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JAX vs PyTorch: Comparing Two Powerhouses in ML Frameworks
Discover the key differences between JAX and PyTorch, two leading ML frameworks. Learn their features, performance, and which one suits your project best.
Deep learning has become an increasingly popular aspect of machine learning, especially in its applications in computer vision, natural language processing, and gaming. With the complexity that comes with building from scratch, the adoption of machine learning frameworks has been welcomed wholeheartedly in the machine learning community as it offers significant help in building and training neural networks.
Choosing the right framework for your project is also an important step in building models. JAX and PyTorch are two popular machine learning frameworks used in deep learning research and production, but which framework is the best for your project?
In this blog post, we will discuss JAX vs PyTorch, explore their differences, and help you choose the right ML framework for your project.
What is JAX?
JAX, developed by Google, is an open-source machine learning framework built on functional programming principles. JAX stands for ‘Just Another XLA” where XLA stands for Accelerated Linear Algebra. It is renowned for its numerical computation and automatic differentiation capabilities, which help in the development of many machine learning algorithms. Although JAX is a relatively new machine learning framework, it has some helpful features for developing machine learning models.
Features of JAX
It can automatically differentiate functions using the reverse-mode differentiation technique. This helps to seamlessly calculate gradients and provide accurate values in training models.
It runs on CPU, GPU, and TPU, which provides faster numerical computations.
It is compatible with NumPy and other Python libraries, making it easier to integrate existing Python codebases.
It uses built-in Just-In-Time (JIT) compilation which results in faster training time for machine learning models that involve complex computations.
It is highly flexible; developers can create custom neural network architectures using JAX.
It offers automatic parallelization and vectorization across multiple devices.
Installation
JAX is compatible with Windows, Linux and MacOS. You can install JAX in your CPU by running the command below:
What is PyTorch?
Developed by the Facebook AI Research (FAIR) lab, PyTorch is an open-source machine learning framework used to build efficient machine learning models. In contrast to JAX, PyTorch is based on an imperative programming paradigm. It is a popular library and is used by many companies to build their machine learning models.
PyTorch is known for its flexibility and ease of use in implementing machine learning algorithms. A key feature of PyTorch is its Dynamic Computational Graph which allows for more flexibility in writing code. It is based on the Torch library.
Features of PyTorch
It has a dynamic computational graph which allows you to build on-the-fly and see how your code runs. This is crucial as it helps in debugging and allows you to seamlessly change the structure of your graph at any point.
Compared to JAX, PyTorch is easier to use as it uses a Python-like syntax.
It supports automatic differentiation by using the Autograd library which helps to calculate gradients without explicitly writing the code from scratch.
It provides the Torch.nn module which helps construct and create a custom neural network.
It can be easily integrated with other Python libraries such as NumPy, Pandas, and SciPy.
It provides the TorchVision library to ease image processing tasks and the TorchText library for natural language processing tasks.
Installation
You can install PyTorch in your project by running the command below:
PyTorch vs JAX: Exploring The Differences
Having learnt about JAX and PyTorch, let’s dive into the differences between the two using metrics such as their programming model, performance, ecosystem, ease of use, and the libraries they provide.
Programming Model
As discussed earlier, JAX follows a functional programming paradigm, which focuses on principles such as transformations, immutability, and pure functions. It also uses automatic differentiation which helps to differentiate functions written in Python and NumPy. This feature helps developers compute gradients efficiently. JAX’s functional programming approach allows the use of reusable functions which makes the development of complex models easier.
PyTorch follows an imperative programming paradigm, which uses an object-oriented approach similar to the syntax of Python. It uses a library (Autograd) for automatic differentiation. PyTorch’s programming model uses a dynamic computational graph, which builds the graph on-the-fly as the code runs.
JAX vs PyTorch: Performance and Speed
An important consideration in choosing a machine learning framework is performance and speed, especially when dealing with large-scale applications. Is JAX faster than PyTorch? Let’s see. JAX uses hardware accelerators such as GPU and TPU, which makes it highly performant and fast in execution. It also optimizes code for Accelerated Linear Algebra (XLA). JAX’s Just-In-Time (JIT) compilation approach offers a significant speed-up to its execution, although it requires additional tweaks to the code structure.
PyTorch is also optimized for GPUs but does not provide extensive support for TPUs due to hardware disparities. This makes JAX faster and more performant than PyTorch.
Ease of Use
The learning curve and ease of use of a particular language or framework are good factors to consider. It may be perceived as minimal but a framework that is easy to use saves development time and leads to faster training times for machine learning models.
Since it uses Python syntax, PyTorch is relatively easier to use than JAX.
Ecosystem and Community
Ecosystem and community support are important in choosing between two frameworks. A framework with a vast ecosystem will have more resources available for learning. An active community also helps in debugging as you can easily find resources that solve your bug or engage in pair programming with other developers in the community.
PyTorch has a more mature ecosystem as it’s the older framework. As it’s relatively new and mostly used in research environments, JAX has a smaller ecosystem.
Extensions and Libraries
The ability to extend functionalities through integrated tools and libraries helps to complete complex tasks without writing the code from scratch. Being the older framework, PyTorch provides a variety of libraries including TorchText for natural language processing, TorchAudio for audio processing, TorchVision for image processing, and torch.nn for training neural networks.
JAX does not provide as many libraries as PyTorch, although it has third-party libraries such as Flax and Haiku which are used for building neural networks. It is also compatible with the Optax library which is used for gradient optimization.
PyTorch or JAX: Enhancing Development in Machine Learning with Pieces
JAX and PyTorch are both great frameworks used in machine learning. The best framework to use ultimately depends on the requirements and scale of your project. Here’s a tip: If you’re in search of a faster framework with functional programming principles, then go for JAX. If you want a framework that has a variety of libraries and is relatively easier to use, PyTorch is your best choice.
Whether you choose JAX or PyTorch, Pieces compensates for the shortcomings the framework may come with. For example, JAX has a steep learning curve, you can get a better understanding when you use Pieces. PyTorch provides libraries for different tasks such as image processing and audio processing, and Pieces provides a snippet repository where you can save the snippets used for each task.
Pieces is an on-device AI coding assistant that boosts developers’ productivity by offering a contextual understanding of their codebase. The Pieces Copilot helps to speed up the development of machine learning models and in your overall day-to-day coding activities. Let’s see it in action:
Here, I have a codebase on image enhancement using deep learning techniques. I asked the Pieces Copilot “How do I train the model for deraining?” Deraining involves the removal of visual effects of rain from an image. Pieces Copilot provides a contextual answer as it relates to my codebase, including the preparation of the dataset, defining the training loop, and setting up the loss function. Note that I have the Pieces for VS Code Extension installed in this demo— it’s also available for your favorite IDEs.
Conclusion
In this blog post, you have compared JAX vs PyTorch, explored the backgrounds of JAX and PyTorch, their features, and their differences using metrics such as the programming models, ecosystem, ease of use, performance, and the libraries they are compatible with. Finally, you saw how to enhance your development in machine learning with Pieces. Happy coding!
