Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Modules can hold parameters of different types on different devices, so it's not always possible to unambiguously determine the device. The recommended workflow in PyTorch is to create the device object …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
The PyTorch doc Reproducibility has very detailed instructions on how to make your model training reproducible. Basically, you need the following code.
torch.manual_seed(args.seed)
np.random.seed(args.seed …Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
PyTorch uses pickle to serialize and deserialize objects.
The PyTorch convention is
to use the file extension .pt or .pth for saving model (or its parameters)
and use the file extension …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Module.__call__ method register all hooks and call the method Module.forward.
In short,
when you train the model you should use the method forward,
while when you test the …Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
https://github.com/ray-project/ray/tree/master/python/ray/util/sgd/pytorch/examples
Distributed PyTorch Using Apache Ray
RaySGD: Distributed Training Wrappers