multiprocessing creates independent worker processes, each with its own Python interpreter and address space.
Understanding the Concept
multiprocessing creates independent worker processes, each with its own Python interpreter and address space. It can use multiple CPU cores but requires explicit communication and serialization.
Process startup methods and default behavior differ across platforms.
How to reason about concurrent execution
Workers may start and finish in a different order on every run. Focus on ownership of data, how work and errors move between workers, and what happens when one worker stops early. Locks protect invariants, queues exchange messages, and explicit shutdown logic prevents blocked or orphaned workers.
Concurrency adds overhead and failure modes. Begin with a working sequential solution, choose bounded worker counts and explicit timeouts, then measure whether overlap or parallel execution improves the target workload.
Worked Example
Read the code from the first line to the last. Identify which values are inputs, which operation is being demonstrated and what should happen before running it.
from multiprocessing import get_context
print(get_context().get_start_method())
# Save top-level worker definitions in a .py file for cross-platform child processes.Walkthrough: What Each Part Does
- The imports make multiprocessing available. These are standard-library building blocks unless the example explicitly names an external tool.
- The last lines coordinate completion and inspect results. Actual worker scheduling may differ between executions.
This demonstration focuses on one practical behavior. Before changing several lines, vary one input or one configuration choice and check whether the result changes for the reason you expected.
Example Output
forkThe output above was captured by running the example in the available Python environment. The order of concurrent events, durations and platform-dependent values can legitimately differ on your computer.
Important Behavior and Edge Cases
Expecting changes to a normal global list in a child process to appear automatically in the parent.
Process startup methods and default behavior differ across platforms.
Practice Exercise
Model a CPU calculation in a standalone script using a process worker and Queue for results.
Write down the result you expect before running your changed code. Check at least one normal case and one boundary or failure case. If your example starts workers or opens resources, verify how they are cleaned up.
Further reading: official documentation and API reference.
Common Beginner Mistakes
- Expecting changes to a normal global list in a child process to appear automatically in the parent.
- Forgetting to handle worker errors and shutdown, even when the normal completion path appears correct.
Key Takeaways
- multiprocessing creates independent worker processes, each with its own Python interpreter and address space.
- Process startup methods and default behavior differ across platforms.
- Practice: Model a CPU calculation in a standalone script using a process worker and Queue for results.