procyl 1.0.0

A lightweight worker system for creating, compiling, running, and managing Python-based processes

Procyl v1.0.0

A lightweight, intelligent worker system for creating, compiling, running, and managing Python-based processes.

Procyl simplifies the management of isolated Python workers with automatic dependency detection, environment management, and parallel execution support.

✨ Features

• 🔧 Automatic Dependency Detection - Scans code for external packages (not stdlib)
• 🎯 Smart Environment Management - One-time environment setup via procyl.prepare()
• 🚀 Multiple Execution Modes - Python source, PyInstaller, or Nuitka compilation
• ⚡ Parallel Execution - Run workers multiple times simultaneously
• 📊 Rich Metadata - Access worker info via .data property
• 🛡️ Verification - Built-in worker integrity checks
• 📦 Minimal Footprint - Lightweight, disk-efficient design
• 🔄 Version Control - Support for specific package versions and requirements files

📦 Installation

pip install procyl

For compilation support:

pip install procyl[compile]

🚀 Quick Start

1. Create Workers

Workers are Python functions executed in isolated processes:

import procyl

# Create a simple worker
worker = procyl.create(
    "greet",
    '''
import sys
print(f"Hello, {sys.argv[1]}!")
'''
)

2. Prepare Environment

Install all dependencies used by workers:

# Automatic - scans all workers
procyl.prepare()

# With specific versions
procyl.prepare(constraints={
    "requests": "==2.32.3",
    "numpy": ">=2.3,<3"
})

# From requirements file
procyl.prepare(requirements="requirements.txt")

3. Access Worker Metadata

print(worker.data.hash)              # Code hash
print(worker.data.compiler)          # Compiler used
print(worker.data.compiled)          # Compilation status
print(worker.data.dependencies)      # External dependencies
print(worker.data.python_version)    # Python version
print(worker.data.platform)          # Platform info
print(worker.data.path)              # Artifact path
print(worker.data.size)              # Artifact size
print(worker.data.running)           # Is running?

4. Verify Workers

result = worker.verify()
print(result)
# {
#     'name': 'greet',
#     'valid': True,
#     'issues': [],
#     'dependencies': ['requests', 'numpy']
# }

5. Run Workers

# Single execution
output = procyl.run("greet", args=["Alice"])

# Multiple parallel executions
results = worker.run(count=8, args=["Bob"])
for output in results:
    print(output)

📚 API Reference

procyl.create()

Create a new worker.

worker = procyl.create(
    name: str,                          # Worker name
    code: str,                          # Python code
    icon: Optional[str] = None,         # Icon path
    args: Optional[List[str]] = None,   # Default arguments
    compiler: str = "auto",             # python/pyinstaller/nuitka/auto
    output_dir: Optional[str] = None,   # Output directory
    timeout_seconds: Optional[int] = None,
    auto_delete_after: Optional[int] = None,
    compile_args: Optional[List[str]] = None,
)

Returns: Worker object

procyl.prepare()

Prepare the Python environment with dependencies.

success = procyl.prepare(
    constraints: Optional[Dict[str, str]] = None,
    requirements: Optional[str] = None,
)

Parameters:

• constraints: Dict of package version constraints
• requirements: Path to requirements.txt file

Returns: bool - Success status

worker.data

Access worker metadata (read-only).

Properties:

• hash - SHA256 hash of code (16 chars)
• compiler - Compiler used
• compiled - Whether compiled
• created_at - Creation timestamp
• last_build - Last build timestamp
• dependencies - Set of external package names
• python_version - Python version
• platform - Platform string
• path - Path to artifact
• size - Artifact size in bytes
• pid - Process ID (if running)
• running - Whether currently running

worker.verify()

Verify worker integrity.

result = worker.verify()
# Returns dict with:
# - 'name': worker name
# - 'valid': boolean
# - 'issues': list of issues
# - 'compiled': (optional)
# - 'artifact_size': (optional)
# - 'dependencies': (optional)

worker.run()

Execute worker once or multiple times in parallel.

results = worker.run(
    count: int = 1,                     # Number of parallel executions
    args: Optional[List[str]] = None,   # Arguments (overrides default)
)

Returns: List[str] - List of outputs

procyl.run()

Execute a worker by name.

output = procyl.run(
    name: str,
    args: Optional[List[str]] = None,
)

procyl.status()

Get worker status.

status = procyl.status(name: str)

procyl.delete()

Delete a worker.

result = procyl.delete(name: str)

procyl.precompile()

Compile a worker ahead of time.

result = procyl.precompile(
    name: str,
    output_dir: Optional[str] = None,
    compiler: Optional[str] = None,
    thread: bool = False,
)

📁 File Structure

.procyl/                    # Created by prepare()
├── env/                    # Python venv
│   ├── bin/               # Python executables
│   ├── lib/               # Installed packages
│   └── ...
├── .metadata/             # Worker metadata
└── metadata.json          # Environment info

🔍 Automatic Dependency Scanning

Procyl automatically scans code for external dependencies using AST analysis:

worker = procyl.create("demo", '''
import requests
import numpy as np
import sys           # stdlib - ignored
import os            # stdlib - ignored

print("Hello")
''')

print(worker.data.dependencies)
# Output: {'requests', 'numpy'}

🎯 Use Cases

Multi-threaded Workers

worker = procyl.create("compute", "print(sum(range(1000)))")
results = worker.run(count=8)
print(f"Computed {len(results)} times in parallel")

Version-Pinned Dependencies

procyl.prepare(constraints={
    "tensorflow": "==2.13.0",
    "numpy": "<2.0"
})

Compiled Executables

worker = procyl.create("app", code)
procyl.precompile("app", compiler="pyinstaller", output_dir="./dist")

📝 Examples

See examples/demo.py for a comprehensive example:

python examples/demo.py

🧪 Testing

Run tests:

pytest tests/test_core_v1.py -v

Run verification:

python verify.py

📄 Documentation

• CHANGELOG_v1.md - What's new in v1.0.0
• MIGRATION_v1.md - Migration guide from v0.1.1
• README_v1.md - Full API documentation

📄 License

MIT

👤 Author

yolezz

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Procyl v1.0.0 - Intelligent Python Worker Management