Comments (4)
If you're using cuda, the code provided in this repo should automatically be enabling parallel processing on available GPU hardware:
See here:
if device == th.device("cuda"):
self.gen = DataParallel(self.gen)
self.dis = Discriminator(depth, latent_size, use_eql=use_eql, gpu_parallelize=True).to(device)
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@wcxchunxin, Glad to know that your problem is solved.
@BlindElephants, Thanks a lot for helping @wcxchunxin out. You do seem to have spent a lot of time in this code. 😄. Thanks again.
Best regards,
@akanimax
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Related Issues (20)
- When you update this repo with styleGAN HOT 1
- What are the parameters for CIFAR10 dataset? I have been training with lr=0.003, betas=(0,0.99). Even after 5 epochs, I am not getting recognizable results at any resolution.
- pretrained models
- I want to stop and resume the training continuously. HOT 3
- Would you add compute IS and FID code ?? HOT 1
- Why output of torch.cat() is still C channels??
- WGAN-GP for Training MSG_ProGAN is failed HOT 3
- demo.py
- I got HOT 1
- Model collapse HOT 5
- Why smaller batch size results better results? With the epochs increased, the G loss went up? HOT 1
- when I train the model ? The d_loss is always 0.0 ? Is there some problems ? HOT 4
- WGAN-GP loss: averaging penalties or not?
- Can't load images | KeyError: 'SM_CHANNEL_TRAINING' HOT 2
- Error(s) in loading state_dict for DataParallel in generating_samples.py
- How to process the resolution of Flower dataset
- Question regarding higher resolution generation HOT 1
- Question about projecting generated images back to latent space
- MSG-cycleGAN HOT 3
- Msggan quality results
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