multi-env-employer 1.0.1

A library for calling functions and generators from Python modules located in other virtual environments, including environments with different Python versions and conflicting dependencies.

       

MultiEnvEmployer

MultiEnvEmployer — a Python library for calling functions and generators from modules in other virtual environments, including environments with different Python versions and conflicting dependencies.

The library is designed for situations where code cannot be imported directly but needs to be called and controlled from a single main process.

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Key Features

• run Python code in isolated virtualenvs
• support for different Python versions
• call functions as regular Python functions
• pass data between processes
• support for return, yield, and streaming large data
• intercept print() without breaking IPC
• execution time control (timeouts)
• error handling as regular exceptions
• file caching of results

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Quick Start

from MultiEnvEmployer import Employer, RemoteModule

emp = Employer(
    project_dir="/path/to/project",
    venv_path="/path/to/venv"
)

moduleA = RemoteModule(emp, "moduleA")

result = moduleA.add(2, 4)
print(result)

The moduleA.py module should be located in the specified project directory and will be executed in a different virtualenv.

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How It Works

• code runs in a separate Python process
• the specified virtualenv is used
• data is exchanged through a binary channel (pickle)
• errors and print() are correctly proxied to the main process

From the user's perspective, calls look like regular Python code.

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Data Return

Supported:

• regular return
• generators (yield)
• streaming large data (automatic)

For large objects, data is sent in chunks to avoid memory overload.

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Timeouts

Supported modes:

• unlimited
• hard timeout
• inactivity-based timeout (progress)

If the time limit is exceeded, the worker process will be terminated.

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Error Handling

Errors inside the remote code are raised as Python exceptions:

• RemoteExecutionError
• RemoteTimeoutError
• RemoteImportError
• and others

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Who This Library Is For

MultiEnvEmployer is useful if you need to:

• run code with conflicting dependencies
• support multiple Python versions
• avoid using import due to architectural constraints
• control code execution at runtime

This is not a dev tool, but an execution infrastructure layer.

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Documentation and Source Code

Full documentation, architecture, and examples are available on GitHub:

https://github.com/REYIL/MultiEnvEmployer

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License

MIT License