MAINT: centralize admonitions for GPU

lectures/_admonition/gpu.md

@@ -0,0 +1,9 @@
+```{admonition} GPU
+:class: warning
+
+This lecture is accelerated via [hardware](status:machine-details) that has access to a GPU and 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]`
+```

lectures/aiyagari_jax.md

@@ -13,14 +13,7 @@ kernelspec:
 
 # The Aiyagari Model
 
-```{admonition} GPU
-:class: warning
-
-This lecture is accelerated via [hardware](status:machine-details) that has access to a GPU and 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
 ```
 
 ## Overview

lectures/arellano.md

@@ -13,14 +13,7 @@ kernelspec:
 
 # Default Risk and Income Fluctuations
 
-```{admonition} GPU
-:class: warning
-
-This lecture is accelerated via [hardware](status:machine-details) that has access to a GPU and 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
 ```
 
 In addition to what's in Anaconda, this lecture will need the following libraries:

lectures/inventory_dynamics.md

@@ -23,14 +23,7 @@ kernelspec:
 
 # Inventory Dynamics
 
-```{admonition} GPU
-:class: warning
-
-This lecture is accelerated via [hardware](status:machine-details) that has access to a GPU and 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
 ```
 
 ```{index} single: Markov process, inventory

lectures/jax_intro.md

@@ -14,14 +14,7 @@ kernelspec:
 # An Introduction to JAX
 
 
-```{admonition} GPU
-:class: warning
-
-This lecture is accelerated via [hardware](status:machine-details) that has access to a GPU and 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
 ```
 
 This lecture provides a short introduction to [Google JAX](https://github.com/google/jax).

lectures/kesten_processes.md

@@ -29,14 +29,7 @@ kernelspec:
 :depth: 2
 ```
 
-```{admonition} GPU
-:class: warning
-
-This lecture is accelerated via [hardware](status:machine-details) that has access to a GPU and 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
 ```
 
 In addition to what's in Anaconda, this lecture will need the following libraries:

lectures/newtons_method.md

@@ -14,14 +14,7 @@ kernelspec:
 
 # Newton’s Method via JAX
 
-```{admonition} GPU
-:class: warning
-
-This lecture is accelerated via [hardware](status:machine-details) that has access to a GPU and 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
 ```
 
 ## Overview

lectures/opt_invest.md

@@ -14,14 +14,7 @@ kernelspec:
 
 # Optimal Investment
 
-```{admonition} GPU
-:class: warning
-
-This lecture is accelerated via [hardware](status:machine-details) that has access to a GPU and 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 require the following library to be installed.

lectures/opt_savings.md

@@ -13,14 +13,7 @@ kernelspec:
 
 # Optimal Savings
 
-```{admonition} GPU
-:class: warning
-
-This lecture is accelerated via [hardware](status:machine-details) that has access to a GPU and 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
 ```
 
 In addition to what’s in Anaconda, this lecture will need the following libraries:

lectures/short_path.md

@@ -15,14 +15,7 @@ kernelspec:
 
 # Shortest Paths
 
-```{admonition} GPU
-:class: warning
-
-This lecture is accelerated via [hardware](status:machine-details) that has access to a GPU and 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
 ```
 
 ## Overview

lectures/wealth_dynamics.md

@@ -14,14 +14,7 @@ kernelspec:
 
 # Wealth Distribution Dynamics
 
-```{admonition} GPU
-:class: warning
-
-This lecture is accelerated via [hardware](status:machine-details) that has access to a GPU and 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
 ```
 
 This lecture is the extended JAX implementation of [this lecture](https://python.quantecon.org/wealth_dynamics.html).