Introduction
This week’s assignment was to train cyclegan. As I have previously explored this during my midterm, a lot of this wasn’t really new to me. I decided to train the van gogh to photo dataset.
Process
As expected the training task is very annoying because of the walltime issue. I actually tried training this on the NYUSH HPC servers but because of the weird issues with not having enough space on the compute servers. I could not get the requirements installed, so I did not have the chance to train the model with gtx 1080s… Additionally I think because my capstone was also training I could not get the compute quota I needed.. very troubling issues. Also I realized I did not get the images outputted during the training process so I can only get the images from inference.
Results
I did not spend a lot of epochs training because of walltime so the results are not good. There are baseline photos online of this model which I will share.
Conclusion
For most models it seems much better to train on GPU. I wanted to get better results on the NYU HPC, but I am still unsure why the requirements filled my disk quota, perhaps I will try that again in the future if I have time.