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Automate Machine Learning with H2O Driverless AI on Dell Infrastructure
Introduction Revision history The challenges of AI adoption Overview of AutoML and H2O Driverless AI Solution architecture for AutoML
Solution architecture for AutoML
Kubernetes-based deployment using Enterprise Steam
Docker image
Security
GPU support
Storage and network configuration
Licensing
Invoking H2O Driverless AI from cnvrg.io MLOps Platform AutoML on an optimized Dell infrastructure Sizing of AutoML infrastructure Validated use cases for AutoML Dell Technologies services and support The Dell Technologies Customer Solutions Center Conclusion References Appendix A – Model serving in cnvrg.io

GPU support

Home > AI Solutions > Artificial Intelligence > White Papers > Automate Machine Learning with H2O Driverless AI on Dell Infrastructure > GPU support

GPU support

  • H2O Driverless AI can run on machines with only CPUs or machines with CPUs and GPUs. H2O Driverless AI supports NVIDIA A100 and A30 GPUs. Only one GPU is supported per instance. Image and NLP use cases in H2O Driverless AI benefit significantly from GPU usage. Model building algorithms such as XGBoost (GBM/DART/RF/GLM), LightGBM (GBM/DART/RF), PyTorch (BERT models), and TensorFlow (CNN/BiGRU/ImageNet) models use GPU.

    NVIDIA’s Multi-Instance GPU (MIG) feature can be used to partition the GPUs, increase overall GPU utilization, and support several types of use cases and deployments with guaranteed quality of service. For more information about GPU partitioning recommendations, see to the NVIDIA Multi-Instance GPU and NVIDIA Technical Brief.

    Image and NLP use cases in H2O Driverless AI benefit significantly from GPU usage. Model building algorithms such as XGBoost (GBM/DART/RF/GLM), LightGBM (GBM/DART/RF), PyTorch (BERT models), and TensorFlow (CNN/BiGRU/ImageNet) models use GPU.