Numba supports CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA execution model. Kernels written in Numba appear to have direct access to NumPy arrays. NumPy arrays are transferred between the CPU and the GPU automatically.
Several important terms in the topic of CUDA programming are listed here:
Most CUDA programming facilities exposed by Numba map directly to the CUDA C language offered by NVidia. Therefore, it is recommended you read the official CUDA C programming guide.
Numba supports CUDA-enabled GPU with compute capability 2.0 or above with an up-to-data Nvidia driver.
You will need the CUDA toolkit installed. If you are using Conda, just type:
$ conda install cudatoolkit
Numba does not implement all features of CUDA, yet. Some missing features are listed below: