Python's standard function calls are synchronous and blocking, executing each function sequentially and waiting for each to complete before moving on. This design can be a limitation, especially with I/O-bound or long-running operations.
functionmonitor is designed specifically for use in Jupyter notebooks to enable easy, asynchronous function execution. By running functions in separate threads, you can manage multiple tasks concurrently within the interactive Jupyter environment, allowing for:
- Concurrent function execution, freeing up the main thread to continue processing other tasks.
- Real-time monitoring of each function's progress.
- Automatic result assignment to variables in the global namespace for easy access as soon as functions complete.
- Execute Functions Concurrently: Run functions in separate threads, improving efficiency by enabling asynchronous execution.
- Easy Result Access: Results are assigned to global variables with the same name as the task key if the
create_variablesparameter is enabled. - Supports Any Callable: Works with any callable (function, lambda, etc.), allowing for flexible function management.
To install functionmonitor, simply use pip:
pip install functionmonitor-
Basic Structure
functionmonitorbehaves similarly to a dictionary, where each task is stored with a task name as the key and the result as the value. Once a task completes, its result is directly accessible. -
Using Callables
Any callable can be assigned tofunctionmonitor, allowing functions, lambda expressions, and more to run asynchronously. Prefixing the callable withlambdaprevents immediate execution, enabling background processing.
import functionmonitor
fm = functionmonitor.get_fm(create_variables=True)
fm[{var}] = lambda: {func()}