Beginner's First Look into Unlocking High Performance ML Kernels

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This article marks the beginning of a multi-part series designed to introduce and explore the JAX ecosystem of libraries. Through this series, we will cover essential libraries such as Flax, Chex, CLU, Optax, and more, providing practical examples and insightful discussions.

What is JAX?

JAX combines Autograd and XLA, providing a high-performance and scalable environment for scientific computing. It allows you to run NumPy on CPU, GPU, and TPU with optimizations readily available out of the box.

Key Features:

1. Autograd: JAX extends Autograd to support automatic differentiation of Python functions, offering capabilities for computing higher-order derivatives efficiently.
2. XLA (Accelerated Linear Algebra): An open-source compiler that helps in accelerating Python and NumPy functions across multiple platforms, utilizing CPU, GPU, and TPU.

Why and When Should You Use JAX?

• Scalability and Performance: Ensures high-speed performance and low memory costs, beneficial for handling large datasets.
• Flexibility: Provides a composable framework that is stateless, allowing extensive customizations without the overhead of managing state on hardware accelerators.
• No Compromise on Complexity: JAX enables high-level computing without requiring users to delve into the complexities of low-level libraries.
• Unified Optimization: Write once and deploy everywhere, optimizing across various hardware platforms.

Hello JAX! Your First JAX Code

Let's dive into some code snippets to illustrate the basics of JAX:

1import jax.numpy as jnp
2from jax import grad, vmap, jit
3
4def predict(params, inputs):
5    for W, b in params:
6        outputs = jnp.dot(inputs, W) + b
7        outputs = jnp.tanh(outputs)
8    return outputs
9
10def loss(params, batch):
11    inputs, targets = batch
12    preds = predict(params, inputs)
13    return jnp.sum((preds - targets) ** 2)
14
15gradient_func = jit(grad(loss))
16perexample_grads = jit(vmap(grad(loss), in_axes=(None, 0)))
17

Key JAX Functions:

1. jax.numpy: A drop-in replacement for NumPy optimized for speed and designed for JAX.
2. jax.grad: Automates the differentiation process.
3. jax.jit: Compiles Python functions to optimize execution speed.
4. jax.vmap: Automates vectorization, allowing operations to be vectorized for efficiency.
5. jax.random: Provides a pseudo-random number generation approach suitable for parallel and reproducible computations.

Stay tuned for more in-depth discussions and examples in the upcoming parts of this series. To read entire article, go to Kaggle!