Comments (2)
First, you hardware configuration seems quite good. When we work with any 3d network, it is important. For the training configuration, I remember that I used 3 GPU, so it is normal that the initial batch size doesn't fit into 1 single GPU. My advice is to allow make sure the batch size is always greater than 32 (it make sure the statistics you capture help the model to generalize well). Another advice is to use apex library for mix precision training (https://github.com/NVIDIA/apex). The library wasn't mature enough when I did this project.
Another important thing is the loading. If you have SSD, make sure your data are stored on it (it greatly increase the loading speed). A poor loading leads to GPU starvation, which slow down the training process.
The training could take several days (depending on your configuration).
If you like you like this project, feel free to leave a star. (it is my only reward ^^)
from tutorial-about-3d-convolutional-network.
Thanks for the prompt response.
Based on the tensorboard, it seems like yours also took like a day and 14hrs for 350 epochs. Thanks for the suggestion though. I already left the star.
from tutorial-about-3d-convolutional-network.
Related Issues (9)
- Prediction code HOT 1
- Code for csv HOT 1
- Gpu memory usage HOT 1
- What should I do? HOT 3
- Explain these variables?
- I just solved problem
- model training HOT 1
- Dependency versions HOT 1
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