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PyTorch vs TensorFlow: What are the differences?

Introduction

In this article, we will discuss the key differences between PyTorch and TensorFlow, two popular deep learning frameworks.

  1. Graph Construction: PyTorch is an imperative, or define-by-run, framework, where the computational graph is defined on the go as the code is executed. This enables dynamic graph construction and allows for easy debugging and efficient use of control flow structures. On the other hand, TensorFlow follows a declarative programming model, where the computational graph is defined before the code is executed. This static graph construction enables better optimization and deployment on various devices.

  2. Ease of Use: PyTorch provides a more intuitive and pythonic interface, making it easier for beginners to learn and use. It offers a more straightforward and interactive debugging experience due to its imperative nature. TensorFlow, on the other hand, has a steeper learning curve, mainly due to its static graph construction and verbosity. However, TensorFlow's mature ecosystem and community support offer more extensive resources and tools for development.

  3. Deployment and Production: TensorFlow has better support for deployment and production scenarios. Its ability to optimize and convert models for deployment on various devices, including mobile and embedded systems, makes it a preferred choice for production use cases. PyTorch, although catching up, still lags behind TensorFlow in terms of deployment options and production-readiness.

  4. Community and Ecosystem: TensorFlow has a larger and more mature community compared to PyTorch. It has been around for a longer time and has gained more popularity in the industry. This leads to a wider range of available models, pre-trained networks, and third-party libraries and tools. PyTorch, however, is gaining popularity rapidly and has a growing community. It is known for its research-friendly ecosystem and is widely used in academia and research.

  5. Model Building Flexibility: PyTorch offers more flexibility in building complex models, as it allows for dynamic graph construction and easy integration of Python control flow operations. This makes it easier to experiment and iterate on model architectures and enables faster prototyping. TensorFlow, with its static graph construction, provides better optimization and performance for large-scale production models.

  6. Visualization and Debugging: PyTorch offers better tools for visualization and debugging, making it easier to understand and debug model behavior. Its integration with libraries like Matplotlib and TensorBoardX allows for real-time visualization of scalar values, images, and computation graphs. TensorFlow also provides visualization tools like TensorBoard, but PyTorch's visual debugging capabilities are more user-friendly and accessible.

In Summary, PyTorch is an imperative framework with dynamic graph construction and a more beginner-friendly interface, while TensorFlow is a declarative framework with static graph construction and better deployment options. PyTorch excels in flexibility and research, while TensorFlow shines in production readiness and support.

Decisions about PyTorch and TensorFlow

Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.

I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.

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Fabian Ulmer
Software Developer at Hestia · | 3 upvotes · 50K views

For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.

We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.

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Xi Huang
Developer at University of Toronto · | 8 upvotes · 92K views

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

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A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

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Pros of PyTorch
Pros of TensorFlow
  • 15
    Easy to use
  • 11
    Developer Friendly
  • 10
    Easy to debug
  • 7
    Sometimes faster than TensorFlow
  • 32
    High Performance
  • 19
    Connect Research and Production
  • 16
    Deep Flexibility
  • 12
    Auto-Differentiation
  • 11
    True Portability
  • 6
    Easy to use
  • 5
    High level abstraction
  • 5
    Powerful

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Cons of PyTorch
Cons of TensorFlow
  • 3
    Lots of code
  • 1
    It eats poop
  • 9
    Hard
  • 6
    Hard to debug
  • 2
    Documentation not very helpful

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What is PyTorch?

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

What is TensorFlow?

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

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What are some alternatives to PyTorch and TensorFlow?
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Caffe2
Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.
MXNet
A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
Torch
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
See all alternatives