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For a while, the machine learning community was split between two major libraries, Tensorflow and PyTorch.
Google Research has launched a new library, Jax, that has grown in popularity since.
This article compares Jax and PyTorch to decide which is better and worth learning.
What is Jax?
Jax is amachine-learningframework, much like PyTorch and TensorFlow.
Deepmind developed it at Google, and while it is not an official Google product, it remains popular.
According to thewebsite, Jax combines Autograd and XLA to provide high-performance numerical computing.
It provides a Numpy-like API to build machine-learning models.
However, Jax functions run on GPUs and TPUs.
As a result, they are faster than Numpys functions which only run on a CPU.
In addition, Jax provides functions for performing transformations on your functions.
The main three functions are jit, grad, and vmap.
Also read:What is Google JAX?
What is PyTorch?
PyTorchis a machine-learning library based on the Torch framework.
PyTorch was originally built by Facebook and is open-source under the Linux Software Foundation.
It is one of the most popular machine-learning frameworks alongside Tensorflow.
Many companies use it for theirdeep learningmodels, such as Tesla.
PyTorch is made up of two main features tensor computation with GPU support and deep neural networks.
Final Words
So which one should you choose?
Well, there definitely is no clear winner here.
Each library has its ideal use case, benefits, and quirks.
When it comes to learning, I would recommend being familiar with both.
Next, check out our guide on how to install PyTorch on Windows and Linux.
