I'm currently having a problem with this, where I load a large deep learning model into "CPU" memory then move it to the GPU, but I can't get rid of the memory reserved by the process.
In your case you should first consider the possibility that there is a pointer to your model's objects that is for some reason not being released. It might simply be that even though you are moving your model the GPU and maybe removing any of your own references, there might be internal references to your model's data that is hidden from you. At least something to consider.
edit: To add to this, I'm now quite sure (though I could be wrong!), that whether python does or does not use sbrk with a negative value is beyond the scope of python. Python is making use of malloc/free under the hood:
https://github.com/python/cpython/blob/master/Objects/obmall...
There's some flexibility for wrapping free in different ways in that file, but it seems like it'll basically always be using free at the core. At least on my system in a debugger I just verified that. So if it's true that python by default uses malloc/free, then the question of whether sbrk with a negative number comes into play is more a question of how your libc implements malloc/free.
Of course I might be wrong, but I think that you should probably stop worrying about it at that level and instead look into object references first as I detailed above.
Which framework do you use for deep learning? It can allocate some object on its own.
Can you give me some stats when using a model and after it's no longer in use and can't be accessible? You can get it by calling the sys._debugmallocstats() function.