For a while, the machine learning community was split between two major libraries, Tensorflow and PyTorch.
However, for its ease of use, PyTorch has emerged to be the more popular library among the two, but Google seems not to be giving up without a fight. 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 a machine-learning framework, much like PyTorch and TensorFlow. Deepmind developed it at Google, and while it is not an official Google product, it remains popular.
According to the website, 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 Numpy’s 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.
Jax can be sued for making faster numeric computations. This is because Jax has a Numpy-like API but runs on GPUs and TPUs.
Developers use Jax to calculate gradients of functions in order to train models.
Jax is mostly used for building research models.
Benefits of Jax
Jax includes autograd, which enables developers to compute gradients of functions easily when building models.
It is very fast and highly performant because it uses the Accelerated Linear Algebra (XLA) compiler, which optimizes computations for GPUs and TPUs.
It is also interoperable with many Python libraries.
Next, we will explore and learn about PyTorch in detail.
What is PyTorch?
PyTorch is 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 their deep learning models, such as Tesla.
PyTorch is made up of two main features – tensor computation with GPU support and deep neural networks. As a result, PyTorch is used extensively as a high-performance replacement for Numpy or as a deep-learning research platform.
Uses of PyTorch
PyTorch is primarily used for building models for deep learning. These models include Recurrent Neural Networks, Convolutional Neural Networks, and Transformers.
Next, we will discuss the detailed comparison between PyTorch and Jax.
PyTorch Vs. Jax
What they are
Jax essentially is a GPU/TPU accelerated version of Numpy plus powerful function transformations such as the JIT compiler and the gradient calculator. It, therefore, functions at a lower level than PyTorch.
Jax supports execution on GPUs and TPUs but is tightly integrated with the XLA compiler; therefore has been demonstrated to outperform PyTorch in a few benchmarks.
Jax is incredibly fast and outperforms PyTorch on most major benchmarks. This is because it runs on GPUs and TPUs and optimizes your code for XLA. Function transformations such as vmap and jit speed up your code.
While PyTorch supports GPUs, its support for TPUs and XLAs is not as extensive as that of Jax. As a result, it tends to be slower and less performant compared to Google Jax.
Ease of use
While it offers additional superpowers, most people find Jax marginally harder to use with a steeper learning curve.
PyTorch follows a Pythonic syntax that makes it easier to follow and pick up.
Jax is relatively new and therefore has a smaller ecosystem and is still largely experimental.
PyTorch being the older of the two, has a more mature and established ecosystem with multiple resources and a larger community.
Jax is intended primarily for research tasks.
PyTorch is suited for both research and production machine learning models.
Jax runs at a lower level compared to Python; therefore is not very abstract. However, it has libraries to simplify building neural networks, such as Flax, Haiku, and Equinox. There’s also PIX for image processing.
While PyTorch already seems fairly abstract compared to Jax, libraries such as PyTorch Lightning provide further abstractions saving you from having to write boilerplate code.
Applications and Best Use Cases for Jax
Given that Jax is still experimental and could be unstable as a result, it may not be ideal for building production systems.
However, for research work and large-scale projects that could benefit from the immense performance benefits provided by Jax, then Jax would be the ideal library.
Applications and Best Use Cases for PyTorch
Because of its maturity, PyTorch works well in production systems. Given its demonstrated use by companies such as Meta, you can be assured that PyTorch is scalable for even very large projects.
It also integrates well with systems for MLOps, such as Kubeflow and TorchServe, making it easier to build and deploy ML models quickly.
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.
However, PyTorch has a smoother learning curve, so you might want to start with that first before learning Jax. As for which one’s more useful in a given project, it is up to you to decide, given what you have learned about Jax and PyTorch and your needs for the project.
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