cloudinvoke 0.1.0

The easiest way to run code on the cloud

CloudInvoke

The easiest way to run code on the cloud with automatic dependency detection and file upload support.

Features

• Decorator-based remote execution: Simply add a decorator to run functions on the cloud
• Automatic dependency detection: Automatically detects and bundles required imports and helper functions
• File upload support: Automatically detects and uploads file dependencies
• Async execution: Support for fire-and-forget execution patterns
• Full stdout/stderr capture: See all output from remote execution

Installation

From PyPI (once published)

pip install cloudinvoke

From wheel file

pip install cloudinvoke-0.1.0-py3-none-any.whl

From source

git clone https://github.com/yourusername/cloudinvoke.git
cd cloudinvoke
pip install -e .

Quick Start

from cloudinvoke import CloudRunner

# Initialize the runner with your API credentials
runner = CloudRunner(
    api_key="your_runpod_api_key",
    endpoint_id="your_endpoint_id"
)

# Decorate functions to run them on the cloud
@runner.remote
def process_data(x, y):
    import torch
    # Your computation here
    return x + y

# Call the function normally - it executes on the cloud
result = process_data(5, 3)
print(result)  # 8

Usage

Basic Remote Execution

from cloudinvoke import CloudRunner

runner = CloudRunner(api_key="your_api_key")

@runner.remote
def my_function(x, y):
    import numpy as np
    return np.array([x, y]).sum()

result = my_function(10, 20)
print(result)  # 30

Async Execution

For fire-and-forget patterns or when you want to submit multiple jobs:

@runner.remote_async
def long_running_task(n):
    import time
    time.sleep(n)
    return f"Slept for {n} seconds"

# Returns immediately with a request ID
request_id = long_running_task(10)

# Get the result later
result = runner.get_result(request_id, wait=True)
print(result)

Automatic File Upload

CloudInvoke automatically detects file references in your code and uploads them:

@runner.remote
def process_csv():
    import pandas as pd
    # File is automatically uploaded and available
    df = pd.read_csv('data.csv')
    return df.shape[0]

result = process_csv()

Debug Mode

Enable debug mode to see the generated code and execution details:

runner = CloudRunner(
    api_key="your_api_key",
    debug=True  # Enable debug output
)

@runner.remote
def test_function(x):
    return x * 2

result = test_function(5)
# Prints the generated code, function name, args, etc.

Configuration

The CloudRunner class accepts the following parameters:

• api_key (str, required): Your RunPod API key
• endpoint_id (str): RunPod endpoint ID (default: "yv68hhady2hgii")
• base_url (str): Base URL for RunPod API (default: "https://api.runpod.ai/v2")
• poll_interval (float): Seconds between status polls (default: 1.0)
• timeout (int): Maximum seconds to wait for execution (default: 300)
• debug (bool): Print debug information (default: False)
• auto_upload_files (bool): Automatically detect and upload files (default: True)
• base_dir (str): Base directory for resolving file paths (default: '.')

How It Works

CloudInvoke analyzes your decorated functions to:

1. Extract the function source code
2. Recursively collect all dependencies (imports and helper functions)
3. Detect file references in the code
4. Bundle everything into a payload
5. Submit to RunPod for execution
6. Poll for results and return them

Requirements

• Python 3.7+
• requests>=2.25.0

Development

Building from source

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Build wheel
python -m build

Running tests

pytest tests/

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

For issues and questions, please use the GitHub issue tracker.