uses: actions/upload-artifact@v4

if: failure()

with:

name: execution-reports

name: execution-reports-notebooks

path: _build/jupyter/reports

- name: Build PDF from LaTeX

shell: bash -l {0}

# false-positive links

linkcheck_ignore: ['https://online.stat.psu.edu/stat415/book/export/html/834']

bibtex_reference_style: author_year

suppress_warnings: ["mystnb.unknown_mime_type"]

nb_mime_priority_overrides: [

# HTML

['html', 'application/vnd.jupyter.widget-view+json', 10],

```{include} _admonition/gpu.md

```

```{code-cell} ipython3

:tags: [skip-execution]

!pip install --upgrade jax

```

```{code-cell} ipython3

import jax

## to check that gpu is activated in environment

print(f"JAX backend: {jax.devices()[0].platform}")

```

In addition to what's included in base Anaconda, we need to install the following packages

```{code-cell} ipython3

:tags: [hide-output]

!pip install kaleido

!conda install -y -c plotly plotly plotly-orca retrying

!pip install -U kaleido plotly

!conda install -y -c plotly plotly-orca

# kaleido needs chrome to build images

import kaleido

kaleido.get_chrome_sync()

```

```{note}

has `plotly` installed.

```

We also need to install JAX to run this lecture

```{code-cell} ipython3

:tags: [skip-execution]

!pip install --upgrade jax

```

```{code-cell} ipython3

import jax

print(f"JAX backend: {jax.devices()[0].platform}") # to check that gpu is activated in environment

```

## Overview

Substantial parts of **machine learning** and **artificial intelligence** are about

!conda list

```

This lecture series has access to the following GPU

```{code-cell} ipython

!nvidia-smi

```

You can check the backend used by JAX using:

```{code-cell} ipython3

import jax

# Check if JAX is using GPU

print(f"JAX backend: {jax.devices()[0].platform}")

```

and this lecture series also has access to the following GPU

```{code-cell} ipython

!nvidia-smi

```