You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Install DyNet with GPU support. This was the most difficult part. To achieve this, I had to install old CUDA 10.0, old gcc 7.3 and run:
CUDNN_ROOT=/path/to/cudnn BACKEND=cuda pip install git+https://github.com/clab/dynet#egg=dynet
Add --dynet-gpu flag to the command line arguments of OptionParser, otherwise OptionParser will report unsupported argument and exit.
For instance, I've added to sesame/targetid.py the following line:
optpr.add_option("--config", type="str", metavar="FILE")
+optpr.add_option("--dynet-gpu")
Finally, I had an error telling me that log_softmax operation does not have GPU implementation in DyNet. So had to move it to the CPU:
After these steps training started utilizing GPU. But... seems it became even slower than on CPU. Probably, need some code optimization to improve performance on GPU :((
The program uses CPU when training. How could I enable GPU?
The text was updated successfully, but these errors were encountered: