Update config, logo and instructions for gui

_config.yml

 title: "2023 AI4Health summer school - Practical Session on Fed-BioMed, an open source framework for federated learning in real world healthcare applications"
 author: "Lucia Innocenti and Francesco Cremonesi"
-logo: "assets/img/logo.jpg"
+logo: "assets/img/logo.png"
 only_build_toc_files: true
 exclude_patterns: [_build, Thumbs.db, .DS_Store, "**.ipynb_checkpoints", "README.md"]
 

_toc.yml

@@ -5,3 +5,4 @@ parts:
 - caption: Fed-BioMed tutorial
   chapters:
   - file: fedbiomed-tutorial/aws-instructions.md 
+  - file: fedbiomed-tutorial/running-the-gui.md

fedbiomed-tutorial/running-the-gui.md

+# Running the Fed-BioMed GUI on JupyterHub
+
+Prerequisites:
+- node configuration must be created
+- your JupyterHub IP address
+- a network port from the table below
+
+## Network ports
+
+| user | port |
+| ---  | ---  |
+| francesco | 8484 |
+
+## Running the GUI
+
+The GUI's execution will be associated with a specific node. For this example, we will refer to it as `node-name`. 
+Open a new terminal and navigate to the `fedbiomed` directory, then run the following command
+```bash
+cd $HOME/fedbiomed
+./scripts/fedbiomed_run gui data-folder /datasets config node-name --port <your port from the table above> --host 0.0.0.0 start
+```
+
+## Accessing the GUI
+Copy the IP address that you used to access JupyterHub, and append `:<your port number>` at the end and paste it in your browser. 
+For example, if your address was `1.2.3.4` and your port is `8456`, you would insert `http://1.2.3.4:8456` in your browser search bar.
+

index.md

-# Handling heterogeneity in the analysis of biomedical information
-## 2021 AI4Health practical session 
+# Fed-BioMed, an open source framework for federated learning in real world healthcare applications
+## 2023 AI4Health practical session 
  
-	
-This session focuses on the problem of statistical analysis of heterogeneous data in biomedical studies. Through guided examples, we will first introduce the basics of latent variable modelling for the joint analysis of heterogeneous data types (such as imaging, clinical or biological measurements). We will initially focus on linear approaches, such as partial least squares and canonical correlation analysis. We will then present more flexible methods based on recent advances in deep learning and stochastic variational inference, such as the multi-channel variational autoencoder. We will finally address the problem of deploying latent variable models for federated learning in multi-centric studies, where models must account for data-privacy and heterogeneity across datasets.
+This practical session focuses on federated learning (FL) for healthcare applications, and is based on Fed-BioMed, an open source framework for deploying FL in real world use-cases. Throughout the session the participants will get introduced to the basics of federated learning, and will learn to deploy a federated training in a network of clients by using the Fed-BioMed software components. We will focus on the federation of general machine learning approaches for the analysis of medical data (such as tabular or medical images), using a variety of AI frameworks, from Pytorch to scikit-learn. Most advanced topics include the use of privacy-preserving techniques in FL, and the definition of custom data types, models and optimisation routines.	
 
 ## Material usage
 
-Herein you will find the material that will be developed during the practical session. Some of the material corresponds to text and images that you can download in the upper right corner <i class="fas fa-download"></i> 
-
-
-
-## Launch my notebooks
-
-You can have an environment for yourself by clicking here: [![Binder](https://mybinder.org/badge_logo.svg)](http://bit.ly/3iahdfl)
+We provide a ready-to-use JupyterHub server. Follow the [instructions](/fedbiomed-tutorial/aws-instructions) to find out how to connect. 

requirements-site.txt

 sphinxcontrib-bibtex==2.5.0
+myst-parser>=0.17.0,<1.0.0
 jupyter-book==0.15.1