PrayTools.jl are former TrainTools, that I had to rename because someone has created an internal package of the same name and I was developing a split personality.
PrayTools.jl is a collection of routines to simplify boring stuff and my messing around of training NNs, mainly about various versions of distributed training.
ptrain!(loss, ps, preparesamples, opt, iterations; cb = () -> (), cby = (y) ->(), bs = Threads.nthreads())
ttrain!(loss, ps, preparesamples, opt, iterations; cb = () -> (), cby = (y) ->())performs parallel training assuming that the loss function is additive differing in where preparesamples is called. In ptrain!, a single thread calls preparesamples to prepare minibatch, then dividebatch(bs::Int, xs...) divides it to bs sub-batches, which are then dispatched to a separated thread to calculate gradient. In ttrain!, each thread calls preparesamples and calculate the gradient immediately. This means that in ptrain!, preparesamples should return the full minibatch, in ttrain! it should return just the sub-batch that would be used by one thread. Both functions use tree-reduction algorithm, such that the complexity of reducing gradients is log(bs). The iterations is the number of iterations, after which the loop stops, cb is the callback function similar to that of Flux.train! and cby is a callback taking the output of the loss function as an argument, which is convenient for floating averages.
BackgroundDataLoader(loadfun, n) executes function loadfun() n-times in background. Loaded items can be retrieved by take!, which is blocking, i.e. if there is no minibatch loaded, it will wait till one is available. If a minibatch is retrieved, a new call to loadfun() is issued. At the moment, there is no way to stop, may-be if the structure is destroyed.
To add loading samples on background, we can do
preparesamples() = (randn(2,100), rand(1:2,100))
bdl = BackgroundDataLoader(preparesamples, 10)
train!(loss, ps, bdl, opt, iterations)Just do not forget to run julia with more then single threads.
initializes a very simple callback function
cby, history = PrayTools.initevalcby(;accuracy = () -> accuracy(model))