How to recover if training is interrupted

[xyt000-xjj]

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Desktop (please complete the following information):

• OS: [e.g. Linux Ubuntu 20.04, Windows 10]
• PyTorch version (e.g., pytorch 1.9.0)
• CUDA toolkit version (e.g., CUDA 11.4)
• NVIDIA driver version
• GPU [e.g., Titan V, RTX 3090]
• Docker: did you use Docker? If yes, specify docker image URL (e.g., nvcr.io/nvidia/pytorch:21.08-py3)

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[PDillis]

Just point to the last .pkl that was saved and want to resume from with the --resume argument in train.py. Note that the new training will start from 0, so account for that when setting how many images to train for in --kimg.

[therealjr]

@PDillis I understand that training will resume from that point. However, when I save a snap at tick 0 it shows nothing but blurred images. Why is it that the images are resetting entirely? Shouldn't it be generating data like it was trained on from the point it left off at?

[dookiethedog]

My Gan crashed and I was extremely annoyed as I was experiencing the exact same issue so I decided to read into the code. You can set the inital augmentation and kimg in the training_loop.py file, this will help but this will not actually continue the training from when it last ran it will only give it an idea where to start off again. The Dev's don't seem to care if it does crash as there is no proper resume code, I was actually able to modify the code and create a perfect resume function, however, I will not be able to resume from my first Gan as I did not have my code added yet so there is no way to pull the settings needed, but at least for future I will be all good and have everything stored in the pickle file.