GRNET announces, in the context of SmartAttica EDIH (European Digital Innovation Hub), the 10th Module of Τraining modules for SMEs with the subject "Multiprocessing in Python".
Date: July 14th, 2025, at 12:00 EET
Location: Online via Zoom
Presentation Languages: Greek
Instructor: Dr. Nikolaos Bakas (GRNET), Panagiota Gyftou
Description: Join us for an insightful seminar where we delve into the world of high-performance computing (HPC) with a focus on leveraging multiprocessing techniques and hyperparameter tuning. This session is designed to equip professionals with the skills to optimize computational tasks, particularly in machine learning applications, using Python's multiprocessing capabilities. We will explore practical examples, including the parallel execution of Random Forest models, to demonstrate how to efficiently utilize computational resources.
Target Audience: This seminar is tailored for professionals from small and medium-sized enterprises (SMEs) who are involved in data science, machine learning, or any computationally intensive tasks. It is ideal for those looking to enhance their understanding of parallel computing and improve the performance of their machine learning models.
Learning Objectives:
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Understand the fundamentals of multiprocessing in Python and its application in HPC environments.
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Learn how to implement parallel processing to optimize machine learning workflows.
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Gain insights into hyperparameter tuning techniques to improve model performance.
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Explore practical examples of using multiprocessing for Random Forest model training and evaluation.
Prerequisites: Participants should have a basic understanding of Python programming and familiarity with machine learning concepts. Prior experience with Random Forest models and basic knowledge of parallel computing will be beneficial but not required.
Indicative Contents:
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Introduction to High-Performance Computing (HPC) and its significance in SMEs.
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Overview of Python's multiprocessing module and its advantages.
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Step-by-step guide to implementing parallel processing in machine learning tasks.
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Case study: Parallel execution of Random Forest models using multiprocessing.
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Techniques for hyperparameter tuning in HPC environments.
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Best practices for optimizing computational resources in SMEs.
The project is co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them.
