Update Model docstring + Readme

README.md

@@ -1,25 +1,52 @@
 
 # Actions SDK
 
-The Actions SDK is a Python library designed to facilitate the development of applications on the Giza platform. It provides a set of decorators and classes to define tasks, actions, and models, and to handle data inputs.
+The Actions SDK is a Python library designed to facilitate the development of ZKML applications on the Giza platform. It provides a set of decorators and classes to define tasks, actions, and models, and to handle data inputs.
+
+## Where to start?
+Check out our extensive [documentation](https://actions.gizatech.xyz/welcome/giza-actions-sdk) to understand concepts and follow how-to-guides.
 
 ## Installation
 
-The Actions SDK can be installed using [Poetry](https://python-poetry.org/):
+Start by creating a virtual environment in your project directory and activate it:
+```bash
+$ python -m venv .env
+
+# Activate the virtual environment. On Linux and MacOs
+$ source .env/bin/activate
 
+# Activate Virtual environment on Windows:
+$ .env/Scripts/activate
 ```
-poetry install
+Now you’re ready to install ⚡Actions with the following command:
+
+```bash
+$ pip install giza-actions
 ```
 
-Be sure you're logged into the [Giza CLI](https://cli.gizatech.xyz/resources/users) and have created a workspace:
+## Setup
 
+From your terminal, create a Giza user through our CLI in order to access the Giza Platform:
+```bash
+$ giza users create
+```
+After creating your user, log into Giza:
+```bash
+$ giza users login
+```
+Optional: you can create an API Key for your user in order to not regenerate your access token every few hours.
+```bash
+$ giza users create-api-key
 ```
-giza workspaces create
+To create Actions Runs you will need a Giza Workspace, create it executing the following command in your terminal:
+```bash
+$ giza workspaces create
 ```
 
 ## Usage
 
 ### Defining Tasks
+A task is a function that represents a distinct segment of work in a Giza Actions workflow. Tasks provide a way to encapsulate parts of your workflow logic in traceable, reusable units across actions.
 
 Tasks are defined using the `@task` decorator. Here's an example:
 ```python
@@ -30,38 +57,48 @@ def preprocess():
     print(f"Preprocessing...")
 ```
 
-## Defining Actions
+### Defining Actions
+An action serves as a framework for coding ML inferencing workflow logic, enabling users to tailor the behaviour of their workflows.
 
 Actions are defined using the `@action` decorator. Here's an example:
 ```python
 from giza.action import action
 
-@action
+@action(name="My Action")
 def inference():
     print(f"Running inference...")
 ```
 
-## Running Actions
+### Deploy Actions
 
-Actions can be deployed executing the Python script defining the Action. For example, if the Action is defined in `example.py`, you can run it using:
+Deployments are server-side representations of actions. They keep essential metadata required for remote orchestration, including when, where, and how a workflow should run. Deployments transform workflows from functions that need to be manually activated to API-managed entities capable of being triggered remotely.
 
-```
-python example.py
-```
+We can easily create a deployment by creating the Action object and then calling the serve function in the entrypoint script:
 
-Then, you can execute the Action using the Prefect UI:
+```python
+from giza_actions.action import Action, action
+from giza_actions.task import task
 
-```
-prefect server start
-```
+@task
+def print_hello():
+    print(f"Hello Action!")
 
-## Examples
+@action
+def hello_world():
+    print_hello()
+
+if __name__ == '__main__':
+    action_deploy = Action(entrypoint=hello_world, name="hello-world-action")
+    action_deploy.serve(name="hello-world-action-deployment")
+```
 
-Examples of how to use the Actions SDK can be found in the `examples` directory. Each example includes a README with detailed instructions on how to run the example.
+Running this script will do two things:
+- Create a deployment called "hello-world-action" for your action in the Giza Platform.
+- Stay running to listen for action runs for this deployment; when a run is found, it will be asynchronously executed within a subprocess locally.
 
-For instance, the `imagenet` example demonstrates how to use the Giza SDK to perform image classification using a pre-trained ResNet-50 model from the ONNX model zoo.
+## Examples
 
-To understand how to execute these examples, please refer to the README file in each example's directory.
+Examples of how to use the Actions SDK can be found in the `examples` directory. Each example includes a README or a Notebook with detailed instructions on how to run the example.
 
 ## Contributing