Add centralized GPU admonition for JAX lectures

lectures/_admonition/gpu.md

@@ -0,0 +1,5 @@
+```{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).
+```

lectures/jax_intro.md

@@ -33,16 +33,7 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 !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

lectures/numpy_vs_numba_vs_jax.md

@@ -48,16 +48,7 @@ tags: [hide-output]
 !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.