Modified code with the article "How to Build a Streaming DataLoader with PyTorch" at https://medium.com/speechmatics/how-to-build-a-streaming-dataloader-with-pytorch-a66dd891d9dd.

itera_dataloader_example.py

import random
from itertools import chain, cycle, islice
import torch.utils.data as data

import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import time
import torch
import numpy as np

# added by CCJ;
def count(start=0, step=1):
    # count(10) --> 10 11 12 13 14 ...
    # count(2.5, 0.5) -> 2.5 3.0 3.5 ...
    n = start
    while True:
        yield n
        n += step

#> see:https://gist.github.com/david-macleod/2b933d28fd3ac09766785728ee191f09
def plot_timings(loader, n_batches, model_time=0.2, max_time=2.5):
    
    fig, ax = plt.subplots()
    ax.set_axisbelow(True)
    ax.yaxis.grid(which="major", color='black', linewidth=1)
    
    zero_time = time.time()
    
    worker_ids = {}
    worker_count = count() # added by CCJ;
    for result in islice(loader, n_batches):
        start = time.time()
        time.sleep(model_time)
        end = time.time()
                
        # check if already batched
        if isinstance(result[0], torch.Tensor):
            result = zip(*result)
            
        batch = []
        #print ('result = ', result)
        for item in result:
            data, worker, t1, t2 = tuple(map(scalar, item))
            #print ("processing worker id = %d"% worker )
            # fix worker position in plot
            if worker != -1:
                if worker not in worker_ids:
                    worker_ids[worker] = next(worker_count)
                worker = worker_ids[worker]
   
            plot_time_box(data, worker, t1-zero_time, t2-zero_time, ax)
            batch.append(data)
        
        batch_str = ",".join(map(str, batch))
        plot_time_box(batch_str, -1, start-zero_time, end-zero_time, ax, color='firebrick')
    
    max_worker = len(worker_ids) - 1 
        
    ax.set_xlim(0, max_time)
    ax.set_ylim(-1.5, max_worker + 0.5)
    ax.set_xticks(np.arange(0, max_time, 0.2))
    ax.set_yticks(np.arange(-1, max_worker+1, 1))
    ax.set_yticklabels([])
    ax.tick_params(axis='y', colors=(0,0,0,0))
    
    fig.set_figwidth(16)
    fig.set_figheight((max_worker + 2) * 0.5)
    
    ax.xaxis.label.set_color('gray')
    ax.tick_params(axis='x', colors='gray')
    for spine in ax.spines.values():
        spine.set_edgecolor((0,0,0,0))
    # for showing image 
    #plt.show()
        
def scalar(x):
    return x.item() if hasattr(x, 'item') else x

def plot_time_box(data, worker, t1, t2, ax, color='steelblue'):
    x = t1
    y = worker - 0.25
    w = t2 - t1
    h = 0.6
    
    rect = Rectangle((x, y), w, h, linewidth=2, edgecolor='black',facecolor=color)
    
    ax.add_patch(rect)
    
    ax.text(x + (w * 0.5), y + (h * 0.5), str(data), va='center', ha='center', color='white', weight='bold')  

class MyIterableDatasetV5(data.IterableDataset):
    def __init__(self, data_list, batch_size):
        self.data_list = data_list
        self.batch_size = batch_size
    
    @property
    def shuffled_data_list(self):
        return random.sample(self.data_list, len(self.data_list))
    
    def process_data(self, mydata):
        for x in mydata:
            worker = torch.utils.data.get_worker_info()
            worker_id = worker.id if worker is not None else -1
            
            start = time.time()
            time.sleep(0.1)
            end = time.time()
            
            yield x, worker_id, start, end
            
    def get_stream(self, data_list):
        return chain.from_iterable(map(self.process_data, cycle(data_list)))
    
    def get_streams(self):
        return zip(*[self.get_stream(self.shuffled_data_list) for _ in range(self.batch_size)])
    
    def __iter__(self):
        return self.get_streams()

if 0:
    my_data_list = [[10, 11, 12, 13],
                    [20, 21, 22, 23],
                    [30, 31, 32, 33],
                    [40, 41, 42, 43],
                    [50, 51, 52, 53],
                    [60, 61, 62, 63],
                    [70, 71, 72, 73],
                    [80, 81, 82, 83],
                    [90, 91, 92, 93],
                ]

    my_batch_size = 4
    my_num_workers = 2
    iterable_dataset = MyIterableDatasetV5(my_data_list, batch_size = my_batch_size)
    loader = data.DataLoader(iterable_dataset, batch_size=None, num_workers=my_num_workers)
    plot_timings(loader, model_time=0.2, n_batches = my_batch_size)


class MyIterableDatasetV7(data.IterableDataset):
    def __init__(self, data_list, batch_size):
        self.data_list = data_list
        self.batch_size = batch_size
    
    @property
    def shuffled_data_list(self):
        return random.sample(self.data_list, len(self.data_list))
    
    def process_data(self, mydata):
        for x in mydata:
            worker = torch.utils.data.get_worker_info()
            worker_id = id(self) if worker is not None else -1
            
            start = time.time()
            time.sleep(0.1)
            end = time.time()
            
            yield x, worker_id, start, end
            
    def get_stream(self, data_list):
        return chain.from_iterable(map(self.process_data, cycle(data_list)))
    
    def get_streams(self):
        return zip(*[self.get_stream(self.shuffled_data_list) for _ in range(self.batch_size)])

    def __iter__(self):
        return self.get_streams()

    @classmethod
    def split_datasets(cls, data_list, batch_size, max_workers):
        for n in range(max_workers, 0, -1):
            if batch_size % n ==0:
                num_workers = n
                break
        
        split_size = batch_size // num_workers
        return [cls(data_list, batch_size=split_size) for _ in range(0, num_workers)]

class multiStreamDataLoader:
    def __init__(self, datasets):
        self.datasets = datasets
    def get_stream_loaders(self):
        return zip(*[torch.utils.data.DataLoader(dataset, num_workers=1, batch_size=None) for dataset in self.datasets])
    def __iter__(self):
        for batch_parts in self.get_stream_loaders():
            yield list(chain(* batch_parts))

if 1:
    my_data_list = [
        [10, 11, 12, 13],
        [20, 21, 22, 23],
        [30, 31, 32, 33],
        [40, 41, 42, 43],
        [50, 51, 52, 53],
        [60, 61, 62, 63],
        [70, 71, 72, 73],
        [80, 81, 82, 83],
    ]

    my_batch_size = 4
    my_num_workers = 4
    print ('[***] iterable dataset with %d workers' % my_num_workers)
    dataset_tmp = MyIterableDatasetV7.split_datasets(my_data_list, batch_size = my_batch_size, max_workers= my_num_workers)
    loader = multiStreamDataLoader(dataset_tmp)
    plot_timings(loader, model_time=0.2, n_batches = 6)

Thanks!