# 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 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)