TensorFlow vs. PyTorch: Deep Learning Showdown

TensorFlow vs PyTorch Deep Learning Showdown
In the past few years, we have seen a huge shift toward Artificial Intelligence and Machine Learning technologies. These technologies are used a lot in building human-like applications. More complex models and data with greater complexity are now available to regular programmers thanks to artificial neural networks (DNNs), such as deep learning.
When we talk about Deep learning then it won’t be wrong if we say that it has transformed the AI world. At the forefront of this revolution, there are 2 big names – PyTorch and TensorFlow. These cutting-edge frameworks have become synonymous with advances in research and practical applications.
In this blog, we are going to compare these two big players. So, stick with us to know more about it.

What is Deep Learning?

Before we start to compare Tensor and PyTorch, we need to know clearly about deep learning. So, first, let’s understand Deep Learning!

Often, people use various terms like ‘deep learning’, ‘machine learning’, and ‘Artificial Intelligence’ interchangeably, and this results in a lot of confusion. Deep Learning and Machine Learning are part of the AI family, and Deep Learning is also a subset of Machine Learning.

Deep learning copies the neural pathways of the human brain while processing data for decision-making, detecting objects, identifying speech, and translating languages. It learns without any human support.
A hierarchical level of artificial neural networks, designed to resemble the human brain with neuron nodes linking in a web, is used in deep learning to process machine learning. Deep learning’s hierarchical function allows machines to analyze data in a nonlinear way, whereas typical machine learning programs operate with data analysis linearly.

What is TensorFlow?

Google created TensorFlow, an end-to-end open-source deep learning framework, which was made available in 2015. It is renowned for its support for various platforms, including Android, scalable production and deployment options, documentation and training support, and multiple abstraction levels.
TensorFlow is a symbolic math framework for neural networks ideal for dataflow programming across various workloads. It provides many abstraction layers for model development and training.
TensorFlow looks very promising in this Deep Learning space. It offers great flexibility, libraries, and tools that help in building & deploying machine learning applications.

Advantages of TensorFlow

Here are some of the top advantages of TensorFlow:

  • Scalable : Tensorflow works with a variety of devices. It functions on mobile devices just as well as it does on other sophisticated machinery. The library is so well specified that it may be deployed on a variety of devices.
  • Graphs : TensorFlow offers better data visualization than any other library available out there.

  • Compatibility : TensorFlow is compatible with a lot of programming languages such as Python, C++, JavaScript, etc. This helps users to work in a comfortable environment.
  • Architectural Support : Utilizing TPU instead of CPU and GPU speeds up calculation in the TensorFlow architecture. Compared to the other two, models developed on TPU are easier to deploy over clouds and operate more quickly.
  • Debugging : Tensorboard, a feature of Tensorflow, makes node debugging simple. By doing this, the overhead of viewing the entire code is decreased.

Cons of TensorFlow

  • Absence of Windows Support : Beyond all of Tensorflow’s benefits, its feature set for Windows users is extremely constrained. This isn’t the case with Linux users, who have access to a vast array of capabilities.
  • Frequent Updates : Tensorflow is frequently updated, making it necessary for users to uninstall and reinstall it on a regular basis so that it can bond and be mixed with the most recent upgrades.
  • Reliance : TensorFlow adds a layer of complexity to the software even if it makes it smaller and easier to use. Every code needs a platform in order to run, which raises reliance.

What is PyTorch?

PyTorch is a comparatively new deep-learning framework based on Torch. PyTorch is developed by the research group of Facebook, it is used for natural language processing applications. PyTorch makes coding more manageable and increases the processing speed.

Advantages of PyTorch

  • Easy to Use : PyTorch has been widely regarded by users as one of the most user-friendly deep learning frameworks due to its straightforward model definition and hyperparameter configuration. Numerous reviewers reported that they had little trouble learning PyTorch’s fundamentals and getting started on developing their models right away.
  • Excellent documentation and community assistance : Users greatly appreciate the PyTorch community and documentation. Many reviewers have expressed gratitude for the thorough documentation that was supplied, stating that it made troubleshooting easier and improved their understanding of the framework. Furthermore, a lot of people have mentioned that they were able to quickly solve their issues when they asked the vibrant online community for assistance.
  • Versatile : PyTorch is versatile for research & development purposes. It is considered an easy-to-use framework for people who have just kept their footsteps in the field of Artificial Intelligence. It provides a huge range of data types and model architecture selections that make it suitable for both research experiments and production usage. Many reviewers have expressed a positive view of PyTorch especially for its module writing and seamless integration of various layers/architectures.

Cons of PyTorch

  • Lack of Monitoring & Visualization Tools : PyTorch does not have monitoring and visualization tools like other frameworks. This is one of the big limitations for people who rely on these tools for better insights into their model’s performance.
  • Inefficient Dataloaders : Many users are reporting that PyTorch’s dataloaders are not working well and are leading to a terrible performance during the training process.
  • Scalability Issues and Limited Platform Support : Although PyTorch is offering some kinds of scalability, it is still difficult to implement it into complex projects. In addition, this interpreter only has C++ API, and it needs models to be deployed on a mobile platform environment.

TensorFlow vs PyTorch: History

Both libraries have commercial licenses and open-source resources available that can be provided in common space preferences.
In 2015 Creation of TensorFlow was done by the Google Brain Team, and still, it is being utilized for both research and production in the company.
However, PyTorch consists of Torch which is a popular framework used by Facebook, that was initially designed as an advanced version of NumPy. Yet, the library started developing Caffe 2 (Convolutional Architecture for Fast Feature Embedding) at the start of 2018 which led to a split in the field of deep learning and data analysis.

TensorFlow vs PyTorch: Prevalence

TensorFlow was superior to any other deep learning libraries in the preceding short timeframe. Among the main things that make it good are the fact that a large number of people are aware of it, and we also have an active community and forum in support of it and the regular press from Google themselves. The application, TensorFlow Lite, also comes along with the TensorFlow family. A notable example of hardware used for running TensorFlow Lite at a higher rate is the ASIC chip, the sole key to Android smartphones. Additionally, TPU is a hardware instrument part of the Google Cloud Platform.
Google came up with an AI/ML course that teaches in-depth machine learning fundamental and practical AI using the basic level of programming languages as well as absolutely for free! By and large, hackers would be familiar with PyTorch and thus, it would be allocated to the challenger segment. It has the ability to practically take the place of NumPy, an array processing software, which is very well-used and popular in the field today. In fact, for any and all beginner data scientists, machine learners, or enthusiasts, NumPy is the first library they might learn to use. By and large, PyTorch’s main interface is kept almost identical to that of NumPy, which ensures a smooth transition for the Python coders.
On the opposite side, in the beginning, TensorFlow struck us as a counterintuitively defined system like placeholders and session ideas. This can make TensorFlow for a high learning curve. This is indeed one of the most vital reasons why PyTorch is preferred by many.

TensorFlow vs PyTorch: Technical Differences

  • Dynamic Computational Graphs 
    Dynamic computational graphs are the essential feature of the PyTorch model, but in TensorFlow it is impossible. The computational graphs are used by the deep learning frameworks in order to define the network’s operation modes in which tasks must be accomplished in the artificial neural network.

    Static graphs need to be represented before the model is exercised. Here there is no means for quick prototyping at all or as well, and it is a rather laborious affair. For example, before the TensorFlow can be run, the graph structure needs to be created first. But, in PyTorch, you can perform dynamic feeding of the graph and its editability. This is beneficial when developer productivity is worked on with respect to variable length inputs that are used in RNNs, and it contributes toward enhancing model performance.

  • Deployment 
    A REST API is the conventional means just between the AI/ML model and the user. To allow for easy access, a Flask server has been created for most Python models. A Flask server is just like a library, it cuts out one step of wrapping around both the libraries.

    TensorFlow leads the way as the most fitting download in mobile and embedded solutions. Among the tools is for example TensorFlow Lite and others and this ensures a seamless connection to the frontends of iOS and Android devices.

    TensorFlow Serving is another cool functionality that is a part of this handy tool. Modeling is a continuous process, and models lose their popularity over time and the usage of fresh data to retrain them is the only solution. TensorFlow ML is the tool that fully addresses the need to break with the previous models and preserve the aliveness of the whole.

  • Saving and Loading Models 
    The fact that both libraries upload and download models well is something they do well. This public API in PyTorch offers weights for the model to be stored. The process is easier during the replication stage. As an addition, it is also possible to pick the whole model.

    TensorFlow additionally performs the saving/loading function very efficiently. Model in our way can be transferred through a protocol buffer with not only parameters but also operations by defining the protocol buffer structure of the full model. Therefore, neuromorphic programming enables your model to also be portable to other languages like Java or C++. This is especially helpful when there’s a chance you might need to rely on a different deployment stack from the one that supports Python.

  • Ease of Use and Flexibility
    PyTorch particularly experienced a great demand by virtue of its simplicity and simplicity of use. The interest of researchers and developers to be more fluent in Python is another reason for this. Beyond the commonly used features of other major libraries, like Tensorflow or Keras, PyTorch incorporates the dynamic computational graph architecture for more convenient building of models and debugging, which makes it great for rapid prototyping and experimental purposes.

    On the contrary, TensorFlow enhances the development of an integrated and complete system for the deployments and scale-up that are needed in production. On the other hand, TensorFlow can be heavy because of its static graph nature thus it is a very good framework for building elaborate models easily.

  • Community and Ecosystem


    To be more specific, TensorFlow and PyTorch are supported by their outstandingly large and active communities of people that offer a lot of help and assets. The TensorFlow framework from Google is a great example of how an organization with good standing and big resources can make an AI tool that the industry wants to incorporate and become accustomed to Google Cloud services.

    PyTorch, fostered by Facebook’s AI Research lab (FAIR), is dear to research and academic community, encouraged by many libraries focused on the areas, for instance, imaging (torchvision) or language processing (torchtext), that give it a vibrant ecosystem and the edge of cutting-edge results.


In this respective phenomenon of deep learning frameworks, TensorFlow and PyTorch have been positioned in the leading ranks. Google, became the creator that launched the famous TensorFlow in 2015. On the other side, PyTorch, the Torch framework’s innovation that originated in Facebook, is prioritizing simplicity and speed prototyping which is more attractive to researchers.
The decision to choose will always depend on your purpose and goals. Make sure to be clear with your goals and select the one that helps you achieve them. This guide has all the information that will make this task easy for you!
Vikas Agarwal is the Founder of GrowExx, a Digital Product Development Company specializing in Product Engineering, Data Engineering, Business Intelligence, Web and Mobile Applications. His expertise lies in Technology Innovation, Product Management, Building & nurturing strong and self-managed high-performing Agile teams.

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