Deep Learning Framework

Hint: The original link is https://viso.ai/deep-learning/pytorch-vs-tensorflow/

For example: TensorFlow, Pytorch, Keras, Chainer, Paddle, etc. Deep learning frameworks provide neural network units, loss functions, and optimizers to help us assemble and train neural network models. Among them, TensorFlow and Pytorch are the most widely used deep learning frameworks. This article discusses the comparison between these two.

1. Tensorflow2.0

Tensorflow is a very popular end-to-end open source platform for machine learning backed by Google. The current latest version is May 20212.5.0 released on the 14th.

TensorFlow 2.0 focuses on simplicity and ease of use. Compared with the steep learning curve of version 1.x, the difficulty of getting started with deep learning in 2.0 is greatly reduced. Replaces Google’s DistBelief framework and runs on nearly all available execution platforms (CPU, GPU, TPU, Mobile, etc.). The advantages and disadvantages of TensorFlow 2.0 are as follows:

1. Advantages of Tensorflow

Support and Library Management: TensorFlow is supported by Google and new features are released frequently. It is widely used in production environments.

Open Source: TensorFlow is an open source platform that is widely available and very popular.

Data Visualization: TensorFlow provides a tool called TensorBoard to visualize data graphically. It also allows for easy debugging of nodes, reduces the effort of viewing the entire code, and efficiently parses neural networks.

Keras Compatibility: TensorFlow is compatible with Keras, allowing its users to write some high-level functional parts and provide TensorFlow with system-specific functionality (pipelines, estimators, etc.).

Extremely scalable: The per-machine nature of TensorFlow allows its users to develop any type of system.

Compatibility: TensorFlow is compatible with languages such as C++, JavaScript, Python, C#, Ruby, and Swift. This allows users to work from the comfort of their environment.

**Architectural Support:** Due to the parallelism of the working model, TensorFlow is used as a hardware accelerated library. It uses different allocation strategies in GPU and CPU systems. TensorFlow also has its architecture TPU, which can calculate faster than GPU and CPU. As a result, models built using TPUs can be easily and cheaper deployed on the cloud and executed much faster. However, TensorFlow’s architecture TPU only allows executing the model, not training it.

2. Disadvantages of Tensorflow

Benchmarks: Computational speed is where TensorFlow lags behind the competition. Its usability is lower compared to other frameworks.

Symbol cycle: While TensorFlow reduces code size and makes it more accessible to users, it increases complexity for other users. Every code needs to be executed using whatever platform for its support, which increases the dependency of execution.

Symbolic loops: TensorFlow is designed for deterministic sequences and cannot provide symbolic loops for indefinite sequences. .

GPU support: TensorFlow only supports NVIDIA and Python for GPU programming, as there is growth in other languages in deep learning.

Second, PyTorch

PyTorch was first introduced in 2016. Before PyTorch, deep learning frameworks usually focused on speed or usability, but not both. PyTorch combines a focus on usability with careful consideration of performance, provides an imperative and Pythonic programming style, supports code as a model, makes debugging easy, and is consistent with other popular scientific computing libraries , while remaining efficient and supporting hardware accelerators such as GPUs.

PyTorch is a Python library that performs dynamic tensor computations with automatic differentiation and GPU acceleration while maintaining performance comparable to the fastest deep learning libraries available today. Today, most of its core is written in C++This is one of the main reasons why PyTorch can achieve much lower overhead compared to other frameworks.

For now and for the foreseeable future, as it moves from beta to production, PyTorch seems best suited to dramatically shorten the design, training, and testing cycle of new neural networks for specific purposes. Hence, it has become very popular in the research community.

Several popular deep learning software are built on top of PyTorch, including Tesla Autopilot or Uber’s Pyro.

1.PyTorch advantage

PyTorch is based on Python: PyTorch is Python-centric or “pythonic”, and is designed to integrate deeply with Python code, rather than as an interface to libraries written in other languages. Python is one of the most popular languages used by data scientists and one of the most popular languages for building machine learning models and ML research.

Easier to learn: Due to its syntax similar to traditional programming languages like Python, PyTorch is easier to learn than other deep learning frameworks.

Debugging: PyTorch can be debugged using one of many widely used Python debugging tools, such as Python’s pdb and ipdb tools.

Dynamic Computational Graphs: PyTorch supports dynamic computational graphs, which means that network behavior can be changed programmatically at runtime. This makes it easier to optimize models and gives PyTorch a major advantage over other machine learning frameworks, which treat neural networks as static objects.

**Data Parallelism:**Data parallelism allows PyTorch to distribute computational work across multiple CPU or GPU cores. While this kind of parallelism can be done in other machine learning tools, it is much easier in PyTorch.

Community: PyTorch has a very active community and forum (discuss.pytorch.org). Its documentation (pytorch.org) is very well organized and helpful for beginners; it stays up to date with PyTorch releases and offers a set of tutorials. PyTorch is very simple to use, which also means that the learning curve for developers is relatively short.

2. PyTorch Disadvantages

**Lack of model serving in production:** While this will change in the future, other frameworks are more widely used for real production work (even though PyTorch is becoming more popular in the research community). Therefore, the documentation and developer community is smaller compared to other frameworks.

**Limited monitoring and visualization interface:** While TensorFlow also comes with a powerful visualization tool (TensorBoard) for building model graphs, PyTorch does not yet have such a thing. As a result, developers can use one of the many existing Python data visualization tools or connect externally to TensorBoard.

Not as broad as TensorFlow: PyTorch is not an end-to-end machine learning development tool; development of real applications requires translating PyTorch code into another framework, such as Caffe2, to deploy applications to servers, workstations and mobile devices.

Summary of PyTorch vs TensorFlow

TensorFlow and PyTorch implementations show equivalent accuracy. However, the training time of TensorFlow is much longer, but the memory usage is lower.
PyTorch allows for faster prototyping than TensorFlow, but if custom functionality is required in the neural network, TensorFlow may be a better choice.
TensorFlow treats neural networks as static objects; if you want to change the behavior of your model, you have to start from scratch. Using PyTorch, it is possible to dynamically tune neural networks at runtime， making it easier to optimize the model.
Debug for developers. Effective debugging with TensorFlow requires a special debugger tool that enables you to inspect how network nodes are computed at each step. PyTorch can be debugged using one of many widely available Python debugging tools.
Both PyTorch and TensorFlow provide ways to speed up model development and reduce the amount of boilerplate code. However, the core difference between PyTorch and TensorFlow is that PyTorch is more “pythonic” and based on an object-oriented approach. At the same time, TensorFlow provides more options to choose from, so it has more flexibility overall.