> at least at an end-user level I'd say that the most popular option at the end-user level today is a whole level of abstraction above: using Python w/ PyTorch

> at least at an end-user level I'd say that the most popular option at the end-user level today is a whole level of abstraction above: using Python w/ PyTorch (or less frequently, TensorFlow). Combined with CuPy and cuNumeric, which is available at https://developer.nvidia.com/cunumeric https://developer.nvidia.com/cunumeric and handles scaling to multiple machines with Python-written code, through the Legate runtime (https://nv-legate.github.io/legate.core/README.html https://nv-legate.github.io/legate.core/README.html). This covers a huge amount of what the CUDA userbase uses.