PyImageSearch

FEBRUARY 27, 2023

Table of Contents Learning JAX in 2023: Part 2 — JAX’s Power Tools grad , jit , vmap , and pmap ?? Summary Citation Information Learning JAX in 2023: Part 2 — JAX’s Power Tools grad , jit , vmap , and pmap In this tutorial, you will learn the power tools of JAX, grad , jit , vmap , and pmap.

Machine Learning 107

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PyImageSearch

FEBRUARY 20, 2023

If you head over to the official documentation of JAX API , you will see several sub-packages and sub-topics with their APIs listed. import numpy as np import jax from jax import numpy as jnp from jax import make_jaxpr from jax import grad, jit, vmap, pmap ? This makes JAX code not only device agnostic but also jit compilable.

Machine Learning 114

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PyImageSearch

APRIL 24, 2023

code run faster (with less memory) by JIT-compiling the PyTorch 2.0 TorchDynamo TorchDynamo (shown in Figure 1 ) is PyTorch’s latest compiler solution that leverages JIT (Just In Time) compilation to transform a general Python program into an FX Graph. _dynamo as dynamo import torch.jit as jit import torch.fx

Deep Learning 103

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Flipboard

APRIL 17, 2023

jit def update_step(theta, x, y, lr): """ A single update step for our model. jit def update_step(theta, x, y, lr): """ A single update step for our model. My mission is to change education and how complex Artificial Intelligence topics are taught. We use the simple stochastic gradient update as our optimizer.

Machine Learning 107

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PyImageSearch

JULY 24, 2023

It allows you to run your experiments in jet speed (jit compilation), but writing a custom train step and using another library (Flax) for modeling is like embracing an unorganized airport with no signs (poor design and documentation of Flax) before boarding your jet. JAX is good because it is fast. And that’s exactly what I do.

Deep Learning 98

Deep Learning Deep Learning Machine Learning Machine Learning 98