Enhance Marimo With Thread & Process Mapping Functions

Marimo's functionality can be significantly improved by introducing thread_map() and process_map() functions. Inspired by tqdm.contrib.concurrent, these additions would provide users with powerful tools for parallelizing tasks within their Marimo notebooks. This enhancement not only aligns with Marimo's existing status bar widgets but also streamlines the execution of computationally intensive operations, leading to a more responsive and efficient user experience. The inclusion of these features will greatly benefit users who frequently work with large datasets or complex calculations, making Marimo a more versatile platform for data science and related fields.

The Need for Concurrent Mapping in Marimo

The core idea behind integrating thread_map() and process_map() is to enable concurrent execution of functions across multiple threads or processes. This is particularly useful when dealing with tasks that can be broken down into independent units, such as processing a large list of data points or performing multiple API calls. By leveraging these functions, users can significantly reduce the overall execution time of their code, leading to faster results and improved productivity. Consider a scenario where you're running a series of simulations or analyzing a large set of images; thread_map() and process_map() would allow you to distribute these tasks across multiple cores, thereby drastically reducing the time it takes to complete the analysis. This is a critical feature for anyone working with computationally heavy tasks within a Marimo notebook.

Moreover, the introduction of these functions would enhance the user experience by providing visual feedback on the progress of concurrent tasks. Marimo's status bar widgets, similar to those provided by tqdm, can be integrated to display progress bars, giving users a clear indication of how far along their tasks are and how much time remains. This level of transparency is essential for maintaining user engagement and allowing users to monitor the execution of their code effectively. The integration of progress bars will provide users with a seamless and informative experience, allowing them to track the execution of concurrent tasks and gain better insights into their projects.

Implementation Details and Suggested Solution

The implementation of thread_map() and process_map() in Marimo can follow a similar structure to the suggested solution. The core functionality revolves around the use of ThreadPoolExecutor and ProcessPoolExecutor from the concurrent.futures module. This allows for easy creation and management of thread and process pools, which handle the concurrent execution of tasks. The provided code example outlines a clear implementation strategy, including the necessary imports and the structure for both thread_map() and process_map() functions. The use of a common concurrent_map() function helps to reduce code duplication and maintainability. This structure also ensures that the functions can handle a variety of input types and provide appropriate feedback to the user, enhancing the usability and performance of Marimo.

from collections.abc import Callable, Collection, Iterable, Mapping, Sequence, Sized
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
from typing import Optional, Type

def concurrent_map[T, R](
    pool: Type[ProcessPoolExecutor] | Type[ThreadPoolExecutor],
    fn: Callable[[T], R],
    iterable: Iterable[T],
    *,
    total: Optional[int] = None,
    title: str | None = None,
    max_workers: Optional[int] = None,
) -> list[R]:
    results = []
    if total is None:
        if isinstance(iterable, Sized):
            total = len(iterable)
        else:
            raise ValueError("total must be specified for non-sized iterables")
    try:
        with pool(max_workers=max_workers) as executor:
            futures = executor.map(fn, iterable)
            for future in mo.status.progress_bar(futures, total=total, title=title):
                results.append(future)
    except KeyboardInterrupt:
        mo.stop(True, "Interrupted by user")
    return results

# This could also be done with functools.partial()
def thread_map[T, R](
    fn: Callable[[T], R],
    iterable: Iterable[T],
    *,
    total: Optional[int] = None,
    title: str | None = None,
    max_workers: Optional[int] = None,
) -> list[R]:
    return concurrent_map(
        ThreadPoolExecutor,
        fn,
        iterable,
        total=total,
        title=title,
        max_workers=max_workers,
    )


def process_map[T, R](
    fn: Callable[[T], R],
    iterable: Iterable[T],
    *,
    total: Optional[int] = None,
    title: str | None = None,
    max_workers: Optional[int] = None,
) -> list[R]:
    return concurrent_map(
        ProcessPoolExecutor,
        fn,
        iterable,
        total=total,
        title=title,
        max_workers=max_workers,
    )

The code defines a generic concurrent_map function that takes a pool type (ThreadPoolExecutor or ProcessPoolExecutor), a function to apply, an iterable, and optional parameters for total items, title, and the number of workers. This design promotes code reusability and simplifies the implementation of both thread_map and process_map. The thread_map function leverages ThreadPoolExecutor for thread-based parallelism, while process_map utilizes ProcessPoolExecutor for process-based parallelism. The optional parameters, like total and title, allow for seamless integration with Marimo's status bar widgets, providing users with real-time feedback on the progress of their concurrent tasks. This modular approach makes the code easy to understand, maintain, and extend, ensuring that the new functionalities fit well within the existing Marimo ecosystem.

Benefits and Practical Applications

The introduction of thread_map() and process_map() in Marimo offers several tangible benefits, including improved performance, enhanced user experience, and increased flexibility in handling various types of tasks. Users can expect significantly faster execution times for tasks that can be parallelized, leading to improved productivity and quicker results. The integration with Marimo's status bar widgets provides clear visual feedback on the progress of these tasks, making it easier for users to monitor and manage their computations. The ability to switch between thread-based and process-based parallelism allows users to optimize their code for different scenarios, depending on the nature of their tasks and the available resources.

Practical applications of these functions are vast and varied. Data scientists can use them to accelerate the processing of large datasets, such as applying a function to each row of a Pandas DataFrame. Machine learning practitioners can utilize them to parallelize hyperparameter tuning or feature engineering tasks. Researchers can employ them to speed up simulations or scientific calculations. The flexibility of these functions makes them suitable for a wide range of use cases, from simple data transformations to complex scientific analyses. By incorporating thread_map() and process_map(), Marimo would empower its users to tackle more ambitious projects and solve more complex problems with greater ease and efficiency.

Future Considerations and Extensibility

While the current implementation focuses on thread and process-based parallelism, there is potential for further expansion and integration with other parallelization strategies. One such consideration is the introduction of an interpreter_map() function, particularly relevant for Python versions 3.14 and later. This function would allow users to execute code across different Python interpreters, providing another layer of flexibility and performance optimization. Furthermore, the integration with other parallel computing frameworks, such as Dask or Ray, could provide even greater scalability and performance benefits. This could involve adding support for distributed computing, allowing users to run their Marimo notebooks on clusters of machines. This future proofing ensures that Marimo remains at the forefront of innovation and caters to the evolving needs of its users.

Conclusion

The implementation of thread_map() and process_map() in Marimo is a valuable enhancement that significantly improves its functionality and usability. These features offer performance gains, enhanced user experience, and greater flexibility for users working with computationally intensive tasks. The suggested solution provides a clear path for integrating these functions, aligning well with Marimo's existing features and promoting code reusability. As the platform evolves, further considerations, such as the addition of interpreter_map() and integration with other parallel computing frameworks, will ensure that Marimo continues to meet the needs of its users. This will solidify Marimo's position as a powerful and versatile tool for data science and related fields.

For more details on concurrent programming in Python, you can check out the official Python documentation: https://docs.python.org/3/library/concurrent.futures.html