nerd-mega-compute 0.1.19

Run Python functions on powerful cloud servers with a simple decorator

NerdMegaCompute

Run compute-intensive Python functions in the cloud with a simple decorator.

Installation

From PyPI

Install via:

pip install nerd-mega-compute

Quick Start

1. Set up your API key: Create a .env file in your project directory with:

API_KEY=your_api_key_here

2. Decorate your function: Use the @cloud_compute decorator to run your function in the cloud:

from nerd_megacompute import cloud_compute

@cloud_compute(cores=8)  # Specify number of CPU cores
def my_intensive_function(data):
   result = process_data(data)  # Your compute-intensive code
   return result

result = my_intensive_function(my_data)

Example

Test a simple addition:

from nerd_megacompute import cloud_compute

@cloud_compute(cores=2)
def add_numbers(a, b):
   print("Starting simple addition...")
   result = a + b
   print(f"Result: {result}")
   return result

print("Running a simple test...")
result = add_numbers(40, 2)
print(f"The answer is {result}")

Features

• Execute intensive tasks on cloud servers
• Scale CPU cores for faster processing
• Automatic data transfer to/from the cloud
• Real-time progress updates

Configuration & Parameters

API Key & Debug Mode

Configure directly in code if not using a .env file:

from nerd_megacompute import set_api_key, set_debug_mode
set_api_key('your_api_key_here')
set_debug_mode(True)

@cloud_compute Parameters

• cores (default: 8): Number of CPU cores in the cloud.
• timeout (default: 1800): Maximum wait time in seconds.

Library Support & Restrictions

Your code can only use Python’s standard library or the following third-party libraries:

• numpy, scipy, pandas, matplotlib, scikit-learn, jupyter
• statsmodels, seaborn, pillow, opencv-python, scikit-image, tensorflow
• torch, keras, xgboost, lightgbm, sympy, networkx, plotly, bokeh
• numba, dask, h5py, tables, openpyxl, sqlalchemy, boto3, python-dotenv, requests

Why? This ensures compatibility and security in the cloud. Unsupported libraries may cause runtime errors.

Recommendations:

• Verify usage of supported libraries.
• Test locally with the same constraints.
• For additional libraries, refactor the code or contact us.

Multi-Core Usage

Guidelines

• Single-threaded code: Multiple cores won’t speed up functions not designed for parallel execution.

  @cloud_compute(cores=8)
  def single_threaded_function(data):
    return [process_item(item) for item in data]
  

• Parallelized code: Use multi-threading or multi-processing to utilize more cores effectively.

  from multiprocessing import Pool
  
  @cloud_compute(cores=8)
  def multi_core_function(data):
    with Pool(8) as pool:
       result = pool.map(process_item, data)
    return result
  

Supported Core Configurations

This library currently supports AWS Batch with Fargate compute environments. The number of cores must be one of the following values:

• 1 core
• 2 cores
• 4 cores
• 8 cores
• 16 cores

Tips

• Optimize your code for parallel execution.
• Experiment with different cores values to balance performance.

Limitations

• Serialization: Both the function and its data must be serializable (using Python’s pickle module). Use only serializable types:

  • Serializable: Integers, floats, strings, booleans, None, lists, tuples, dictionaries, and custom objects without non-serializable attributes.
  • Not Serializable: Nested/lambda functions, open file handles, database connections, or objects with non-serializable attributes.

  Test serialization example:

  import pickle
  
  try:
    pickle.dumps(your_function_or_data)
    print("Serializable!")
  except pickle.PickleError:
    print("Not serializable!")
  

• Hardware Constraints: The decorator is for CPU-based tasks. For GPU tasks, consider dedicated GPU cloud services.

• Internet Requirement: An active internet connection is needed during function execution.