Do you train artificial intelligence models and want to improve their performance without spending hours manually testing different configurations?
This training course will teach you how to optimize the hyperparameters of your AI models and harness the power of GPUs and a supercomputer’s multi-GPU environments.
Course outline:
- Understand hyperparameters and the main methods for optimizing them
- Discover the concept of pruning
- Optimize hyperparameters on a GPU
- Scale up in a multi-GPU environment
- Analyze model performance
- Compare and discuss the gains achieved after optimization
- Put these concepts into practice through exercises on the ROMEO2025 supercomputer’s GPUs
Why participate?
- Learn how to optimize the hyperparameters of your artificial intelligence models
- Understand the metrics used to evaluate a model’s performance
- Reduce the time required to find the best configurations
- Effectively leverage multi-GPU resources
- Conduct experiments directly on the ROMEO2025 supercomputer
Target Audience:
This training is intended for engineers and researchers who wish to optimize their models and hyperparameters by leveraging the multi-GPU computing power available on the nodes of a supercomputer such as ROMEO.
Pre-required logistics
Prerequisites:
Knowledge of Python. If you do not have this, you can take free courses to learn the basics of Python. These courses include:
Learn the basics of Python – OpenClassrooms
Get started with Python for data analysis – OpenClassrooms
Basic knowledge of high-performance computing (HPC). The two courses ‘Introduction to Supercomputers’ and ‘My First Steps on a Supercomputer’ offered by ROMEO cover this prerequisite.
Understanding the fundamental concepts of deep learning. The ‘Fundamentals of Deep Learning’ course offered by ROMEO covers this prerequisite.
The ‘Accelerate your AI projects with HPC and multi-GPUs’ course offered by ROMEO is recommended but not compulsory.