what is python torch

python torch, also known as PyTorach, is a Python-first deep learning framework, an open source Python machine learning library for natural language processing and other applications. It not only enables powerful GPU acceleration, but also supports Dynamic neural networks are not supported by many mainstream frameworks such as Tensorflow.

PyTorch was developed by the Torch7 team. As can be seen from its name, its difference from Torch is that PyTorch uses Python as the development language.

The so-called "Python first" also means that it is a Python-first deep learning framework that not only achieves powerful GPU acceleration, but also supports dynamic neural networks, which is what many mainstream frameworks such as Tensorflow currently have. Unsupported.

PyTorch can be regarded as both numpy with GPU support and a powerful deep neural network with automatic derivation function. In addition to Facebook, it has also been used by Twitter, CMU and Salesforce. adopted by other institutions.

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Why use PyTorch

Faced with so many deep learning frameworks, why should we choose PyTorch? Isn't Tensorflow the default leader of deep learning frameworks? Why not choose Tensorflow directly but choose PyTorch? The following is an introduction to why you should use PyTorch in 4 aspects.

(1) Mastering a framework is not a one-time solution. No one has an absolute monopoly in deep learning now, not even Google, so just learning Tensorflow is not enough. At the same time, current researchers use various frameworks. If you want to see the code they implement, you need to at least understand the framework they use, so learn one more framework in case you need it.

(2) Tensorflow and Caffe are both imperative programming languages, and they are static. You must first build a neural network, and then use the same structure again and again. If you want to change the structure of the network, just Must start from scratch. But for PyTorch, through a reverse automatic derivation technology, you can change the behavior of the neural network arbitrarily with zero delay. Although this technology is not unique to PyTorch, it is the fastest implementation so far and can help you The implementation of any crazy idea achieves the highest speed and the best flexibility, which is also the biggest advantage of PyTorch compared to Tensorflow.

(3) The design idea of ​​PyTorch is linear, intuitive and easy to use. When you execute a line of code, it will be executed faithfully and there is no asynchronous world view, so when a bug appears in your code , you can use this information to easily and quickly find the erroneous code, and you will not waste too much time due to wrong directions or asynchronous and opaque engines when debugging.

(4) PyTorch’s code is more concise and intuitive than Tensorflow. At the same time, PyTorch’s source code is much friendlier and easier to understand than Tensorflow’s highly industrialized underlying code that is difficult to understand. Understand. It is definitely a pleasure to go deep into the API and understand the underlying layers of PyTorch. A framework whose underlying architecture can be understood will give you a deeper understanding of it.

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Finally, we briefly summarize the characteristics of PyTorch:

Support GPU;

Dynamic neural network;

Python first;

Imperative experience;

Easy expansion.

With so many advantages, PyTorch also has its shortcomings. Because this framework is relatively new, there are fewer people using it, which makes its community not so strong, but PyTorch provides There is an official forum where you can search for most of the questions you encounter. The answers there are usually provided by the author or other PyTorch users. The forum is also updated very frequently. You can also file an issue on Github. Generally, you will get a response from the developer very quickly, which can be regarded as solving the community's problem to a certain extent.

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