Comments (1)
ghenter
commented on August 16, 2024
Thanks for the compliment, @nlpofwhat!
In issue #5, you can read some more information about the environment and the Python version used. @simonalexanderson might be able to supply CUDA/cuDNN-related information.
As detailed in issue #3 and in Table 1 in our StyleGestures paper, we trained our models on a single GPU. In fact, as mentioned in our readme here on GitHub, we have found that training on multiple GPUs gives incorrect results with the distributed backend we used due to a limitation in the Glow implementation we built our work upon. Seeing that we used the same GPU model as you (Nvidia GeForce RTX 2080 Ti), I think your computational resources are sufficient for training these models.
from stylegestures.
Related Issues (20)
- Did you experiment with different batch sizes in training? HOT 2
- Strange results when I train with multiple GPUs. HOT 2
- bvh files with fixed frames
- Difference between time_steps and seqlen? HOT 2
- Possible bug when computing the log-det of Jacobian for affine coupling HOT 2
- About datasets HOT 1
- For the freshmen about Gesture Generation HOT 2
- Questions about the latent random variable Z HOT 2
- The Python version
- About the Cuda version HOT 4
- About the swapaxes for self.x and self.cond HOT 6
- This dataset link doesn't seem to work. HOT 2
- The dataset are inconsistent HOT 10
- Excuse me, where can I find the dataset used by Example3 in readme? HOT 11
- This is the curve when I use two different data sets for training, and the parameters are the same. It can be roughly seen that the loss in Figure 1 will be lower than that in Figure 2, which can indicate that the performance of the first trained model will be better? HOT 6
- How to apply the output file(*.bvh) to 3D model file(*.3ds) HOT 3
- Some questions about the style control HOT 2
- Some questions about the style control HOT 1
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- The trained model posture shakes badly. What might be the cause? Is there any way to solve this problem? HOT 2
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from stylegestures.