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Automatic differentiation can make a huge difference in a deep learning model's success, reducing the development time for iterating over models and experiments. Earlier, programmers had to design their own gradients which make the model vulnerable to bugs apart from consuming a lot of time which in course of time proved to be disastrous. To keep track of gradients over a neural network, libraries like TensorFlow and PyTorch are used. These libraries help develop normal functionalities, but to develop a model which lies out of its purview, these are not enough. Autograd is one library meant for automatic differentiation of native Python and NumPy code. JAX, the improvised version of Autograd, can combine hardware acceleration and automatic differentiation with XLA (accelerated linear algebra), a domain-specific compiler for linear algebra that can accelerate tensor flow models. In short, JAX is a Python library with NumPy functions used to cut out trivial machine learning operations.
Apart from supporting automatic differentiation, which primarily holds the forte for deep learning, JAX can enhance the speed tremendously, a sole functionality why JAX is dear to many developers.
As JAX operations are based on XLA, it is possible to compile at a faster rate than normal, ie., around 7.3 times faster with normal training, and an accelerated speed of 12 times in the long run. The JIT (Just In Time) compilation feature of JAX, further helps in enhancing its speed, by adding a simple function decorator. JAX helps developers immensely in reducing redundancy through vectorization. The machine learning process comprises several iterations, wherein a single function is used to model a large number of datasets. The automatic vectorization that JAX offers via vmap transformation, allows for data parallelism using pmap transformation. Usually, JAX is considered an alternative deep learning framework, however, its applications go beyond the library functionalities. Flax, haiku, and elegy are the libraries that are built on top of JAX for deep learning processes. Particularly, Hessians, perform higher order optimization, which JAX is good at computing, all because of XLA.
As JXA is highly compatible with GPUs, it has inherent compatibility with CPUs, unlike Numpy which is only compatible with CPUs. JAX has an API similar to Numpy and so it can auto-compile code directly on accelerators like GPUs and TPUs, making the process seamless. This means a code written in Numpy's syntax can be run on both CPUs and GPUs glitch-free. Despite having specialized constructs, JAX sits at a lower level with a lower level of control than deep learning, which makes it a perfect replacement for NumPy, and because of its bare metal structure, it can be used for all sorts of development besides deep learning. All in all, JAX can be considered an augmented version of Numpy to perform the aforementioned functions, with JAX's numpy version addressed as Jax.numPy, and JAX is almost numpy except that JAX can run code on accelerators.
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