of the work in my spare time.)RequirementsPython 3cfileDocumentationDocumentation RootInstallation GuideAUTOSAR 4 APIRelease TracksStable (v0.4)This is the stable release track. Only bug fixes and stability improvements will be made.Latest stable release isv0.4.1.Future work will be tracked on branchrelease/0.4.Experimental (v0.5)This is the development track for new features. Future changes might break existing APIs from v0.4.x. Use at your own risk.First releasev0.5.0is not yet made.Future work will be tracked on branchmaster.
Package: autosar-e2e
autosar-e2eThe documentation is availablehere.Table of ContentsDescriptionInstallationUsageTestBuildLicenseDescriptionThis library provides fast C implementations of the E2E CRC algorithms and E2E profiles.Currently, all relevant CRC algorithms are available in modulee2e.crcbut only E2E profiles 1, 2 and 5 are available.
If you provide example data for the other profiles I would try to implement them, too.Installationpip install autosar-e2eUsageCRC exampleimporte2ecrc:int=e2e.crc.calculate_crc8_h2f(b"\x00\x00\x00\x00")E2E Profile 2importe2e# create datadata=bytearray(b"\x00"*8)length=len(data)-1data_id_list=b"\x00"*16# increment counter and calculate CRC inplacee2e.p02.e2e_p02_protect(data,length,data_id_list,increment_counter=True)# check CRCcrc_correct:bool=e2e.p02.e2e_p02_check(data,length,data_id_list)Testpip install pipxpipx run toxBuildpip install pipxpipx run buildpipx run twine check dist/*Licenseautosar-e2eis distributed under the terms of theMITlicense.
Package: autosave
autosaveInstallationpipinstallautosaveExamplefromautosaveimportFile,AppStorage# When editing multiple entries,# use a with statement to only save on exit.withFile('my_file.json')asdata:data['dessert']='pancakes'data['genre']='jazz'# By indexing entries on their own, the file is saved on each edit.# This is adviced against if you're editing multiple entries at a time,# as it is much less performant.file=File('my_file.json')file['garbage']='smooth'+file['genre']# Get access to the right directories for your app,# by using this wrapper around `appdirs`app=AppStorage('MyApp')app.data/'plugins/baguette.json'
Package: autosbatch
autosbatchsubmit hundreds of jobs to slurm automaticallyDocumentation:https://Jianhua-Wang.github.io/autosbatchGitHub:https://github.com/Jianhua-Wang/autosbatchPyPI:https://pypi.org/project/autosbatch/Free software: MITFeaturesSometimes, it's quite inconvenient when we submit hundreds of jobs to slurm. For example, one needs to align RNA-seq data from one hundred samples. He may start with a bash script that takes the fastq of one sample and write sbatch scripts which executebash align.sh sample.fqmultiple times. If he wants to run 50 samples at the same time, he should write 50 sbatch scripts and each script contains two align commands. Manually managing these sbatch scripts is inconvenient. autosbatch is very helpful for submitting slurm jobs automatically and it's just like themultiprocessing.Pool.Automatically submit hundreds of jobs to Slurm with a few code.The same usage asmultiprocessing.Pool.Provide command line tool for people who are not familiar with Python.TODOSupport gpu allocationSupport MPI jobsCreditsThis package was created withCookiecutterand thewaynerv/cookiecutter-pypackageproject template.
Package: autoscale-agent
Python Agent (Autoscale.app)ProvidesAutoscale.appwith the necessary metrics for autoscaling web and worker processes.InstallationInstall the package:pip install autoscale-agentUsageThis package may be used as a stand-alone agent, or as middleware that integrates withDjangoandFlask.Installation instructions are provided during the autoscaler setup process onAutoscale.app.Related PackagesThe following packages are currently available.Queues (Worker Metric Functions)Worker LibraryRepositoryCeleryhttps://github.com/autoscale-app/python-queue-celeryRQhttps://github.com/autoscale-app/python-queue-rqLet us know if your preferred worker library isn't available and we'll see if we can add support.DevelopmentPrepare environment:pip install poetry
poetry installBoot the shell:poetry shellSee Paver for relevant tasks:paver --helpReleaseUpdatepyproject.tomlUpdateautoscale_agent/__init__.pyUpdateCHANGELOG.mdCreate a new git tag (v1.2.3)Push the new git tagContributingBug reports and pull requests are welcome on GitHub athttps://github.com/autoscale-app/python-agent
Package: autoscaleaws
Autoscale AWS allows to scale up virtual machines by invoking spot requests as per
start and stop rules.To publish
cd repo
python pypi.py  pblishCreate a folder of tasks.
   /tasks/mytask01/
   /tasks/mytask02/
   /tasks/mytask03/


Autoscale will pick up those ones, launch AWS instances,
copy the folder to remote AWS, and launch main.sh on remote instances.
and stop them when the tasks are finished.


The results are stored in S3 folders and it can be retrieved
even the instances are closed on your local PCInstall process  from pip :

cd yourenv
pip install autoscale_aws

Configuration is in 
YOURHOME/.aws/Install process  from github :

CURDIR=`pwd`
git clone https://github.com/arita37/autoscale_aws/
mkdir ${CURDIR}/venvs
Make sure virtualenv is installed
virtualenv -p python3 ${CURDIR}/venvs/autoscaleenv
source ${CURDIR}/venvs/autoscaleenv/bin/activate
cd ${CURDIR}/autoscale_aws

pip install -U pip
pip install -r requirements.txt
export PYTHONPATH=${CURDIR}/autoscale_aws/src:${PYTHONPATH}
python src/autoscale_aws/batch_daemon_autoscale_cli.py --help
Package: autoscale-queue-celery
Python Queue Celery (Autoscale.app)ProducesCeleryqueue metrics for theAutoscale.appAgent.InstallationInstall the package:pip install autoscale-queue-celeryUsageInstructions are provided during the autoscaler setup process onAutoscale.app.DevelopmentPrepare environment:pip install poetry
poetry installBoot the shell:poetry shellSee Paver for relevant tasks:paver --helpReleaseUpdatepyproject.tomland__init__.pyCreate and push a new tag (v.1.2.3)ContributingBug reports and pull requests are welcome on GitHub athttps://github.com/autoscale-app/python-queue-celery
Package: autoscale-queue-rq
Python Queue RQ (Autoscale.app)ProducesRQqueue metrics for theAutoscale.appAgent.InstallationInstall the package:pip install autoscale-queue-rqUsageInstructions are provided during the autoscaler setup process onAutoscale.app.DevelopmentPrepare environment:pip install poetry
poetry installBoot the shell:poetry shellSee Paver for relevant tasks:paver --helpReleaseUpdatepyproject.tomland__init__.pyCreate and push a new tag (v.1.2.3)ContributingBug reports and pull requests are welcome on GitHub athttps://github.com/autoscale-app/python-queue-rq