Package: graph-jsp-utils
Graph Job Shop Problem Gym Environment UtilsAbout The ProjecttodoGithub:https://github.com/Alexander-Nasuta/graph-jsp-utilsPyPi: todoQuick Starttodo update docsInstall the PackageInstall the package with pip:todoProject StructureThis project is still in development and will have some significant changes before version 1.0.0.
This project ist structured according toJames Murphy's testing guideand
thisPyPi-publishing-guide.Getting StartedIf you just want to use the environment, then only the Usage section is relevant for you.
If you want to further develop the environment the follow the instructions in the Development section.UsageInstall the package with pip:todoTODO: present all major features of the env with ray, stb3DevelopmentTo run this Project locally on your machine follow the following steps:Clone the repogitclonehttps://github.com/Alexander-Nasuta/graph-jsp-env.gitInstall the python requirements_dev packages.requirements_dev.txtincludes all the packages of
specifiedrequirements.txtand some additional development packages likemypy,pytext,toxetc.pipinstall-rrequirements_dev.txtInstall the modules of the project locally. For more info have a look atJames Murphy's testing guidepipinstall-e.TestingFor testing make sure that the dev dependencies are installed (requirements_dev.txt) and the models of this
project are set up (i.e. you have runpip install -e .).Then you should be able to runmypysrcflake8srcpytestor everthing at once usingtox.toxIn this Section describes the used Setup and Development tools.
This only relevant if you plan on further developHardwareAll the code was developed and tested locally on an Apple M1 Max 16" MacBook Pro (16-inch, 2021) with 64 GB Unified Memory.Thecodeshould run perfectly fine on other devices and operating Systems (see Github tests).Python Environment ManagementMacOn a Mac I recommend usingMiniforgeinstead of more common virtual
environment solutions likeAnacondorConda-Forge.Accelerate training of machine learning models with TensorFlow on a Mac requires a special installation procedure,
that can be foundhere.
However, this repository provides only the gym environment and no concrete reinforcement learning agents.
Todo: example project with sb3 and rlSetting up Miniforge can be a bit tricky (especially when Anaconda is already installed).
I found thisguideby Jeff Heaton quite helpful.WindowsOn aWindowsMachine I recommendAnacond, sinceAnacondandPycharmare designed to work well with each
other.IDEAI recommend to usePycharm.
Of course any code editor can be used instead (likeVS codeorVim).This section goes over a few recommended step for setting up the Project properly insidePycharm.PyCharm SetupMark thesrcdirectory asSource Root.right click on the 'src' -> 'Mark directory as' -> `Source Root`Mark theresourcesdirectory asResource Root.right click on the 'resources' -> 'Mark directory as' -> `Resource Root`Mark thetestsdirectory asTest Source Root.right click on the 'tests' -> 'Mark directory as' -> `Test Source Root`afterwards your project folder should be colored in the following way:(optional) When running a script enableEmulate terminal in output consoleRun (drop down) | Edit Configurations... | Configuration | ☑️ Emulate terminal in output consoleLicenseDistributed under the MIT License. SeeLICENSE.txtfor more information.