How to Set Random Seeds for Reproducibility with `torch.manual_seed()` in PyTorch

Reproducibility is a fundamental aspect of research and development in machine learning. When utilizing libraries like PyTorch for building neural networks or any stochastic models, you often want to ensure that your results are replicable. This is where setting random seeds becomes essential. In this article, we'll cover how to set random seeds for reproducibility using torch.manual_seed().

Understanding Randomness in Machine Learning

Machine learning algorithms often depend on random processes, whether it's splitting datasets, initializing weights in a neural network, or ordering data for stochastic gradient descent. Without controlling these random processes, different executions of the same program might yield varying results. This unpredictability can complicate debugging and verifying results, which is why setting a random seed is critical for robustness and reliability in your experiments.

Setting Random Seeds with torch.manual_seed()

PyTorch provides a simple way to control randomness through the use of torch.manual_seed(). This function sets the seed for generating random numbers, which ensures that the sequence of random numbers remains the same across different runs of the program.

Basic Usage of torch.manual_seed()

To set a random seed in PyTorch, use:

import torch

# Setting the seed
torch.manual_seed(42)

Once this seed is set, any subsequent calls to random functions in PyTorch will yield the same results every time you run your code.

Ensuring Comprehensive Reproducibility

While torch.manual_seed() is effective, true reproducibility often requires setting the seed for all used libraries that involve random generation. Here’s how you can achieve this:

Setting Seed Across Libraries

In a typical PyTorch program, you might want to set seeds for other libraries such as Python's built-in random generator and NumPy. Here's a comprehensive way to do it:

import torch
import numpy as np
import random

# Seed value
torch.manual_seed(42)
np.random.seed(42)
random.seed(42)

# For devices with CUDA
if torch.cuda.is_available():
    torch.cuda.manual_seed(42)
    torch.cuda.manual_seed_all(42)  # for multi-GPU

Setting the seed for these libraries typically solidifies your code's reproducibility, especially when experiments involve data manipulation and transformations using NumPy or direct harnessing of random generators from the standard library.

Reproducibility in a Multi-Device Environment

When utilizing multiple GPUs, set seeds for all GPUs using torch.cuda.manual_seed() and torch.cuda.manual_seed_all().

Caveats and Considerations

Even after setting seeds, complete reproducibility can be elusive. Certain operations might be non-deterministic depending on your hardware or algorithms' specifics. PyTorch strives to alert users with warnings when non-deterministic operations are used. You can maximize reproducibility by:

• Setting torch.backends.cudnn.deterministic = True to force the selection of deterministic algorithms.
• Setting torch.backends.cudnn.benchmark = False to stop PyTorch from optimizing based on runtime, which can introduce variability.

torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

Conclusion

Reproducibility in machine learning is not only beneficial but often necessary. By setting random seeds using torch.manual_seed() and configuring related library seeds, you ensure more reliable and explainable outcomes in your research. Keep in mind the potential exceptions with non-deterministic algorithms as you work, and employ the provided configurations to mitigate reproducibility issues. By doing so, you enhance the quality and reliability of your computational experiments and machine learning research.