We’re looking at this from opposite sides of the same coin.
The NN graph is written at a high-level in Python using frameworks (PyTorch, Tensorflow—man I really don’t miss TF after jumping to Torch :) ).
But the calculations don’t execute on the Python kernel—sure you could write it to do so but it would be sloooow. The actual network of calculations happen within the framework internals; C++. Then depending on the hardware you want to run it on, you go down to BLAS or CUDA, etc. all of which are written in low-level languages like Fortran or C.
Numpy fits into places all throughout this stack and its performant pieces are mostly implemented in C.
Any way you slice it: the post I was responding to is to argue that AI IS CODE. No two ways about that. It’s also the weights and biases and activations of the models that have been trained.
Other way around. The NNs are written in, mostly, Python. The frameworks, mainly Pytorch now, handle the heavy-duty math.
We’re looking at this from opposite sides of the same coin.
The NN graph is written at a high-level in Python using frameworks (PyTorch, Tensorflow—man I really don’t miss TF after jumping to Torch :) ).
But the calculations don’t execute on the Python kernel—sure you could write it to do so but it would be sloooow. The actual network of calculations happen within the framework internals; C++. Then depending on the hardware you want to run it on, you go down to BLAS or CUDA, etc. all of which are written in low-level languages like Fortran or C.
Numpy fits into places all throughout this stack and its performant pieces are mostly implemented in C.
Any way you slice it: the post I was responding to is to argue that AI IS CODE. No two ways about that. It’s also the weights and biases and activations of the models that have been trained.