How to use a DataLoader in PyTorch? Last Updated : 24 Feb, 2021 Comments Improve Suggest changes Like Article Like Report Operating with large datasets requires loading them into memory all at once. In most cases, we face a memory outage due to the limited amount of memory available in the system. Also, the programs tend to run slowly due to heavy datasets loaded once. PyTorch offers a solution for parallelizing the data loading process with automatic batching by using DataLoader. Dataloader has been used to parallelize the data loading as this boosts up the speed and saves memory. The dataloader constructor resides in the torch.utils.data package. It has various parameters among which the only mandatory argument to be passed is the dataset that has to be loaded, and the rest all are optional arguments. Syntax: DataLoader(dataset, shuffle=True, sampler=None, batch_size=32) DataLoaders on Custom Datasets: To implement dataloaders on a custom dataset we need to override the following two subclass functions: The _len_() function: returns the size of the dataset.The _getitem_() function: returns a sample of the given index from the dataset. Python3 # importing the required libraries import torch from torch.utils.data import Dataset from torch.utils.data import DataLoader # defining the Dataset class class data_set(Dataset): def __init__(self): numbers = list(range(0, 100, 1)) self.data = numbers def __len__(self): return len(self.data) def __getitem__(self, index): return self.data[index] dataset = data_set() # implementing dataloader on the dataset and printing per batch dataloader = DataLoader(dataset, batch_size=10, shuffle=True) for i, batch in enumerate(dataloader): print(i, batch) Output: DataLoaders on Built-in Datasets: Python3 # importing the required libraries import torch from torch.utils.data import Dataset from torch.utils.data import DataLoader import seaborn as sns from torch.utils.data import TensorDataset # defining the dataset consisting of # two columns from iris dataset iris = sns.load_dataset('iris') petal_length = torch.tensor(iris['petal_length']) petal_width = torch.tensor(iris['petal_width']) dataset = TensorDataset(petal_length, petal_width) # implementing dataloader on the dataset # and printing per batch dataloader = DataLoader(dataset, batch_size=5, shuffle=True) for i in dataloader: print(i) Output: Comment More infoAdvertise with us Next Article How to use a DataLoader in PyTorch? d2anubis Follow Improve Article Tags : Python Python-PyTorch Practice Tags : python Similar Reads How to use GPU acceleration in PyTorch? PyTorch is a well-liked deep learning framework that offers good GPU acceleration support, enabling users to take advantage of GPUs' processing power for quicker neural network training. This post will discuss the advantages of GPU acceleration, how to determine whether a GPU is available, and how t 7 min read How to Split a Dataset Using PyTorch Splitting a dataset is an important step in training machine learning models. It helps to separate the data into different sets, typically training, and validation, so we can train our model on one set and validate its performance on another. In this article, we are going to discuss the process of s 6 min read PyTorch DataLoader PyTorch's DataLoader is a powerful tool for efficiently loading and processing data for training deep learning models. It provides functionalities for batching, shuffling, and processing data, making it easier to work with large datasets. In this article, we'll explore how PyTorch's DataLoader works 14 min read How to load CIFAR10 Dataset in Pytorch? The CIFAR-10 dataset is a popular resource for training machine learning models, especially in the field of image recognition. It consists of 60,000 32x32 color images in 10 different classes, with 6,000 images per class. The dataset is divided into 50,000 training images and 10,000 testing images. 3 min read How to Use Multiple GPUs in PyTorch PyTorch, a popular deep learning framework, provides robust support for utilizing multiple GPUs to accelerate model training. Leveraging multiple GPUs can significantly reduce training time and improve model performance. This article explores how to use multiple GPUs in PyTorch, focusing on two prim 5 min read Like