With the explosive interest in AI, Nvidia extended the reach of CUDA by developing containers—NGC—Nvidia GPU-accelerated Containers. The company has developed containers for AI software like TensorFlow, PyTorch, MXNet, TensorRT, RAPIDS, and others.

But CUDA remains a closed, proprietary language and only runs efficiently on Nvidia GPUs. There are translation programs like AMD’s Boltzmann Initiative and HIP (Heterogeneous-compute Interface for Portability) for porting CUDA source code to a common C++ programming model.

Edinburgh-based Codeplay has also contributed to the TensorFlow stack, enabling it with SYCL, allowing any AI program to run on any OpenCL-enabled CPU, GPU, custom AI accelerators, DSPs, or FPGA. Intel has likewise adopted this approach by recently announcing their OneAPI across their processors (GPU, AI, and FPGA) with SYCL inside.