GPUs in Driverless AI

Driverless AI can run on machines with only CPUs or machines with CPUs and GPUs. For the best (and intended-as-designed) experience, install Driverless AI on modern data center hardware with GPUs and CUDA support. Feature engineering and model building are primarily performed on CPU and GPU respectively. For this reason, Driverless AI benefits from multi-core CPUs with sufficient system memory and GPUs with sufficient RAM. For best results, we recommend GPUs that use the Pascal or Volta architectures. Ampere-based NVIDIA GPUs are also supported on x86 machines (requires NVIDIA CUDA Driver 11.2 or later).

Driverless AI ships with NVIDIA CUDA 11.2.2 and cuDNN.

Image and NLP use cases in Driverless AI benefit significantly from GPU usage.

Model building algorithms, namely, XGBoost (GBM/DART/RF/GLM), LightGBM (GBM/DART/RF), PyTorch (BERT models) and TensorFlow (CNN/BiGRU/ImageNet) models utilize GPU. Model scoring on GPUs can be enabled by selecting non-zero number of GPUs for prediction/scoring via num_gpus_for_prediction system expert setting of the experiment. Shapley calculation on GPUs is coming soon. MOJO scoring for productionizing models on GPUs can be enabled for some uses cases. See tensorflow_nlp_have_gpus_in_production in config.toml. Driverless AI Tensorflow, BERT and Image models support C++ MOJO scoring for production.

Feature engineering transformers such as ClusterDist cuML Transformer, TruncSVDNum cuML Transformer, DBSCAN cuML Transformer run on GPUs.

With Driverless AI Dask multinode setup, GPUs can be used for extensive model hyperparamenter search.

For details see -

GPUs can be enabled/disabled per Experiment. System expert settings of an experiment exposes some fine grained control of GPUs. For all other GPU related config settings see config.toml.