[Y_Y]

What's surprising to me is that this isn't better known. The only reliable solution I've found is to go with the pytorch or deepstream images from NGC. Conda is probably a good idea for noobs who need Cuda installed for them on windows, but otherwise I find it an endless source of finicky issues, especially for unsavvy ML scientists who are looking for a silver bullet for package management.

This link shows which package versions come in which Docker tag and is invaluable: https://docs.nvidia.com/deeplearning/frameworks/support-matr...

[rmnclmnt]

10 years ago, « Data Science » work past the experimental stage was performed by SWE with a knack for applied maths. So investing in tooling to do things properly was a given.

Nowadays, most DS people only want to do ML at the experimental stage only and get lost when things get on the engineering side of things. But for their defense, nowadays the bare minimum skills require to do programming, containerization, CI/CD, etc. More experienced and swiss army knife SWE/MLE have to educate the willing.

It was already the same 10 years ago with MATLAB dudes not wanting to get dirty with C/C++/ASM SIMD. The history repeats itself, only at a faster pace