| |
| Reproducibility |
| =============== |
| |
| Completely reproducible results are not guaranteed across PyTorch releases, |
| individual commits or different platforms. Furthermore, results need not be |
| reproducible between CPU and GPU executions, even when using identical seeds. |
| |
| However, in order to make computations deterministic on your specific problem on |
| one specific platform and PyTorch release, there are a couple of steps to take. |
| |
| There are two pseudorandom number generators involved in PyTorch, which you will |
| need to seed manually to make runs reproducible. Furthermore, you should ensure |
| that all other libraries your code relies on an which use random numbers also |
| use a fixed seed. |
| |
| PyTorch |
| ....... |
| You can use :meth:`torch.manual_seed()` to seed the RNG for all devices (both |
| CPU and CUDA) |
| |
| import torch |
| torch.manual_seed(0) |
| |
| |
| CuDNN |
| ..... |
| When running on the CuDNN backend, one further option must be set:: |
| |
| torch.backends.cudnn.deterministic = True |
| |
| .. warning:: |
| |
| Deterministic mode can have a performance impact, depending on your model. |
| |
| Numpy |
| ..... |
| If you or any of the libraries you are using rely on Numpy, you should seed the |
| Numpy RNG as well. This can be done with:: |
| |
| import numpy as np |
| np.random.seed(0) |
