I’m running into exactly the same problem when trying to work through lesson 1: everything seems to be working except that only the CPU is utilized and training is therefore super slow.
Here’s the output when I run the command:
=== Software ===
python : 3.7.3
fastai : 1.0.51
fastprogress : 0.1.21
torch : 1.0.1
torch cuda : 10.0 / is available
torch cudnn : 7401 / is enabled
=== Hardware ===
torch devices : 1
- gpu0 : GeForce GTX 1060 6GB
=== Environment ===
platform : Windows-10-10.0.17134-SP0
conda env : fastai_v1
python : C:\Users\Patrick\Anaconda3\envs\fastai_v1\python.exe
sys.path :
C:\Users\Patrick\Anaconda3\envs\fastai_v1\python37.zip
C:\Users\Patrick\Anaconda3\envs\fastai_v1\DLLs
C:\Users\Patrick\Anaconda3\envs\fastai_v1\lib
C:\Users\Patrick\Anaconda3\envs\fastai_v1
C:\Users\Patrick\Anaconda3\envs\fastai_v1\lib\site-packages
no nvidia-smi is found
I already tried reinstalling the respective modules but to no awail and I’m about to install Ubuntu to see if I get it working there.
Since you posted the problem 2 weeks ago: did you manage to find a solution yet?
num_workers is number subprocesses to use for data loading. 0 means that the data will be loaded in the main process.
Increasing the num_workers works for me. And wrapping the code in “if name == ‘main’:” and “main”.
But… doesn’t this only mean we do not have the nvidia-smi monitor utility up and running? The very slow training time from lesson 1 benchmark (my GTX 1070 runs 1 cycle in 1:50 instead of 0:20s-0.30s) maybe happens because we have a .jpeg bottleneck and cannot install pillow-simd instead of pillow, as reported for example from How to install precompiled pillow-simd into conda env ?
You exactly described my problem (my GTX 1070 runs 1 cycle in 1:50 instead of 0:20s-0.30s). I have installed pillow-simd but nothing has changed. Have you solved it?