PyTorch is an open source machine learning (ML) framework based on the Python programming language and the Torch library. It is one of the preferred platforms for deep learning research. The framework is built to speed up the process between research prototyping and deployment.
PyTorch is similar to NumPy and computes using tensors that are accelerated by graphics processing units (GPU). Tensors are arrays, a type of multidimensional data structure, that can be operated on and manipulated with APIs. The PyTorch framework supports over 200 different mathematical operations.
The popularity of PyTorch continues to rise as it simplifies the creation of artificial neural network (ANN) models. PyTorch is mainly used for applications of research, data science and artificial intelligence (AI).
Key Features of PyTorch
Some of the key features of PyTorch include:
- TorchScript- This is the production environment of PyTorch that enables users to seamlessly transition between modes. TorchScript optimizes functionality, speed, ease-of-use and flexibility.
- Dynamic graph computation- This feature allows users to change network behavior on the fly, rather than waiting for the entire code to be executed.
- Automatic differentiation- This technique numerically computes the derivative of a function by making backward passes in neural networks.
- Python support- Because PyTorch is based on Python, it can be used with popular libraries and packages such as NumPy, SciPy, Numba and Cynthon.
Using PyTorch can provide developers with the following benefits:
- Is based on Python, making it easy to learn and simple to code.
- Allows for easy debugging with popular Python tools.
- Is well supported on major cloud platforms, making it easy to scale.
- Has a small community of focused on open source.
- Can export learning models to the Open Neural Network Exchange (ONNX) standard format.
- Has a user-friendly interface.
- Offers a C++ front end interface option.
PyTorch vs. Tensorflow
However, PyTorch does come with its advantages over Tensorflow. PyTorch defines computational graphs in a dynamic way, unlike the static approach of Tensorflow. Dynamic graphs can be manipulated in real time instead of at the end. Additionally, Tensorflow has a steeper learning curve as PyTorch is based on intuitive Python.
Tensorflow may be better suited for projects that require production models and scalability, as it was created with the intention of being production-ready. PyTorch is easier and lighter to work with, making it a good option for creating prototypes quickly and conducting research.