PyTorch vs TensorFlow.js: What are the differences?
PyTorch and TensorFlow.js can be primarily classified as "Machine Learning" tools.
PyTorch and TensorFlow.js are both open source tools. It seems that PyTorch with 29.6K GitHub stars and 7.18K forks on GitHub has more adoption than TensorFlow.js with 11.2K GitHub stars and 816 GitHub forks.
Suggestic, cotobox, and Depop are some of the popular companies that use PyTorch, whereas TensorFlow.js is used by 8villages, ADEXT, and Taralite. PyTorch has a broader approval, being mentioned in 21 company stacks & 46 developers stacks; compared to TensorFlow.js, which is listed in 5 company stacks and 3 developer stacks.
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.
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.
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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