Devices

Supported devices

As of right now, only CUDA and CPU devices are supported. How to install a working Python environment for either case please refer to the installation guide.

Some functions can be run on the GPU if a CUDA device is available and the python environment is properly installed with CUDA enabled. These functions will automatically detect if a CUDA device is available and will use it if so. It is possible to also force the use of a specific device through the device parameter of the respective function. For most GPU-capable functions it is possible to pass either cpu or cuda as a string to the device parameter. In a multi-GPU setup, the device can be specified by passing the device index as an integer (e.g. 0 for the first GPU, 1 for the second GPU, etc.). However, functions which use PyTorch expect the device to be a PyTorch device object, so you need to pass torch.device("cuda:0") instead of just 0. Which type of device is expected is documented in the respective function documentation.

As of now, the following functions can be run on the GPU:

• darts_preprocessing.preprocess_legacy_fast - device: "cpu" | "cuda" | int
• darts_postprocessing.prepare_export - device: "cpu" | "cuda" | int
• darts_segmentation.SMPSegmenter - device: torch.device
• darts_ensemble.EnsembleV1 - device: torch.device