A Pytorch Iterable Dataset Example

A Pytorch Iterable Dataset Example

A Pytorch IterableDataset example can be extremely helpful when you’re trying to learn how to use this powerful tool. In this post, we’ll go over what an IterableDataset is and how you can use one to boost your productivity.

For more information check out this video:


In this post, I want to show you how to create an iterable dataset in Pytorch. You probably know that a dataset is a collection of samples (e.g., images) that can be read one by one. An iterable dataset is one that can be used by Pytorch’s DataLoader class to read the individual samples. Thanks to iterable datasets, we can create mini-batches of samples and train our neural networks much faster!

What is a Pytorch IterableDataset?

A Pytorch IterableDataset is a dataset that can be iterated over, similar to an iterator. The difference is that an IterableDataset can be used with the Pytorch DataLoader class to create a mini-batch for training or testing. An IterableDataset must implement the __iter__() and __len__() methods. The __iter__() method should return an object that has a __next__() method, and the __len__() method should return the number of data points in the dataset.

Why use a Pytorch IterableDataset?

The Pytorch IterableDataset is a useful tool when you need to perform data pre-processing on your dataset before passing it into your model. This can be especially helpful when you have a very large dataset that would take too long to fit into memory all at once. By using the Pytorch IterableDataset, you can process your data in small batches and pass it into your model one batch at a time.

To use the Pytorch IterableDataset, you first need to create a subclass of the Pytorch Dataset class. In this subclass, you will need to override the __iter__() method. This method is what allows the dataset to be used as an iterator. The __iter__() method should return an object that has a __next__() method, which returns the next batch of data from the dataset.

Below is an example of how to use the Pytorch IterableDataset:

import torch
from torch.utils.data import Dataset, IterableDataset

class MyIterableDataset(IterableDataset):

def __init__(self, data):
self.data = data

def __iter__(self):
return iter(self.data)

def get_batch(self, batch_size):

for i in range(0, len(self.data), batch_size):
yield self.data[i:i+batch_size]

dataset = MyIterableDataset([1, 2, 3])
dataloader = DataLoader(dataset, batch_size=2) # Create DataLoader with our dataset and desired batch size
for batch in dataloader: # Get batches one at a time from the DataLoader iterator print(batch) # prints [1 2], [3] “`

How to use a Pytorch IterableDataset?

An IterableDataset is a Pytorch dataset that can be iterated over like a standard Python iterator. The advantage of this is that it allows for easy, efficient streaming of data into your model during training. In this post, we’ll show you how to use an IterableDataset with a simple example.

Pytorch’s IterableDataset is defined in the torch.utils.data.dataset module. To use it, you first need to create a subclass that inherits from IterableDataset and implements the __iter__ method. This method should return an iterator that yields the data samples one at a time.

In our example, we’ll create an IterableDataset that streams randomly generated data into our model. Our __iter__ method will simply yield a new random data sample each time it is called. Let’s take a look at the code:

A Pytorch IterableDataset Example

In this Pytorch IterableDataset example, we’ll be creating a dataset that can be iterated over and used to train a model. IterableDatasets are a subclass of the Dataset class and can be used in the same way. The main difference is that an IterableDataset can be iterated over multiple times, which is useful when training a model.

To create an IterableDataset, we need to override the __iter__ method. This method should return an iterator that returns the data for each batch. In this example, we’ll be using a TensorDataset, which is a dataset that contains tensors.

First, we’ll create some data:
import torch
import numpy as np

#create some data
data = np.random.rand(100,3)
labels = np.random.randint(0,10,(100))

Next, we’ll create our dataset and iterator:

#create our dataset and iterator
dataset = TensorDataset(torch.from_numpy(data), torch.from_numpy(labels)) #create our dataset from the data and labels tensors it = iter(dataset) #get an iterator for our dataset so we can get batches of data X_batch, y_batch = next(it) #get the first batch of data print(‘Size of batch: ‘,X_batch.size()) #print the size of the first batch print(‘Batch of data : ‘, X_batch) #print the first batch of data print(‘Batch labels: ‘, y_batch) #print the first batch of labels]“`


Pytorch’s IterableDataset class is a convenient way to create an iterator for your dataset. In this tutorial, we’ve seen how to use it to create an iterator for a MNIST dataset. We’ve also seen how to use the tqdm module to make our training loop more efficient.

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