And unless you work for Google, you will probably never see a TPU anywhere outside Google Colab.Īpart from the numpy-like API, JAX includes the following main operations: However, unlike TF, JAX has no official docker images yet. GPU installation requires precise versions of CUDA and CUDNN, just like for TensorFlow. Then your code can run on CPU, GPU, or TPU with no changes. You write code like in numpy, but use the prefix jnp. You can view JAX as “numpy with backprop, XLA JIT, and GPU+TPU support”. It is optional in TensorFlow, but required by JAX. It compiles stuff into an efficient machine code. Google XLA (accelerated linear algebra): fast matrix operations for CPU, Nvidia GPU and TPU.autograd: Numpy-like library with gradients (backprop).JAX (the low-level API) has two predecessors: JAX: Basics, Pytrees, Random Numbers & Neural Networks JAX Basics and Functional Programming We assume that the reader has basic DL and python knowledge and some experience with either TF or PyTorch. We also recommend AI Epiphany lectures on JAX and Flax. JAX is open-source, it has pretty good documentation and tutorials. Thus you will have to use either TF or PyTorch for these tasks or implement everything yourself. Note that currently, JAX has no dataset/dataloader API, nor standard datasets like MNIST. Numerous more specialized packages: Trax, Objax, Stax, Elegy, RLax, Coax, Chex, Jraph, Oryx ….OPTAX: Optimizers and loss function for JAX.Haiku: Another layer API, from DeepMind, inspired by Sonnet (TF).FLAX (FLexible JAX): Layer API from Google (excluding DeepMind).JAX: Low-level API (like torch without torch.nn or TF without tf.keras).JAX ecosystem consists of the following packages (which are separate PIP packages): Probably nobody outside of DeepMind has ever heard of it. Even in the TensorFlow era, DeepMind used their own layer API called Sonnet (instead of the usual Keras). To understand JAX, note that Google has not one, but at least two (perhaps more) competing AI teams: Google Brain and DeepMind. But first, why was JAX created? We don’t know exactly, but apparently, AI folk in Google got fed up with TensorFlow and wanted a new toy to fool around with. We will address questions like “When to use JAX?” and “Does JAX have any chance of success?”. In this article, we examine JAX and look at its positive and negative sides. One of the more serious contenders in the ”DL framework Junior League” is Google JAX. We have no idea which DL framework will be popular in, say, ten years. All the time, new DL frameworks are proposed. However, there is no reason to believe such a duopoly will persist forever. Nowadays, they appear to be dead or dying, as just two frameworks heavily dominate the DL scene: Google TensorFlow (TF), which includes Keras and PyTorch from Meta aka FaceBook. Historically, there have been many Deep Learning (DL) frameworks, like Theano, CNTK, Caffe2, and MXNet.
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