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JAX is a library for array-oriented numerical computation (à la NumPy), with automatic differentiation and JIT compilation to enable high-performance machine learning research. This document provides a quick overview of essential JAX features, so you can get started with JAX quickly:
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JAX is a Python library for accelerator-oriented array...
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- What Is Xla?
- Installing Jax
- Setting Up TPUs on Google Colab
- Data Types in Jax
- Ways to Create Jax Arrays
- Generating Random Numbers with Jax
- Pure Functions
- Jax Numpy Operations
- Jax Arrays Are Immutable
- Out-Of-Bounds Indexing
XLA (Accelerated Linear Algebra) is a linear algebra compiler for accelerating machine learning models. It leads to an increase in the speed of model execution and reduced memory usage. XLA programs can be generated by JAX, PyTorch, Julia, and NX.
JAX can be installed from the Python Package Index using: JAX is pre-installed on Google Colab. See the link below for other installation options.
You need to set up JAX to use TPUson Colab. That is done by executing the following code. Ensure that you have changed the runtime to TPU by going to Runtime-> Change Runtime Type. If no accelerator is available, JAX will use the CPU.
The data types in NumPy are similar to those in JAX arrays. For instance, here is how you can create float and intdata in JAX. When you check the type of the data, you will see that it's a DeviceArray. DeviceArray in JAX is the equivalent of numpy.ndarry in NumPy. jax.numpy provides an interface similar to NumPy's. However, JAX also provides jax.la...
You can create JAX arrays like you would in NumPy. For example, can use: 1. arange 2. linspace 3. Python lists. 4. ones. 5. zeros. 6. identity.
Random number generation is one main difference between JAX and NumPy. JAX is meant to be used with functional programs. JAX expects these functions to be pure. A pure function has no side effects and expects the output to only come from its inputs. JAX transformation functions expect pure functions. Therefore, when working with JAX, all input shou...
We have mentioned that the output of a pure function should only come from the result of the function. Therefore, something like Python's printfunction introduces impurity. This can be demonstrated using this function. We can see the printed statement the first time the function is executed. However, we don't see that print statement in consecutive...
Operations on JAX arrays are similar to operations with NumPy arrays. For example, you can max, argmax, and sum like in NumPy. However, JAX doesn't allow operations on non-array input like NumPy. For example, passing Python listsor tuples will lead to an error.
Unlike in NumPy, JAX arrays can not be modified in place. This is because JAX expects pure functions. Array updates in JAX are performed using x.at[idx].set(y). This returns a new array while the old array stays unaltered.
NumPyusually throws an error when you try to get an item in an array that is out of bounds. JAX doesn't throw any error but returns the last item in the array. JAX is designed like this because throwing errors in accelerators can be challenging.
Mar 4, 2021 · Jax is a Python library designed for high-performance ML research. Jax is nothing more than a numerical computing library, just like Numpy, but with some key improvements. It was developed by Google and used internally both by Google and Deepmind teams.
Google JAX is a machine learning framework for transforming numerical functions. [1][2][3] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and TensorFlow 's XLA (Accelerated Linear Algebra).
JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning.
JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions.
May 30, 2022 · JAX is a Python library, made by Google, for optimized scientific computing: It can be seen as an alternative to NumPy, providing a very similar interface, which runs on GPUs and TPUs as...
