For multi-GPU machines, users may want to select which GPU to use. By default the CUDA driver selects the fastest GPU as the device 0, which is the default device used by Numba.
The features introduced on this page are generally not of interest unless working with systems hosting/offering more than one CUDA-capable GPU.
If at all required, device selection must be done before any CUDA feature is used.
from numba import cuda
cuda.select_device(0)
The device can be closed by:
cuda.close()
Users can then create a new context with another device.
cuda.select_device(1) # assuming we have 2 GPUs
numba.cuda.select_device(device_id)Create a new CUDA context for the selected device_id. device_id should be the number of the device (starting from 0; the device order is determined by the CUDA libraries). The context is associated with the current thread. Numba currently allows only one context per thread.
If successful, this function returns a device instance.
numba.cuda.close()Explicitly close all contexts in the current thread.
Note
Compiled functions are associated with the CUDA context. This makes it not very useful to close and create new devices, though it is certainly useful for choosing which device to use when the machine has multiple GPUs.
The Device List is a list of all the GPUs in the system, and can be indexed to obtain a context manager that ensures execution on the selected GPU.
numba.cuda.gpusnumba.cuda.cudadrv.devices.gpus¶gpus is an instance of the _DeviceList class, from which
the current GPU context can also be retrieved:
numba.cuda.cudadrv.devices._DeviceListcurrentReturns the active device or None if there’s no active device