PyTorch is one of the most popular deep learning frameworks and is based on the library Torch. Computer vision and natural language processing are two major use cases. When it was launched, it was easier to use with Graphics Processing Units (GPUs) and that explains why researchers preferred it over competitive offerings like TensorFlow. API calls in PyTorch are executed when they are called. This is also known as eager execution. TensorFlow originally added the calls to a graph for later execution. This is known as lazy or deferred execution. However, lately it has made its support for eager execution a lot better.
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PyTorch allows easy switching between eager and graph mode. This can be done using TorchScript. It’s production ready, easy to scale, and enables distributed training and performance optimization. The community has built a rich ecosystem of machine learning libraries on it. The community is so active that right after Open AI launched its services, Stable Diffusion was created in no-time and built on top of PyTorch using Generative Pre-trained Transformer 3 (or GPT-3). GPT-3 is an autoregressive language model that uses deep learning to produce human-like text.
Tensors and graphs are the core components of PyTorch. Tensors are like a multidimensional array that can run on GPUs to accelerate computing and are used to store and manipulate the inputs, outputs and parameters of a model. Graphs are data structures consisting of nodes and edges. PyTorch records all the tensors and executed operations in a directed acyclic graph (or DAG).
It’s based on dynamic computation, which means the graph is built and rebuilt at runtime. The code that performs the computation for forward pass also creates the data structure needed for back propagation. It was the first deep learning framework that was based on this principle but also matched the performance as well as capabilities of static graph frameworks. This is why it’s a great fit for a wide array of deep learning paradigms including convolutional networks and recurrent neural networks.
PyTorch is popular among researchers (data scientists) as well as software developers. It’s an easy-to-use API, easy to debug and comes with a wide range of built-in Python extensions. It's compatible with scikit-learn and Captum (for model operability) and has GPU support. GPUs are composed of hundreds of cores that handle thousands of threads simultaneously.
PyTorch is mainly used for Natural Language Processing (NLP) and computer vision. Models that treat language as a flat sequence of words use recurrent neural network model. However, the consensus is shifting towards language being a hierarchical tree of phrases. Recursive neural networks are deep learning models that can make this structure work even though traditionally they have been hard to implement and inefficient to run. However, PyTorch makes complex NLP models a lot easier to create and more efficient to run.
PyTorch is used for computer vision, including image classification, object detection and segmentation. Images consist of pixels. This collection of pixels can be represented using tensors, which makes PyTorch a great fit for computer vision related problems. In computer vision, we can start with a simple MNIST dataset consisting of handwritten digits. This could be represented with 28x28 pixels. This problem can easily be solved by a NumPy array. However, for more complex data sets it makes sense to use tensors. PyTorch tensors are better than NumPy arrays because tensors support parallel operations on GPU. For example, if we were to add the three color code (Red, Green and Blue), we would need an array of 3xWxH and, if this is a video of 100 frames, we may use the tensor of size 100x3x28x28.
Python is one of the most popular programming languages. However, performance isn’t its best suit and programmers find it to be quite slow. PyTorch uses Python’s strength which is ease of use. It also takes advantage of the high performance benefits of low-level languages like C and C++. All the heavy lifting in PyTorch is implemented in C/C++. This is made possible by using C/C++ as an extension to Python.
One of the most common use cases is working with NumPy, which is an extremely popular library and offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. While PyTorch enables conversion from NumPy to its own internal representation (a PyTorch tensor), and vice versa, it doesn’t copy the data over. It only uses a pointer to the raw data. Effectively, it’s just a reference that allow the memory to be shared.
With TorchServe, you can deploy PyTorch models in either eager or graph mode using TorchScript, serve multiple models simultaneously, version production models for A/B testing, load and unload models dynamically, and monitor detailed logs and customizable metrics.
As mentioned above, Python’s main strength is ease of use. However, in production environment, speed and efficiency are critically important, so it makes sense to use a standalone C++ program in production instead of Python. This can be done using TorchScript. Its main purpose is to create serializable and optimizable models from PyTorch code. The best part is that any TorchScript program can be saved from a Python process and loaded in a process without any Python dependency.
This means that the TorchScript program can be run independently as a standalone C++ program. In short, you are transitioning the Python model to a C++ program with no Python dependency. This enables researchers and developers have the best of the both worlds: speed up development using Python with it diverse ecosystem and high performance output in production using C++. This could be done in a few easy steps: convert the PyTorch model to TorchScript via tracing or annotation, serialize the script module and then load and execute script module in C++.
PyTorch is supported on all major cloud platforms. It provides frictionless development, GPU training and ability to run models in a highly scalable cloud environment through prebuilt images. Major cloud support for PyTorch has been discussed below:
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