pathos is a framework for heterogeneous computing that provides tools for parallel graph management and execution. It offers a consistent high-level interface for configuring and launching parallel computations across diverse resources, aiming to extend user code to parallel and distributed computing with minimal refactoring. The library is currently at version 0.3.5 and has a consistent release cadence with minor versions released every few months, typically adding incremental features and dependency updates.
pip install pathosVerified import paths — ran on the pinned version, not inferred.
This example demonstrates how to use `pathos.multiprocessing.ProcessingPool` to parallelize a function with multiple arguments using its enhanced `map` method. The `nodes` parameter configures the number of worker processes. Ensure the `if __name__ == '__main__':` block is used for multiprocessing compatibility.
Upgrade your Python environment to version 3.9 or higher. For compatibility with older Python versions, install an earlier `pathos` release (e.g., `pip install 'pathos<0.3.5'` for Python 3.8, or `pip install 'pathos<0.3.1'` for Python 3.7).
Design your parallel functions to return explicit results from worker processes. If shared mutable state is absolutely necessary, consider using `multiprocess.Manager` objects (e.g., `Manager().dict()`, `Manager().list()`) or explicit inter-process communication mechanisms like queues or pipes, understanding their inherent overhead.
Pass each argument iterable as a separate argument to the `pool.map()` call. For a function `f(a, b)`, you can call `pool.map(f, iterable_a, iterable_b)` directly, where `iterable_a` and `iterable_b` are sequences of arguments for `a` and `b` respectively.
This is a feature of `pathos` that solves a common `multiprocessing` problem. If you encounter serialization issues with standard `multiprocessing`, `pathos` is designed to handle such complex objects transparently. Ensure `dill` is correctly installed and `pathos`'s pool implementations are utilized.
Ensure `dill` and `multiprocess` are correctly installed: `pip install dill multiprocess`. On Windows, a C++ compiler might be required for `multiprocess` to compile correctly. Explicitly import from `pathos.multiprocessing` (e.g., `from pathos.multiprocessing import ProcessingPool`). If passing class methods, define them at the top level of a module or make them static/class methods.
Install `pathos` and its dependencies using pip: `pip install pathos`. If issues persist, try upgrading setuptools (`pip install --upgrade setuptools`) then reinstalling `pathos`. Also, ensure that `multiprocess` and `dill` are explicitly installed: `pip install multiprocess dill`.
Manually manage the pool lifecycle by calling `pool.close()` and `pool.join()` explicitly after the parallel computation is complete, instead of solely relying on the `with` statement's implicit exit.