Leaderboard

Just switch to Release option on the MSVS toolbar. As to the differences, you may read about them e.g. here . To add to there, debug builds depend on the debug version of Visual Studio Runtime libraries, whereas release builds depend on common MSVCRT DLLs, that are very likely already installed in the system. Hence if you compile debug app or library and deploy it to machines without Visual Studio installed, it will ask you for the debug DLLs or even the whole Visual Studio to be installed. Release app/DLL on the other hand usually requires Visual C++ Redistributable Runtime only.

For MSVS - on the left of the function name, for LabVIEW - in the CLFN settings. extern "C" __declspec(dllexport) int __stdcall Mult(int a) { // will be exported with stdcall convention }

Did you try Release build also? I noticed, that you're exporting Mult function without explicit specification of the calling convention. By default Visual Studio exports in cdecl convention. But in LabVIEW you set stdcall convention. Likely it's not the reason for errors though.

Hi everyone, I have recently been playing around with the Tensorflow support built into the Vision Development Module. It seems to work fine and I have the basics up and running. There are a couple of problems though that I would like to solve. Firstly, it seems to not use GPU acceleration at all which mostly makes it useless for real time processing at any sensible frame rate. Secondly, it is locked into the VDM which is not cheap and also totally closed source. Under the hood the regular Tensorflow DLLs are called, but via a layer of the Vision toolkit. Third, for this closed source reason we are locked into whey ever version of Tensorflow NI chooses. I totally get that from their business point of view, but conversely I suspect that there is very little push inside NI to update this regularly due to all the hurdles that come with it. My proposal is to implement some kind of community edition of Tensorflow API that wraps the Tensorflow DLL directly in LabVIEW and exposes hardware acceleration capabilities. Anybody interested in collaboration? I know a little bit about Tensorflow, but not enough to be productive, so my first step would just be to mimic the API provided by NI which is deliberately quite simple.