Docs/SK-924 | Update quickstart guide

docs/quickstart.rst

@@ -16,17 +16,13 @@ Getting started with FEDn
 
 Start by creating an account in Studio. Head over to `fedn.scaleoutsystems.com/signup <https://fedn.scaleoutsystems.com/signup/>`_  and sign up.
 
-Logged into Studio, do: 
+Logged into Studio, create a new project by clicking  on the "New Project" button in the top right corner of the screen.
+You will see a Studio project similar to the image below. The Studio project provides all the necessary server side components of FEDn. 
+We will use this project in a later stage to run the federated experiments. But first, we will set up the local client.
 
-1. Click on the "New Project" button in the top right corner of the screen.
-2. Continue by clicking the "Create button". The FEDn template contains all the services necessary to start a federation.
-3. Enter the project name (mandatory). The project description is optional.
-4. Click the "Create" button to create the project.
 
 .. image:: img/studio_project_overview.png
 
-When these steps are complete, you will see a Studio project similar to the above image. The Studio project provides all server side components of FEDn needed to manage 
-federated training. We will use this project in a later stage to run the federated experiments. But first, we will set up the local client.
 
 2. Install FEDn on your client
 -------------------------------
@@ -60,8 +56,15 @@ Next, we will prepare the client. For illustrative purposes, we use one of the p
 
 In order to train a federated model using FEDn, your Studio project needs to be initialized with a ``compute package`` and a ``seed model``. The compute package is a code bundle containing the 
 code used by the client to execute local training and local validation. The seed model is a first version of the global model. For a detailed explaination of the compute package and seed model, see this guide: :ref:`projects-label`
-
-Locate into ``examples/mnist-pytorch`` folder in the cloned fedn repository. The compute package is located in the folder ``client``.
+
+To work through this quick start you need a local copy of the ``mnist-pytorch`` example project contained in the main FEDn Git repository. 
+The following command clones the entire repository but you will only use the example:
+
+.. code-block:: bash
+
+   git clone https://github.com/scaleoutsystems/fedn.git
+
+Locate into ``examples/mnist-pytorch`` folder. The compute package is located in the folder ``client``.
 
 Create a compute package: