The sound of war: Tensorflow VS Pytorch

In the age of deep learning, many programmers are turning to the most popular frameworks in deep learning, the TensorFlow library from Google, and the PyTorch library from Facebook.?

What is the best between them and what is the difference between the two libraries? In this article, we will answer these two questions, and we will explain more about the difference between them.

Tensorflow, produced by Google in 2015, is a deep learning library, built on machine learning algorithms, this framework is used in many fields of engineering, economics, medicine, and many other fields, and Tensorflow have relied on Python as a programming language to build many models.

Many companies and organizations use tensorflow in their businesses such as Deep Mind, Coca-Cola, Intel and many other leading companies in their field.

Advantages and disadvantages of tensorflow.?

It is natural that tensorflow has advantages and disadvantages, because tensorflow is not a unit in the field, but there are many other libraries that provide solutions sometimes better than tensorflow, and this is a natural thing as a result of competition, hence the idea of comparing between different offices, and the emergence of advantages?And disadvantages for each library of them, so the disadvantages of some frameworks converge with the advantages of other frameworks.

Advantages:

1-permanent support:

Because Google is the development of this framework, so there is great support and development for it, and a distinguished community support.?

2- Debugging:

It implements subparts of the graph, giving it the ability to present and retrieve discrete data.?

3-Pipelining:

TensorFlow is designed to use various back-end software (ASIC-GPUs).?

4-Scalability:

The libraries are deployed on a hardware machine, which is a cellular device to the computer with a complex setup.

5-High performance:

Tensorflow has a very high and distinctive performance, which makes it one of the preferred frameworks for many developers

Disadvantages:

1-For the strength of the structure and its uniqueness, it is difficult to get errors and debugging in the tensorflow

2-No support for OpenCL.?

3-There is a wide variety of users who are comfortable in a window environment rather than Linux, and Tensorflow doesn't satisfy these users.

4-Missing Symbolic loops:

In the variable-length sequence, tensorflow does not provide functionality.?

PyTorch, is a framework based on machine learning algorithms, used in deep learning projects, and this framework is used in many fields, produced in 2016 by Facebook.?

Recently a lot of developers have started leaving tensorflow and moving to Pytorch.?

This framework has many advantages and disadvantages, like other frameworks.?

Let's know the advantages and disadvantages of the Pytorch library.?

Advantages:

1-Rich set of powerful APIs to extend the Pytorch Libraries.

2-Supports cloud platforms.?

3-Debugging tools which help ease debugging.

4-Flexible, fast and supports GPU and CPU.

5-Support for arithmetic graph at run time.

6-Easy and simple to learn and program.

Disadvantages:

1-Not widely known, because it was released in 2016 is relatively new.

2-The developer community is relatively small compared to other libraries like Tensorflow?

3-Absence of monitoring and visualization tools like a tensor board.

This was a simple comparison between two libraries of the most important offices in the field of deep learning, and here the question is not which is better but rather which is most appropriate for your work.?