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I/O Utilities User Manual

The cloudmesh.ai.common.io module provides a set of helper functions for common input/output operations, focusing on cross-platform path handling, YAML configuration management, and benchmark data generation.

Overview

This module simplifies the process of interacting with the file system and configuration files, ensuring that paths are resolved correctly regardless of the operating system (Windows, macOS, or Linux).

Key Features

  • Smart Path Expansion: Resolves ~, environment variables, and relative path components (. and ..) into absolute paths.
  • Safe YAML Handling: Provides wrappers for loading and dumping YAML files with built-in error handling and directory creation.
  • Benchmark File Generation: Utilities to create dummy files of specific sizes or YAML files with a large number of service entries for performance testing.

Usage Guide

Path Expansion

Use path_expand to ensure a path is absolute and compatible with the current OS.

from cloudmesh.ai.common.io import path_expand

# Expands ~ and environment variables
path = path_expand("~/my_project/$VERSION/config.yaml")
print(path) # Output: /home/user/my_project/1.0/config.yaml (on Linux)

Working with YAML

The load_yaml and dump_yaml functions provide a safe way to handle configuration files.

from pathlib import Path
from cloudmesh.ai.common.io import load_yaml, dump_yaml

config_path = Path("config.yaml")

# Load YAML safely
data = load_yaml(config_path)

# Modify data
if data:
    data['version'] = '2.0'

# Save YAML (automatically creates parent directories)
dump_yaml(config_path, data)

Creating Benchmark Files

For performance testing, you can generate large files or complex YAML structures.

from cloudmesh.ai.common.io import create_benchmark_file, create_benchmark_yaml

# Create a 100MB binary file for I/O testing
create_benchmark_file("/tmp/test_file.bin", 100)

# Create a YAML file with 1000 service entries
create_benchmark_yaml("/tmp/benchmark.yaml", 1000)

API Reference

Refer to the auto-generated API documentation for detailed function signatures.