![]() ![]() 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.
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