Merge pull request #39 from Cambridge-ICCS/binder

Binder gh pages and readme.
Cambridge-ICCS · Nov 19, 2023 · 82e0c86 · 82e0c86
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diff --git a/README.md b/README.md
@@ -6,6 +6,7 @@
 
 ![GitHub](https://img.shields.io/github/license/Cambridge-ICCS/ml-training-material)
 [![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Cambridge-ICCS/ml-training-material/main)
 
 This repository contains documentation, resources, and code for the Introduction to
 Machine Learning with PyTorch session designed and delivered by [Jack Atkinson](https://jackatkinson.net/) ([**@jatkinson1000**](https://github.com/jatkinson1000))
@@ -70,13 +71,6 @@ These are for recapping after the course in case you missed anything, and contai
 [linted](https://docs.pylint.org/intro.html), and conforming to the
 [black](https://black.readthedocs.io/en/stable/) code style.
 
-If you were working on Colab you can open the worked solutions using the following links:
-
-* [Exercise 01](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/01_penguin_classification_solutions.ipynb)
-* [Exercise 02](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/02_penguin_regression_solutions.ipynb)
-* [Exercise 03](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/03_mnist_classification_solutions.ipynb)
-* [Exercise 04](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/04_ellipse_regression_solutions.ipynb)
-
 
 ## Preparation and prerequisites
 
@@ -136,17 +130,18 @@ us before a training session.
 
 ## Installation and setup
 
-There are two options for participating in this workshop for which instructions are provided below:
+There are three options for participating in this workshop for which instructions are provided below:
 
 * via a [local install](#local-install)
 * on [Google Colab](#google-colab)
+* on [binder](#binder)
 
 We recommend the [local install](#local-install) approach, especially if you forked
 the repository, as it is the easiest way to keep a copy of your work and push back to GitHub.
 
 However, if you experience issues with the installation process or are unfamiliar with
 the terminal/installation process there is the option to run the notebooks in
-[Google Colab](#google-colab).
+[Google Colab](#google-colab) or on [binder](#binder).
 
 ### Local Install
 
@@ -219,18 +214,31 @@ python -m ipykernel install --user --name=MLvenv
 
 ### Google Colab
 
-To run the notebooks in Google Colab click the following links for each of the exercises:
+Running on Colab is useful as it allows you to access GPU resources.  
+To launch the notebooks in Google Colab click the following links for each of the exercises:
 
-* [Exercise 01](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/01_penguin_classification.ipynb)
-* [Exercise 02](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/02_penguin_regression.ipynb)
-* [Exercise 03](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/03_mnist_classification.ipynb)
-* [Exercise 04](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/04_ellipse_regression.ipynb)
+* [Exercise 01](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/01_penguin_classification.ipynb) - [Worked Solution 01](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/01_penguin_classification_solutions.ipynb)
+* [Exercise 02](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/02_penguin_regression.ipynb) - [Worked Solution 02](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/02_penguin_regression_solutions.ipynb)
+* [Exercise 03](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/03_mnist_classification.ipynb) - [Worked Solution 03](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/03_mnist_classification_solutions.ipynb)
+* [Exercise 04](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/04_ellipse_regression.ipynb) - [Worked Solution 04](https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/worked-solutions/04_ellipse_regression_solutions.ipynb)
 
 _Notes:_
 * _Running in Google Colab requires you to have a Google account._
 * _If you leave a Colab session your work will be lost, so be careful to save any work
   you want to keep._
 
+### binder
+
+If you cannot operate using a local install, and do not wish to sign up for a Google account,
+the repository can be launched
+[on binder](https://mybinder.org/v2/gh/Cambridge-ICCS/ml-training-material/main).
+
+_Notes:_
+* _If you leave a binder session your work will be lost, so be careful to save any work
+  you want to keep_
+* _Due to the limited resources provided by binder you will struggle to run training in
+  exercises 3 and 4._
+
 
 ## License
 

diff --git a/slides/index.html b/slides/index.html
@@ -86,6 +86,7 @@ <h2 id="contents">Contents</h2>
               <ul>
                 <li><a href="#github">GitHub</a></li>
                 <li><a href="#colab">Colab</a></li>
+                <li><a href="#binder">binder</a></li>
                 <li><a href="#solutions">Solutions</a></li>
               </ul>
               <li><a href="#prerequisites">Prerequisites</a></li>
@@ -135,6 +136,7 @@ <h2 id="setup">Setup Instructions</h2>
           <ul>
             <li><a href="#github">a local install via Github</a></li>
             <li><a href="#colab">online via Google Colab</a></li>
+            <li><a href="#binder">online via binder</a></li>
           </ul>
 
           <p>We recommend the local install approach, especially if you forked the repository, as it is the easiest way to keep a copy of your work and push back to github.</p>
@@ -183,7 +185,8 @@ <h6>Optional) Keep virtual environment persistent in Jupyter Notebooks</h6>
           <code>python -m ipykernel install --user --name=MLvenv</code></p>
 
           <h4 id="colab">Google Colab</h4>
-          <p>To run the notebooks in Google Colab click the following links for each of the exercises:</p>
+          <p>Running on Colab is useful as it allows you to access GPU resources.<br>
+             To launch the notebooks in Google Colab click the following links for each of the exercises:</p>
           <ul>
             <li><a href="https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/01_penguin_classification.ipynb">Exercise 01</a></li>
             <li><a href="https://colab.research.google.com/github/Cambridge-ICCS/ml-training-material/blob/colab/exercises/02_penguin_regression.ipynb">Exercise 02</a></li>
@@ -196,6 +199,17 @@ <h4 id="colab">Google Colab</h4>
             <indent>If you leave a Colab session your work will be lost, so be careful to save any work you want to keep.</indent>
           </i></p>
 
+          <h4 id="binder">binder</h4>
+          <p>To run the notebooks in binder click the following link:</p>
+          <ul>
+            <li><a href="https://mybinder.org/v2/gh/Cambridge-ICCS/ml-training-material/main">Launch repository in binder</a></li>
+          </ul>
+
+          <p><i>Notes:<br>
+            <indent>If you leave a binder session your work will be lost, so be careful to save any work you want to keep.</indent>
+            <indent>Due to the limited resources provided by binder you will struggle to run training in exercises 3 and 4.</indent>
+          </i></p>
+
           <h4 id="solutions">Solutions</h4>
 
           <p>Worked solutions for all of the exercises can be found in the <inlinecode>worked-solutions/</inlinecode> directory.
@@ -246,7 +260,7 @@ <h4>Python</h4>
           </ul>
 
           <h4>git and GitHub</h4>
-          <p>Unless participating via <a href="#colab">Colab</a> you will be expected to know how to:</p>
+          <p>Unless participating via <a href="#colab">Colab</a> or <a href="#binder">binder</a> you will be expected to know how to:</p>
           <ul>
           <li>clone and/or fork a repository,</li>
           <li>commit, and</li>