Good way to reshaffle/reacreate dataloader content?
I have dataset class like following and after each eposh refreshing it like this train_loader.dataset.load_shuffle_concat()Would it work as expected in multi gpu setting? By expected i mean same shuffle concat operation in each process and splitting all batches across gpus?Is it a good way to do what i want?class StressDataset(Dataset):
    def __init__(self, hp):
        self.file_path = hp.file_path
        self.max_len = hp.max_len
        self.load_shuffle_concat()

    # shuffle and concat strings up to target len
    def load_shuffle_concat(self):
        with open(self.file_path, 'r') as f:
            lines = f.readlines()

        # use torch for random source
        ids = torch.randperm(len(lines)).tolist()
        lines = np.array(lines)[[ids]]

        self.samples = []
        current_sample = {'x': []}
        for x in lines:
            if len(x) > self.max_len:
                continue
            if len(current_sample['x']) + len(x) > self.max_len:
                self.samples.append(current_sample)
                current_sample = {'x': x}
            else:
                current_sample['x'] += x
        self.samples.append(current_sample)

    def __len__(self):
        return len(self.samples)

    def __getitem__(self, idx):
        out = {}
        out['x'] = self.samples[idx].values()
        out['x'] = torch.tensor(out['x'])
        return out