TensorFlow vs PyTorch vs Jax – Compared

In this article, we try to explore the 3 major deep learning frameworks in python – TensorFlow vs PyTorch vs Jax. These frameworks however different have two things in common –

• They are Open-Sourced. It means if you feel there is a bug in the library, you can post an issue in GitHub(and get it fixed). You can add your own features in the library as well.
• Python is internally slow due to the global interpreter lock. So these frameworks use C/C++ as a backend that handles all the computations and parallel processes.

We will be highlighting the most important points about each of these frameworks and try to answer which one is best suited for you.

TensorFlow vs PyTorch vs Jax – Quick Overview

TensorFlow

TensorFlow developed by Google is currently the most popular machine learning library. These are some of the important features of TensorFlow:

• It is a very user-friendly framework to start with. The availability of high-level API -Keras makes the model layers definition, loss function and model creation very easy.
• TensorFlow2.0 comes with eager execution which uses dynamic type graphs. This makes the library more user friendly and is a significant upgrade from previous versions.
• This high-level interface of Keras has certain disadvantages. As TensorFlow abstracts away a lot of underlying mechanisms (solely for the convenience of the end-user), it leaves the researchers with less freedom as to what they can do with their model.
• One of the most attractive things Tensorflow has to offer is the TensorBoard, which is in fact the TensorFlow visualization toolkit. It allows you to visualize loss function, model graphs, profiling etc.

So if you are starting with Deep Learning or looking to deploy your model easily TensorFlow can be a good framework to start with. TensorFlow Lite makes it easier to deploy ML models to mobile and edge devices. You can check out the official GitHub Repo to gain more insight into the framework.

PyTorch

PyTorch(Python-Torch) is a machine learning library from Facebook. It is slowly catching up in popularity with TensorFlow. Some of the most important features of PyTorch are:

• Unlike TensorFlow, PyTorch uses Dynamic Type Graphs, which means the execution graph is created on the go. It allows us to modify and inspect the internals of the graph at any time.
• Apart from the user-friendly high-level APIs, PyTorch does have a well-built low-level API which allows more and more control over your Machine Learning model. We can inspect and modify the output during the forward and backward pass of the model during training. This proves to be very effective for gradient clipping and neural style transfer.
• PyTorch allows extending their code, add new loss functions and user-defined layers easily. PyTorch autograd is powerful enough to differentiate through these user-defined layers. Users can also choose to define how the gradients the calculated.
• PyTorch has a wide range of support for data parallelism and GPU usage.
• PyTorch is more pythonic than TensorFlow. PyTorch fits well into the python ecosystem, which allows using Python debugger tools for debugging PyTorch code.

PyTorch due to its high flexibility has attracted the attention of many academic researchers and industry. It is easy and intuitive to learn. PyTorch also has great community support in case you run into some problems. Make sure to check out more of PyTorch from the repository hosted in GitHub.

Jax

Jax is a relatively new machine learning library from Google. It is more of an autograd library that can differentiate through every native python and NumPy code. Let us look at some of the features of JAX:

• As the official site describes it, JAX is capable of doing Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.
• The most important aspect of JAX as compared to PyTorch is how the gradients are calculated. In torch, the graph is created during the forward pass and the gradients are calculated during the backward pass. On the other hand in JAX, the computation is expressed as a function. Using grad() on the function returns a gradient function that computes the gradient of the function for the given input directly.
• JAX is an autograd tool, using it alone is barely a good idea. There are various JAX-based ML libraries, notable of them are ObJax, Flax and Elegy. Since all of them use the same core and the interface is just a wrapper around the JAX library, we put them under the same bracket.
• Flax is originally developed under the PyTorch ecosystem. It focuses more on the flexibility of use. On the other hand, Elegy is more of Keras inspired. ObJAX was mainly designed for research-oriented purposes which focuses more on simplicity and understandability. It in fact goes with the tagline – by the researchers for the researchers.

JAX is becoming increasingly popular day by day. A lot of researchers are using JAX for their experiments, attracting a bit of traffic from PyTorch. JAX is still in its infancy and is not recommended for people who are just starting out with exploring Deep Learning( for now). It takes some mathematical expertise to play with the state of the art. Visit the official repository to learn more about this promising new library.

Which one do you choose?

The answer to choosing between TensorFlow vs PyTorch vs Jax is completely dependent on the purpose of your usage. However, if you won’t go wrong with either of these libraries if you’re working on a machine learning project as a beginner. Once you get into the advanced ML modeling, your requirements will become specific enough for you to identify the best library to suit you.

Until then, stay tuned and keep learning!