```{admonition} GPU

:class: warning

This lecture is designed to run on a GPU. To use Google Colab's free GPUs, click the play icon top right, select Colab, and set the runtime to include a GPU. For local GPU setup, see the [JAX installation guide](https://github.com/google/jax).

```

!pip install jax quantecon

```

```{admonition} GPU

:class: warning

This lecture is accelerated via [hardware](status:machine-details) that has access to a GPU and targets JAX for GPU programming.

Free GPUs are available on Google Colab.

To use this option, please click on the play icon top right, select Colab, and set the runtime environment to include a GPU.

Alternatively, if you have your own GPU, you can follow the [instructions](https://github.com/google/jax) for installing JAX with GPU support.

If you would like to install JAX running on the `cpu` only you can use `pip install jax[cpu]`

```{include} _admonition/gpu.md

```

## JAX as a NumPy Replacement

!pip install quantecon jax

```

```{admonition} GPU

:class: warning

This lecture is accelerated via [hardware](status:machine-details) that has access to a GPU and target JAX for GPU programming.

Free GPUs are available on Google Colab.

To use this option, please click on the play icon top right, select Colab, and set the runtime environment to include a GPU.

Alternatively, if you have your own GPU, you can follow the [instructions](https://github.com/google/jax) for installing JAX with GPU support.

If you would like to install JAX running on the `cpu` only you can use `pip install jax[cpu]`

```{include} _admonition/gpu.md

```

We will use the following imports.