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> However, Python is unmatched for developer speed in compute heavy AI / ML since it uses C, etc., under the hood. John Carmack seems to think otherwise. > "T

> However, Python is unmatched for developer speed in compute heavy AI / ML since it uses C, etc., under the hood. John Carmack seems to think otherwise. > "That means that in the time that Python can perform a single FLOP, an A100 could have chewed through 9.75 million FLOPS" https://twitter.com/id_aa_carmack/status/1503844580474687493?lang=en https://twitter.com/id_aa_carmack/status/1503844580474687493...

cldellow 4y ago
Am I missing something? The OP's comment is that while Python's native interpreter is slow, Python code as a whole can still be fast since people will delegate the hot spots to non-Python libraries optimized for speed. To refute that, you linked to John Carmack quoting Horace He saying that Python's native interpreter does CPU-based math slower than a GPU. But...that's why people writing Python use libraries to delegate math-intensive work to a GPU, which is the OP's point.
- grumpyprole 4y ago
  If one is forced into writing code that needs to be fast in a language other than Python, then I'm not sure I understand why Python gets to claim any credit?
  - cldellow 4y ago
    Slight nitpick: the credit that was being claimed is that Python allows fast developer speed while not sacrificing overall runtime performance. The code that is written in a language other than Python is often not written by the developer writing the Python code. As an example, I've been fiddling with inferring automatic data extraction rules for data in scraped webpages. The meat of the algorithm and its heuristics is in Python. The heavy lifting of parsing HTML and evaluating CSS selectors is done by a C parser that I did not write. For me, prototyping the heuristics in C would have been quite a bit slower.
dekhn 4y ago
I generally respect Carmack, but it's clear he wasn't considering Jax or even numpy, but just python flops. Jax code running on TPUs often reaches order of magnitude of the hardware's capabilities.

cldellow 4y ago

Am I missing something? The OP's comment is that while Python's native interpreter is slow, Python code as a whole can still be fast since people will delegate the hot spots to non-Python libraries optimized for speed. To refute that, you linked to John Carmack quoting Horace He saying that Python's native interpreter does CPU-based math slower than a GPU. But...that's why people writing Python use libraries to delegate math-intensive work to a GPU, which is the OP's point.

grumpyprole 4y ago
If one is forced into writing code that needs to be fast in a language other than Python, then I'm not sure I understand why Python gets to claim any credit?
- cldellow 4y ago
  Slight nitpick: the credit that was being claimed is that Python allows fast developer speed while not sacrificing overall runtime performance. The code that is written in a language other than Python is often not written by the developer writing the Python code. As an example, I've been fiddling with inferring automatic data extraction rules for data in scraped webpages. The meat of the algorithm and its heuristics is in Python. The heavy lifting of parsing HTML and evaluating CSS selectors is done by a C parser that I did not write. For me, prototyping the heuristics in C would have been quite a bit slower.

dekhn 4y ago

I generally respect Carmack, but it's clear he wasn't considering Jax or even numpy, but just python flops. Jax code running on TPUs often reaches order of magnitude of the hardware's capabilities.