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+# Introduction to JAX with Distributed Computations
+
+JAX is a high-performance numerical computing library that brings together the power of automatic differentiation and GPU/TPU acceleration. It is particularly well-suited for machine learning research and other computationally intensive tasks. One of the standout features of JAX is its ability to seamlessly scale computations across multiple devices and hosts, enabling efficient distributed computing.
+
+## Key Features of JAX for Distributed Computations
+
+- **Automatic Differentiation**: JAX provides powerful automatic differentiation capabilities, making it easy to compute gradients for optimization tasks.
+- **GPU/TPU Acceleration**: JAX can leverage GPUs and TPUs to accelerate computations, providing significant performance improvements over CPU-only execution.
+- **Distributed Computing**: JAX supports distributed computations across multiple devices (e.g., multiple GPUs) and multiple hosts (e.g., multiple machines in a cluster), allowing for scalable and efficient parallel processing.
+
+## Distributed Computations on Multiple Devices
+
+JAX simplifies the process of distributing computations across multiple devices. By using the `jax.pmap` function, you can parallelize operations across multiple devices, such as GPUs, within a single host. This enables you to take full advantage of the available hardware resources.
+
+## Distributed Computations on Multiple Hosts
+
+For even larger-scale computations, JAX supports distributed computing across multiple hosts. This involves coordinating computations across different machines in a cluster, allowing for massive parallelism and efficient use of distributed resources. JAX provides tools and abstractions to manage communication and synchronization between hosts, ensuring that distributed computations are both efficient and scalable.
+
+In summary, JAX's support for distributed computations on multiple devices and hosts makes it a powerful tool for tackling large-scale numerical and machine learning tasks. Whether you are working on a single machine with multiple GPUs or a large cluster of machines, JAX provides the flexibility and performance needed to efficiently scale your computations.
+
+## Distributed data (`sharded_device_array`)
+
+JAX provides a data structure called `sharded_device_array` that allows you to distribute large arrays across multiple devices in a memory-efficient manner and relatively transparently for your code. This data structure is particularly useful for handling large datasets that do not fit in the memory of a single device.

_sources/chapters/jax/introduction.md

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-# Jax
+# Jax
+
+# Introduction to JAX
+
+JAX is a numerical computing library that brings together the power of automatic differentiation and GPU acceleration. It is designed to enable high-performance machine learning research and other scientific computing tasks. JAX's ability to transform numerical functions and execute them on various hardware accelerators makes it a valuable tool for researchers.
+
+## Importance in Astronomy Research
+
+In the field of astronomy, JAX is particularly important because it allows researchers to efficiently process and analyze large datasets, perform complex simulations, and develop sophisticated models. The ability to leverage GPU acceleration means that computations can be performed much faster, enabling more detailed and timely insights into astronomical phenomena.
+
+For more information, visit the [JAX website](https://github.com/google/jax).