Thanks Eric, I failed to mention that I am one of the main developers of Lora ( so I basically wrote this README note :) ). I was just trying to figure out why Lora fails with Julia 0.5, but I can now see that it is almost certainly due to a recent change in ForwardDiff, since the latter package has a "data" function.
On Sunday, 3 January 2016 00:18:38 UTC, Eric Forgy wrote: > > Hi, > > I don't use Lora.jl, but opening up the repo on GitHub, I see the tests on > master are failing. It's probably a good idea to stick with the latest > tagged release. > > The README also has this warning: > > "Lora has undergone a major upgrade. Some of its recent changes include: > > Models are represented internally by graphs. > > Memory allocation and garbage collection have been reduced by using > mutating functions associated with targets. > > It is possible to select storing output in memory or in file at runtime. > > Automatic differentiation is available allowing to choose between forward > mode and reverse mode (the latter relying on source transformation). > > To run the current version of Lora, it is needed to Pkg.checkout() both > Lora and ReverseDiffSource. Both packages will be pushed to METADATA very > soon. > > Some of the old one has not been fully ported. The full porting of old > functionality, as well as further developments, will be completed shortly. > Progress is being tracked systematically via issues and milestones. > > The documentation is out of date, but will be brought up-to-date fairly > soon. In the meantime, this README file provides a few examples of the new > interface, explaining how to get up to speed with the new face of Lora." > > Hope this helps. > >
