There seems two be two reasons for this:
- 0.4 introduced 0-dimensional tensors (scalars) and to get their value as a python float we need to call
.item() on them. If we don't (and train on GPU) yellowfin will hold on to tensors on both CPU and GPU and try to do operations on them (which will cause an exception since they are on different devices, that exception will be swallowed by the checkpoint restoration mechanism).
- Tensors and Variables have been merged in 0.4, so unless the code is changed yellowfin will hold on to tensors with gradient history causing a memory leak.
The first issue seems to be quite easy to patch, I can send a pull-request for that part if you want to.
There seems two be two reasons for this:
.item()on them. If we don't (and train on GPU) yellowfin will hold on to tensors on both CPU and GPU and try to do operations on them (which will cause an exception since they are on different devices, that exception will be swallowed by the checkpoint restoration mechanism).The first issue seems to be quite easy to patch, I can send a pull-request for that part if you want to.