Description

def set_seed(seed):
    random.seed(seed)  # Python random module.
    np.random.seed(seed)  # Numpy module.
    set_seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    torch.random.manual_seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU.
    torch.backends.cudnn.benchmark = cudnn_benchmark
    torch.backends.cudnn.deterministic = cudnn_deterministic

Contributor guide

Tech stack

pythonpytorch

Domain

machine learning

Issue type

research

DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.

3

Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.

under 1 hour

Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.

needs maintainer response

ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.

needs investigation

Prerequisites

Basic understanding of PyTorchKnowledge of reproducibility concepts

Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.

40

Research direction

Investigate the xformers repository for existing utilities or documentation related to random seed setting and reproducibility. Look at the issue history for similar questions or answers. Consider contributing by adding a clear guide or a utility function to the documentation. Refer to the PyTorch reproducibility documentation for best practices.