Build a Secure AI Code Execution Workflow Using Daytona SDK

Understanding the Target Audience

The target audience for the Daytona SDK tutorial includes software developers, data scientists, and machine learning engineers who are interested in securely executing AI-generated code. Their primary goals are to:

• Ensure the security of their host environments while testing untrusted code.
• Improve their workflow efficiency by leveraging isolated execution environments.
• Gain hands-on experience with modern tools for AI and data processing.

Common pain points include:

• Concerns about executing potentially harmful code.
• Challenges in managing dependencies and environment configurations.
• Difficulty in scaling code execution across multiple tasks or datasets.

They prefer clear, concise communication with practical examples and step-by-step instructions that facilitate quick learning.

Tutorial Overview

This tutorial provides a comprehensive walkthrough for utilizing Daytona’s secure sandbox environment to execute untrusted or AI-generated Python code safely within a Notebook. It covers:

• Sandbox creation and basic code execution.
• Process isolation and dependency management.
• Data processing with pandas and file operations.
• Execution of complex AI-generated code snippets.
• Parallel task execution across multiple sandboxes.
• Resource management and cleanup procedures.

Getting Started with Daytona SDK

To begin, ensure you have the Daytona SDK installed. If it’s not installed, use the following command:

!pip install daytona-sdk

Then, 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

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

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

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

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, ensuring their host environments remain safe while enabling powerful data processing capabilities. The tutorial emphasizes the importance of resource management and provides a 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.