问题
I am currently building a 3d convolutional network, for video classification. The main problem is that I run out of memory too easily. Even if I set my batch_size to 1, there is still not enough memory to train my CNN the way I want.
I am using a GTX 970 with 4Gb of VRAM (3.2Gb free to use by tensorflow). I was expecting it to still train my network, maybe using my RAM memory as a backup, or doing the calculations in parts. But until now I could only run it making the CNN simpler, which affects performance directly.
I think I can run on CPU, but it is significantly slower, making it not a good solution either.
Is there a better solution than to buy a better GPU?
Thanks in advance.
回答1:
Using gradient checkpointing will help with memory limits.
来源：https://stackoverflow.com/questions/48466255/is-there-a-workaround-for-running-out-of-memory-on-gpu-with-tensorflow