PyTorch is a machine learning framework developed by Facebook in October 2016. It is open-source and based on the popular Toshi library. PyTorch is designed to provide good flexibility and great speed for deep neural network implementation.
Initially, PyTorch was developed by Hugh Perkins as a Python wrapper for LusJIT according to the Torch framework. There are two versions of PyTorch.
PyTorch redesigns and uses Torch in Python while sharing the same root C
background code libraries. PyTorch developers are adjusting this back end code for it Python works well. They also maintain GPU-based hardware acceleration and increasing features that made Lua-based Torch
Why PyTorch
- Python API
- You Can Use CPU, GPU(CUDA Only)
- Supports Common Platform (Windows, Linux, IOS)
- PyTorch is a small framework that allows you to work closely with neural network planning
- Focus on the machine that reads the part, not the outline itself
- Pythonic control flow
- Flexible
- Clean and intuitive code
- Easy to fix error - Python debugger
- With PyTorch, we can use the python debugger
- Not all work in a C ++ environment
Advantages
- Easy Interface
PyTorch provides an easy-to-use API. that’s why it’s so it is considered very easy to operate again works on Python. Making a code for this The framework is simple - Python Usage
This library is considered Pythonic integrates well with Python data
a stack of science. Therefore, it can use all files for services and functionality provided by Python Nature. - Computational Graphs
PyTorch offers an excellent platform that is provides dynamic calculation graphs. Therefore, a the user can change them during operation. This
is very useful when the engineer does not know at all How much memory is needed to create a file neural network model
Why Do You Have To Use PyTorch
- PyTorch Is Pythonic
- Easy To Learn
- Higher Developer Community
- Data Parallelism
- Hybrid Front-End
Companys Usages PyTorch
Requirements & Installation
Typically, PyTorch works on any platform that supports 64-bit Python development environment. That is enough to train and evaluate very simple examples and lessons.
For Installation, It’s pretty straight-forward based on the system properties such as the Operating System or the package managers. It can be installed from the Command Prompt or within an IDE such as PyCharm etc.
