Comments (3)
Thanks for pointing it out.
If you are using the code within Tensoflow that is not a problem because the memory allocation is handled by Tensorflow.
This is only a problem if you are using this as a standalone package.
I will fix it when I get the time or if you would like to help me out please submit a pull request.
Best,
Miguel
from permutohedral_lattice.
Hey Miguel,
I like to help you. I create a pull request in the next days. I use the Permutohedral Lattice for a pytorch backend.
greets Sebastian
from permutohedral_lattice.
Thanks,
I don't know how the pytorch GPU memory allocation works? Does it have its own allocator or does it rely on C++ allocation?
In Tensorflow, the Tensorflow memory allocator allocates most of the GPU memory (like 95%) at beginning independently of whether its needed or not. Because of this, when I was using C++ allocation instead of the Tensorflow allocator I would always run out of memory.
You should check whether this is the case in pytorch.
Best,
Miguel
from permutohedral_lattice.
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