In this article, we are going to demonstrate a comparison between PyTorch vs TensorFlow. The past few years have seen a huge surge of interest in Artificial Intelligence (AI) and Machine Learning (ML) technologies, which are being used to build more human-like applications. With the advent of artificial neural networks (eg., deep learning), higher complexity data and more sophisticated models have become accessible to the everyday programmer.
With a traditional ML system, a model needed to be trained on both data and feature sets (ie., labels), which meant that the data had to be structured and each value manually represented by a tag. This posed a problem for visual tasks such as image recognition.
Now, with artificial neural networks, one only needs to train with data and the model is responsible for finding patterns and links within the data. This works great with image, audio, video and even text data! Artificial neural networks seek to mimic the function of biological neural networks.
TensorFlow is an open-source deep learning framework created by developers at Google and released in 2015. The official research is published in the paper “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.”
PyTorch is one of the latest deep learning frameworks and was developed by the team at Facebook and open-sourced on GitHub in 2017. You can read more about its development in the research paper “Automatic Differentiation in PyTorch.”
Let us weigh the two frameworks below and do a summarized comparison between PyTorch vs TensorFlow:
While both Tensorflow and PyTorch are open-source, they have been created by two different wizards. Tensorflow is based on Theano and has been developed by Google, whereas PyTorch is based on Torch and has been developed by Facebook.
The most important difference between PyTorch vs TensorFlow is the way these frameworks define the computational graphs. While TensorFlow creates a static graph, PyTorch has the ability to build a dynamic graph.
With TensorFlow, the construction is static and the graphs need to go through compilation and then running on the execution engine that I previously mentioned.
PyTorch code makes our lives that much easier because PDB can be used. Again, by making use of the standard python debugger we will not require learning to make use of another debugger from scratch.
Tensorflow has a more steep learning curve than PyTorch. PyTorch is more pythonic and building ML models feel more intuitive. It’s similar to numpy, very pythonic, and integrates easily with the rest of the Python ecosystem. For example, you can simply throw in a PDB breakpoint anywhere into your PyTorch model and it’ll work. In TensorFlow, debugging the model requires an active session and ends up being much trickier.
Tensorflow has a much bigger community behind it than PyTorch. This means that it becomes easier to find resources to learn Tensorflow.
TensorBoard is a brilliant tool that enables visualizing your ML models directly in your browser. PyTorch doesn’t have such a tool
Finally, Tensorflow is much better for production models and scalability.
We have demonstrated some of the differences between PyTorch vs TensorFlow, to be fair, I would say PyTorch and TensorFlow are similar and I would leave it at a tie. But, in my personal opinion, I would prefer PyTorch over TensorFlow (in the ratio of 70% over 30%)However, this doesn’t mean PyTorch is better! At the end of the day, it comes down to what you would like to code with and what your organization requires! , AWS PS can help you in setting up your ML architecture so you don’t need to get involved into the complexity of the Archticiture setup.
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