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GPU Optimization with Run:ai Atlas
Introduction Overview of Run:ai Atlas platform Reference Architecture of Run:ai Atlas on Dell infrastructure Use case validation for Run:ai Atlas
Overview
Dashboard and analytics
Training and Interactive build
GPU fractions
Hyperparameter optimization
Deployment Considerations Why Dell Technologies? Dell Technologies services and support The Dell Technologies Customer Solutions Center Conclusion References

Hyperparameter optimization

Hyperparameter optimization

  • Hyperparameter optimization is the process of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter’s value is used to control the learning process, to define the model architecture or the data preprocessing process, and so on. Examples of hyperparameters include learning rate, batch size, different optimizers, and number of layers.

    There are several strategies for searching the hyperparameter space, such as Random search and Grid search. 

    We ran hyperparameter optimization by using two GPU instances with two concurrent pods with total of four completions:

    runai submit hpo1 -I gcr.io/run-ai-demo/quickstart-hpo -g 2 \ --parallelism 2–-completions 4 -v /root/runai-robin-test:/nfs

    Figure 5. Hyperparameter tuning with Run:ai Atlas