Secure AI Code Execution Workflow with Daytona SDK for Developers

Understanding the Target Audience

The Daytona SDK tutorial is designed for software developers, data scientists, and machine learning engineers who want to execute AI-generated code securely. These professionals aim to:

• Protect their host environments while testing untrusted code.
• Enhance workflow efficiency through isolated execution environments.
• Gain practical experience with modern tools for AI and data processing.

Common challenges they face include concerns about executing potentially harmful code, managing dependencies, and scaling code execution across multiple tasks or datasets. They appreciate clear communication with practical examples and step-by-step instructions for quick learning.

Tutorial Overview

This tutorial provides a detailed guide on using Daytona’s secure sandbox environment to safely execute untrusted or AI-generated Python code within a Notebook. Key topics include:

• Creating a sandbox and executing basic code.
• Managing process isolation and dependencies.
• Data processing using pandas and file operations.
• Executing complex AI-generated code snippets.
• Running parallel tasks across multiple sandboxes.
• Resource management and cleanup procedures.

Getting Started with Daytona SDK

To start, ensure you have the Daytona SDK installed. If it’s not already installed, run the following command:

!pip install daytona-sdk

Next, import the necessary modules:

import daytona_sdk

Creating a Secure Sandbox

The first step is to create a secure sandbox using the Daytona SDK. This allows you to run code in an isolated environment:

class DaytonaTutorial:
    def __init__(self, api_key: str):
        self.config = DaytonaConfig(api_key=api_key)
        self.daytona = Daytona(self.config)
        self.sandboxes: List[Any] = []

    def basic_sandbox_demo(self):
        try:
            sandbox = self.daytona.create(CreateSandboxParams(language="python"))
            self.sandboxes.append(sandbox)
            code = 'print("Hello from Daytona Sandbox!")'
            response = sandbox.process.code_run(code)
        except Exception as e:
            print(f" Error in basic demo: {e}") 
    

Data Processing in an Isolated Environment

Next, you can perform data processing within the sandbox:

def data_processing_demo(self):
        try:
            sandbox = self.daytona.create(CreateSandboxParams(language="python"))
            install_cmd = "import subprocess; subprocess.run(['pip', 'install', 'pandas'])"
            response = sandbox.process.code_run(install_cmd)
            data_code = """
import pandas as pd
data = {'name': ['Alice', 'Bob'], 'age': [25, 30]}
df = pd.DataFrame(data)
print(df.describe())
"""
            response = sandbox.process.code_run(data_code)
        except Exception as e:
            print(f" Error in data processing demo: {e}") 
    

File Operations within the Sandbox

You can also perform file operations to read and write data securely:

def file_operations_demo(self):
        try:
            sandbox = self.daytona.create(CreateSandboxParams(language="python"))
            file_code = """
import json
data = {'message': 'Hello from Daytona!'}
with open('sample.json', 'w') as f:
    json.dump(data, f)
"""
            response = sandbox.process.code_run(file_code)
        except Exception as e:
            print(f" Error in file operations demo: {e}") 
    

Executing AI-Generated Code

Now, let’s run complex AI-generated code snippets safely:

def ai_code_execution_demo(self):
        try:
            sandbox = self.daytona.create(CreateSandboxParams(language="python"))
            ai_code = "# Fibonacci sequence\n def fib(n): return n if n <= 1 else fib(n-1) + fib(n-2)"
            response = sandbox.process.code_run(ai_code)
        except Exception as e:
            print(f" Error in AI code execution demo: {e}") 
    

Parallel Task Execution

You can execute multiple tasks in parallel across different sandboxes:

def parallel_execution_demo(self):
        try:
            tasks = ["print('Task 1')", "print('Task 2')"]
            results = []
            for task in tasks:
                sandbox = self.daytona.create(CreateSandboxParams(language="python"))
                response = sandbox.process.code_run(task)
                results.append(response.result)
        except Exception as e:
            print(f" Error in parallel execution demo: {e}") 
    

Cleanup Procedures

Finally, ensure proper cleanup of resources after execution:

def cleanup_sandboxes(self):
        for sandbox in self.sandboxes:
            self.daytona.remove(sandbox)
        self.sandboxes.clear() 
    

Conclusion

By following this tutorial, developers can effectively leverage Daytona for secure AI code execution. This ensures that their host environments remain safe while enabling powerful data processing capabilities. The emphasis on resource management provides a solid foundation for integrating Daytona into broader machine learning workflows.

Further Resources

For additional information, visit the Daytona website or check out their API key management section.