7 min read

A typical deep learning workflow starts with ideation and research around a problem statement, where the architectural design and model decisions come into play. Following this, the theoretical model is experimented using prototypes. This includes trying out different models or techniques, such as skip connection, or making decisions on what not to try out.

PyTorch was started as a research framework by a Facebook intern, and now it has grown to be used as a research or prototype framework and to write an efficient model with serving modules.

The PyTorch deep learning workflow is fairly equivalent to the workflow implemented by almost everyone in the industry, even for highly sophisticated implementations, with slight variations. In this article, we explain the core of ideation and planning, design and experimentation of the PyTorch deep learning workflow.

This article is an excerpt from the book PyTorch Deep Learning Hands-On by Sherin Thomas and Sudhanshi Passi. This book attempts to provide an entirely practical introduction to PyTorch. This PyTorch publication has numerous examples and dynamic AI applications and demonstrates the simplicity and efficiency of the PyTorch approach to machine intelligence and deep learning.


Ideation and planning

Usually, in an organization, the product team comes up with a problem statement for the engineering team, to know whether they can solve it or not. This is the start of the ideation phase. However, in academia, this could be the decision phase where candidates have to find a problem for their thesis. In the ideation phase, engineers brainstorm and find the theoretical implementations that could potentially solve the problem. In addition to converting the problem statement to a theoretical solution, the ideation phase is where we decide what the data types are and what dataset we should use to build the proof of concept (POC) of the minimum viable product (MVP).

Also, this is the stage where the team decides which framework to go with by analyzing the behavior of the problem statement, available implementations, available pretrained models, and so on. This stage is very common in the industry, and I have come across numerous examples where a well-planned ideation phase helped the team to roll out a reliable product on time, while a non-planned ideation phase destroyed the whole product creation.

Design and experimentation

The crucial part of design and experimentation lies in the dataset and the preprocessing of the dataset. For any data science project, the major timeshare is spent on data cleaning and preprocessing. Deep learning is no exception from this.

Data preprocessing is one of the vital parts of building a deep learning pipeline. Usually, for a neural network to process, real-world datasets are not cleaned or formatted. Conversion to floats or integers, normalization and so on, is required before further processing. Building a data processing pipeline is also a non-trivial task, which consists of writing a lot of boilerplate code. For making it much easier, dataset builders and DataLoader pipeline packages are built into the core of PyTorch.

The dataset and DataLoader classes

Different types of deep learning problems require different types of datasets, and each of them might require different types of preprocessing depending on the neural network architecture we use. This is one of the core problems in deep learning pipeline building.

Although the community has made the datasets for different tasks available for free, writing a preprocessing script is almost always painful. PyTorch solves this problem by giving abstract classes to write custom datasets and data loaders. The example given here is a simple dataset class to load the fizzbuzz dataset, but extending this to handle any type of dataset is fairly straightforward. PyTorch’s official documentation uses a similar approach to preprocess an image dataset before passing that to a complex convolutional neural network (CNN) architecture.

A dataset class in PyTorch is a high-level abstraction that handles almost everything required by the data loaders. The custom dataset class defined by the user needs to override the __len__ and __getitem__ functions of the parent class, where __len__ is being used by the data loaders to determine the length of the dataset and __getitem__ is being used by the data loaders to get the item. The __getitem__ function expects the user to pass the index as an argument and get the item that resides on that index:

from dataclasses import dataclassfrom torch.utils.data import Dataset, [email protected](eq=False)class FizBuzDataset(Dataset):    input_size: int    start: int = 0    end: int = 1000    def encoder(self,num):        ret = [int(i) for i in '{0:b}'.format(num)]        return[0] * (self.input_size - len(ret)) + ret    def __getitem__(self, idx):        x = self.encoder(idx)        if idx % 15 == 0:            y = [1,0,0,0]        elif idx % 5 ==0:            y = [0,1,0,0]        elif idx % 3 == 0:            y = [0,0,1,0]        else:            y = [0,0,0,1]        return x,y           def __len__(self):        return self.end - self.start

The implementation of a custom dataset uses brand new dataclasses from Python 3.7. dataclasses help to eliminate boilerplate code for Python magic functions, such as __init__, using dynamic code generation. This needs the code to be type-hinted and that’s what the first three lines inside the class are for. You can read more about dataclasses in the official documentation of Python (https://docs.python.org/3/library/dataclasses.html).

The __len__ function returns the difference between the end and start values passed to the class. In the fizzbuzz dataset, the data is generated by the program. The implementation of data generation is inside the __getitem__ function, where the class instance generates the data based on the index passed by DataLoader. PyTorch made the class abstraction as generic as possible such that the user can define what the data loader should return for each id. In this particular case, the class instance returns input and output for each index, where, input, x is the binary-encoder version of the index itself and output is the one-hot encoded output with four states. The four states represent whether the next number is a multiple of three (fizz), or a multiple of five (buzz), or a multiple of both three and five (fizzbuzz), or not a multiple of either three or five.

Note: For Python newbies, the way the dataset works can be understood by looking first for the loop that loops over the integers, starting from zero to the length of the dataset (the length is returned by the __len__ function when len(object) is called). The following snippet shows the simple loop:

dataset = FizBuzDataset()for i in range(len(dataset)):    x, y = dataset[i]dataloader = DataLoader(dataset, batch_size=10, shuffle=True,                     num_workers=4)for batch in dataloader:    print(batch)

The DataLoader class accepts a dataset class that is inherited from torch.utils.data.Dataset. DataLoader accepts dataset and does non-trivial operations such as mini-batching, multithreading, shuffling, and so on, to fetch the data from the dataset. It accepts a dataset instance from the user and uses the sampler strategy to sample data as mini-batches.

The num_worker argument decides how many parallel threads should be operating to fetch the data. This helps to avoid a CPU bottleneck so that the CPU can catch up with the GPU’s parallel operations. Data loaders allow users to specify whether to use pinned CUDA memory or not, which copies the data tensors to CUDA’s pinned memory before returning it to the user. Using pinned memory is the key to fast data transfers between devices, since the data is loaded into the pinned memory by the data loader itself, which is done by multiple cores of the CPU anyway.

Most often, especially while prototyping, custom datasets might not be available for developers and in such cases, they have to rely on existing open datasets. The good thing about working on open datasets is that most of them are free from licensing burdens, and thousands of people have already tried preprocessing them, so the community will help out. PyTorch came up with utility packages for all three types of datasets with pretrained models, preprocessed datasets, and utility functions to work with these datasets.

This article is about how to build a basic pipeline for deep learning development. The system we defined here is a very common/general approach that is followed by different sorts of companies, with slight changes. The benefit of starting with a generic workflow like this is that you can build a really complex workflow as your team/project grows on top of it.

Build deep learning workflows and take deep learning models from prototyping to production with PyTorch Deep Learning Hands-On written by Sherin Thomas and Sudhanshu Passi.

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