Experiment Seeding for GPUs

set_seed.py

import os
import numpy as np
import random

import tensorflow as tf
# from tfdeterminism import patch - only in older versions (<2?) of Tensorflow

# import torch

def seed(seed=42):
  # -- Python & Numpy -- 
  random.seed(seed)
  np.random.seed(seed)
  
  # -- Tensorflow --
  tf.random.set_seed(seed)
  tf.experimental.numpy.random.seed(s)
  os.environ['TF_CUDNN_DETERMINISTIC'] = '1'
  os.environ['TF_DETERMINISTIC_OPS'] = '1'
  # tf.set_random_seed(s) - Older Version
  
  # -- Pytorch --
  # torch.manual_seed(seed)
  # The following may slightly slow down training:
  # torch.use_deterministic_algorithms(True)
  # os.environ['CUBLAS_WORKSPACE_CONFIG']=':4096:8' # see https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
  # torch may also require specifying seeded worker for dataloader
  
# https://github.com/NVIDIA/framework-determinism