Package: traxfiber
Project HomeBlogDocumentsPaperMedia CoverageJoin Fiber users email listfiber-users@googlegroups.comFiberDistributed Computing for AI Made SimpleThis project is experimental and the APIs are not considered stable.Fiber is a Python distributed computing library for modern computer clusters.It is easy to use. Fiber allows you to write programs that run on a computer cluster level without the need to dive into the details of computer cluster.It is easy to learn. Fiber provides the same API as Python's standardmultiprocessinglibrary that you are familiar with. If you know how to use multiprocessing, you can program a computer cluster with Fiber.It is fast. Fiber's communication backbone is built on top ofNanomsgwhich is a high-performance asynchronous messaging library to allow fast and reliable communication.It doesn't need deployment. You run it as the same way as running a normal application on a computer cluster and Fiber handles the rest for you.It it reliable. Fiber has built-in error handling when you are running a pool of workers. Users can focus on writing the actual application code instead of dealing with crashed workers.Originally, it was developed to power large scale parallel scientific computation projects likePOETand it has been used to power similar projects within Uber.Installationpip install fiberCheckherefor details.Quick StartHello FiberTo use Fiber, simply import it in your code and it works very similar to multiprocessing.importfiberif__name__=='__main__':fiber.Process(target=print,args=('Hello, Fiber!',)).start()Note thatif __name__ == '__main__':is necessary because Fiber usesspawnmethod to start new processes. Checkherefor details.Let's take look at another more complex example:Estimating Piimportfiberimportrandom@fiber.meta(cpu=1)definside(p):x,y=random.random(),random.random()returnx*x+y*y<1defmain():NUM_SAMPLES=int(1e6)pool=fiber.Pool(processes=4)count=sum(pool.map(inside,range(0,NUM_SAMPLES)))print("Pi is roughly{}".format(4.0*count/NUM_SAMPLES))if__name__=='__main__':main()Fiber implements most of multiprocessing's API includingProcess,SimpleQueue,Pool,Pipe,Managerand it has its own extension to the multiprocessing's API to make it easy to compose large scale distributed applications. For the detailed API guild, check outhere.Running on a Kubernetes clusterFiber also has native support for computer clusters. To run the above example on Kubernetes, fiber provided a convenient command line tool to manage the workflow.Assume you have a working docker environment locally and have finished configuringGoogle Cloud SDK. Bothgcloudandkubectlare available locally. Then you can start by writing a Dockerfile which describes the running environment.  An example Dockerfile looks like this:# example.dockerFROMpython:3.6-busterADDexamples/pi_estimation.py/root/pi_estimation.pyRUNpipinstallfiberBuild an image and launch your jobfiber run -a python3 /root/pi_estimation.pyThis command will look for local Dockerfile and build a docker image and push it to your Google Container Registry . It then launches the main job which contains your code and runs the commandpython3 /root/pi_estimation.pyinside your job. Once the main job is running, it will start 4 subsequent jobs on the cluster and each of them is a Pool worker.Supported platformsOperating system: LinuxPython: 3.6+Supported cluster management systems:Kubernetes (Tested with Google Kubernetes Engine on Google cloud)We are interested in supporting other cluster management systems likeSlurm, if you want to contribute to it please let us know.Checkherefor details.DocumentationThe documentation, including method/API references, can be foundhere.TestingInstall test dependencies. You'll also need to make suredockeris available on the testing machine.$pipinstall-e.[test]Run tests$maketestContributingPlease read ourcode of conductbefore you contribute! You can find details for submitting pull requests in theCONTRIBUTING.mdfile. Issuetemplate.VersioningWe document versions and changes in our changelog - see theCHANGELOG.mdfile for details.LicenseThis project is licensed under the Apache 2.0 License - see theLICENSEfile for details.Cite Fiber@misc{zhi2020fiber,
    title={Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods},
    author={Jiale Zhi and Rui Wang and Jeff Clune and Kenneth O. Stanley},
    year={2020},
    eprint={2003.11164},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}AcknowledgmentsSpecial thanks to Piero Molino for designing the logo for Fiber