ethernity-cloud 0.0.14
Ethernity Cloud Runner Python

<p align="center">
  <a href="https://ethernity.cloud" title="Ethernity Cloud">
    <img src="https://ethernity.cloud/images/dark_gradient_logo.svg" alt="Ethernity Cloud logo" width="244" />
  </a>
</p>

<h3 align="center">The Python implementation of the Ethernity CLOUD runner protocol</h3>

### Installation

To begin developing with the Ethernity Cloud Runner, you need to set up your environment:

```console
$ python -m 'venv' venv
$ source venv/bin/activate
$ pip install ethernity-cloud-runner-py
```

This command will install the necessary dependencies for the Ethernity Cloud Runner package. With the packages installed, you're ready to start utilizing the Ethernity Cloud Runner functionality and explore its capabilities for your application development.

### Environment Prerequisites

#### 1. IDE

For a streamlined development process, we recommend using Visual Studio Code—a powerful and versatile code editor that offers a seamless experience for developers. You can download Visual Studio Code from the official [website](https://code.visualstudio.com/).

After running the IDE, a new workspace will be created, where you can start developing the framework and code.

#### 2. Python

Ensure you have Python installed on your local machine. You can download Python from the official [website](https://www.python.org/downloads/).

#### 3. Wallet Setup

A wallet is a crucial tool within the Ethernity Cloud ecosystem, empowering users to securely store and transfer their data for processing. As a developer, having a Web3 dApp testing wallet is necessary.

The wallet setup process with Metamask is detailed here: [Getting started with MetaMask](https://support.metamask.io/hc/en-us/articles/360015489531-Getting-started-with-MetaMask).

Currently, there are two networks for using Ethernity Cloud. Please set up your wallet for the desired network following one of the articles below:

- OpenBeta Network on bloxberg
- TestNet Network on bloxberg
- MainNet Network on Polygon

### 4. Code execution

To execute a new task using the Ethernity Cloud Runner, follow the straightforward template provided below. Simply insert your desired code into the designated section, and the runner will handle the rest, interacting with the IPFS network and processing the task accordingly.

```python
# ethernity_task.py
import os
import sys

from ethernity_cloud_runner_py.runner import EthernityCloudRunner  # type: ignore


def execute_task() -> None:
    ipfs_address = "http://ipfs.ethernity.cloud:5001/api/v0"

    code = '___etny_result___("Hello, Python World!")'

    runner = EthernityCloudRunner()
    runner.initialize_storage(ipfs_address)

    resources = {
        "taskPrice": 8,
        "cpu": 1,
        "memory": 1,
        "storage": 1,
        "bandwidth": 1,
        "duration": 1,
        "validators": 1,
    }
    # this will execute a new task using Python template and will run the code provided above
    # the code will run on the TESTNET network
    runner.run(
        "etny-pynithy-testnet",
        code,
        "",
        resources,
    )


if __name__ == "__main__":
    execute_task()

```

Save the above script as `ethernity_task.py` and run it from the command line:

```sh
$ python ethernity_task.py
```

make sure to have .env file with the following content:

```sh
PRIVATE_KEY=42....
```
Without it the script will not be able to make blockchain transactions.

This script will initialize the `EthernityCloudRunner`, set up the necessary event listeners, and execute a task using the provided code. The results and progress of the task execution will be printed to the console.

### `___etny_result___` function

The `___etny_result___` function is a special function used in Ethernity Cloud Runner tasks. When executing a task, this function allows the task code to send the result back from the Ethernity Cloud platform.

In the context of the Ethernity Cloud Runner, tasks are executed in a decentralized and distributed manner. The `___etny_result___` function acts as a communication channel between the task code and the Ethernity Cloud platform. When the function is called with the result data as its argument, it sends the result back to the platform, where it can be processed and stored.

This special function plays a crucial role in ensuring that the results of executed tasks are properly recorded and accessible. It enables seamless interaction with the Ethernity Cloud platform, making it a key component of the Ethernity Cloud Runner's functionality.


### Events subscription

In the Ethernity Cloud Runner integration, events play a crucial role in providing real-time feedback and updates during the execution of tasks. By subscribing to these events, developers can monitor the progress and completion status of their tasks.

**1. Task Created Event (`ECEvent.TASK_CREATED`):**

The `ECEvent.TASK_CREATED` event is triggered when a new task is successfully created and published on the network. This event signifies that the task has been registered and is ready for processing. Developers can define a function, such as `on_task_created`, to handle this event and execute any actions required upon task creation.

In the provided example code:

```python
def on_task_created(e):
    print('Task published.')
```

The `on_task_created` function simply logs the message "Task published." to the console, indicating that the task has been successfully created and registered on the EthernityCloud network.

**2. Task Order Placed Event (`ECEvent.TASK_ORDER_PLACED`):**

The `ECEvent.TASK_ORDER_PLACED` event is triggered when an order for task processing is placed and approved by the network. This event indicates that the task execution is about to begin. Developers can define a function, such as `on_task_order_placed`, to handle this event and perform any necessary actions when the task order is placed.

In the provided example code:

```python
def on_task_order_placed():
    print('Task order placed and approved, started processing.')
```

The `on_task_order_placed` function logs the message "Task order placed and approved, started processing." to the console, signaling that the task execution process has commenced.

**3. Task Not Processed Event (`ECEvent.TASK_NOT_PROCESSED`):**

The `ECEvent.TASK_NOT_PROCESSED` event is triggered when a task fails to be processed due to unavailability of nodes or when the network is currently busy or when the resource requirements are not met. This event indicates that the task execution encountered an issue and could not proceed. Developers can define a function, such as `on_task_not_processed`, to handle this event and respond appropriately to the failed task processing.

In the provided example code:

```python
def on_task_not_processed(e):
    print('Task processing failed due to unavailability of nodes. The network is currently busy. Please consider increasing the task price.')
```

The `on_task_not_processed` function logs the error message "Task processing failed due to unavailability of nodes. The network is currently busy. Please consider increasing the task price." to the console, providing information about the reason for the task processing failure. This message can be used to inform the user or perform any necessary error handling.

**4. Task Progress Event (`ECEvent.TASK_PROGRESS`):**

The `ECEvent.TASK_PROGRESS` event is triggered when there is progress in the execution of a task. To capture and handle this event, developers can define a function, such as `on_task_progress`, to process the event data. The event object, `e`, provides access to the event detail, which contains information about the task's current status.

In the example code provided:

```python
def on_task_progress(e):
    if e['status'] == ECStatus.ERROR:
        print(f"Error: {e['message']}")
    else:
        print(e['message'])
```

The `on_task_progress` function receives the event object `e`, and it checks the `e['status']` to determine if the task encountered an error or if it is progressing successfully. If an error is detected, the function logs the error message to the console using `print`, otherwise, it logs the progress message using `print`.

**5. Task Completed Event (`ECEvent.TASK_COMPLETED`):**

The `ECEvent.TASK_COMPLETED` event is triggered when a task is successfully completed. Similar to the previous event, developers can define a function, such as `on_task_completed`, to handle the event and access the task result.

In the example code provided:

```python
def on_task_completed(e):
    print(f"Task Result: {e['result']}; Task code: {e['resultTaskCode']}")
```

The `on_task_completed` function receives the event object `e`, and it accesses the task result from `e['result']`. The function then logs the result to the console, providing developers with the outcome of the completed task.

By subscribing to these events, developers can stay informed about the execution progress and results of tasks, enabling them to monitor and respond to task executions effectively. The Ethernity Cloud Runner's event system enhances the developer experience, allowing for seamless integration and handling of task-related events in real-time.

### Task resources

```python
resources = {
    'taskPrice': 10,
    'cpu': 1,
    'memory': 1,
    'storage': 40,
    'bandwidth': 1,
    'duration': 1,
    'validators': 1
}
```

The `resources` parameter provided in the `run` method as the last parameter is an object that defines the resource requirements for executing a new task using the Python/Node.js template. It specifies the amount of various resources needed for the task to be processed on the EthernityCloud network. The `resources` object contains the following properties:

1. `taskPrice`: This represents the price in tETNY that a user is willing to pay for the task execution. It determines the priority and readiness of the task for processing.

2. `cpu`: This specifies the amount of CPU (Central Processing Unit) resources required for the task. It indicates the computational power needed to execute the task's code.

3. `memory`: This indicates the amount of RAM memory required for the task's execution. It represents the storage space in RAM memory needed to run the task.

4. `storage`: This property represents the storage space needed for the task's execution. It indicates the amount of disk space required for the task.

5. `bandwidth`: This defines the amount of network bandwidth required for the task's execution. It represents the data transfer capacity needed to perform the task.

6. `duration`: This specifies the time duration or the maximum time allowed for the task's execution. It sets a time limit for how long the task can run.

7. `validators`: This property determines the number of validators required for the task. Validators are nodes on the network responsible for processing and validating tasks.

By providing these resource requirements in the `resources` object, the task execution engine (EthernityCloudRunner) can use this information to allocate the necessary resources and process the task accordingly on the specified TESTNET network.
```