Getting brain to train in parallel (on many machines or on many threads).
This uses klyng which is built on fibers.
Get klyng installed globally:
yarn global add klyng
klyng by default runs a beacon on port :2222. You can change the port and add a password in the config file. If you ran the above command it is found here by default ~/.config/yarn/global/node_modules/klyng/config.json.
There is also the machine.json which describes how communication on the cluster will perform.
yarn down
Shuts down the klyng beacon. You shouldn't have to do this unless you are running a bunch of klyng commands at once and it things start breaking and it isn't handled nicely
yarn up
Starts the klyng beacon. If running a klyng script and the beacon isn't up you shouldn't need to do that. This might need to be done manually when running on secondary machines.
yarn start
Start cluster.js Currently configured in the packages.json to start 6 local processses. Currently hard coded to train a network in solving the likely problem. As this gets flushed out there will be better documentation and integration with brain.js directly.
yarn cluster
will start cluster.js but will also apply the machines.json This is just in reference to how to run on a cluster, but hasn't been flushed out yet (also the machines.json doesn't mean anything at this point)
yarn example
starts a trivial example, used for concept testing on klyng. This will get removed eventually but is still useful in testing out specific functionality
yarn test-sizes
Like example this is used for testing specifically how large of a data set we can send back and forth from klyng instances. The documentation on klyng isn't stellar so this was a semi-stress test. This will also be removed as brain-cluster matures