Solving the Printing Dilemma: Python Multiprocessing on Slurm Clusters скачать в хорошем качестве

Solving the Printing Dilemma: Python Multiprocessing on Slurm Clusters

Learn how to effectively use Python's multiprocessing with Slurm while ensuring your print statements output to the console correctly. --- This video is based on the question https://stackoverflow.com/q/77993588/ asked by the user 'Igor Agafonov' ( https://stackoverflow.com/u/4014823/ ) and on the answer https://stackoverflow.com/a/77994687/ provided by the user 'Booboo' ( https://stackoverflow.com/u/2823719/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, comments, revision history etc. For example, the original title of the Question was: Python multiprocessing on slurm doesn't output print() to console Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Solving the Printing Dilemma: Python Multiprocessing on Slurm Clusters When working with Python scripts on a cluster utilizing Slurm, many users encounter a frustrating issue—print() statements do not appear in the console. This common problem arises particularly when using Python’s multiprocessing library. In this guide, we’ll dive deep into this dilemma, exploring the reasons behind it and presenting effective solutions to make sure your output is visible as intended. Understanding the Problem Slurm, a widely-used job scheduler for high-performance computing, changes how output is handled when executing tasks in parallel. When running your Python script with srun, the print statements from multiple processes may not be flushed to the console simultaneously, leading to the impression that they are simply not executing. For example, if you've set up a multiprocessing pool using Pool(processes=num_processes) for data processing but don’t see your progress messages or debug statements appear, you're not alone. Key Observations Prints and Progress Bars in Multiprocessing: If your worker function attempts to write output to the same line in the console from different processes, synchronization issues may occur. Using flush=True: In Python, outputs are buffered. This means that unless you flush your outputs, the print statements may only appear once the program terminates, leading to a lack of real-time feedback. Minimal Reproducible Example: Lack of a simple demonstration can obscure the root of the problem, making it challenging to identify issues quickly. Solutions to the Printing Issue Let’s explore some practical approaches to resolve this problem, ensuring that your print output is displayed correctly. 1. Add flush=True in your Print Statements When printing progress information or debugging data, ensure that your print function includes flush=True. This forces the Python interpreter to write out the contents immediately: [[See Video to Reveal this Text or Code Snippet]] Including flush=True can significantly help in visualizing progress in real-time. 2. Implement a Progress Bar with Synchronization If you're updating a progress bar from multiple workers, consider using a Lock to control output access: [[See Video to Reveal this Text or Code Snippet]] This prevents race conditions that may lead to jumbled output in the console. 3. Use the Main Process for Output Another effective strategy is to have the main process handle all printing duties. By using apply_async() with callbacks, you can route updates back to the principal thread, which can print the progress or results directly. Here’s an example: [[See Video to Reveal this Text or Code Snippet]] 4. Redefining the Worker Function Sometimes, the worker function might need reworking for clarity. Consider returning indices or specific status updates that the main process can interpret in real-time. [[See Video to Reveal this Text or Code Snippet]] Alternative Methods: Chunking with imap_unordered While using map() has its advantages for automatically handling chunks of data, switching to imap_unordered() along with managing your own chunks can yield better control: [[See Video to Reveal this Text or Code Snippet]] This method will give you more granular control over updates to the console, which can be particularly useful for progress tracking. Conclusion When dealing with Python multiprocessing on Slurm, ensuring that your print statements give the feedback you need requires some adjustments. By incorporating flushing, managing synchronization, and letting the main process handle prints, you can achieve a smooth logging experience on your outputs. Implement these changes in your workflow to reclaim visibility into your multiprocess