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Were you doing anything with: 1. custom units 2. custom GPU kernels 3. Custom array types 4. custom bayesian priors. 5. AD through custom types 6. Task based

by amkkma 6y ago

Were you doing anything with: 1. custom units 2. custom GPU kernels 3. Custom array types 4. custom bayesian priors. 5. AD through custom types 6. Task based parallelism 7. symbolic gradients with modeling toolkit 8. Agent based modeling 9. physics informed neural networks 10. abstract tables types ... or various combinations of the above?

glial 6y ago
Is Julia better for these use-cases? I'd interested and would love some links/examples.
- UncleOxidant 6y ago
  > 2. custom GPU kernels You can write them in Julia, whereas in Python you have to write them in C/C++ and then use the FFI to call them.
  - helgie 6y ago
    You can write them in python using numba
WanderPanda 6y ago
I think the biggest thing that is "light years ahead" is that numpy is built right in and insanely extensible. Libraries can be very lightweight / maintainable because they don't need to roll their own numpy like tensorflow pytorch etc. do (which makes them depend on bigcorps to improve and maintain). In julia somehow everything is compatible and composable. On the other hand counting from 1 is is quite a big issue for me and leads to constant errors when I switch between python and julia. Also the startup time it takes is a huge bummer. They should have an interpreted mode for debugging (as long as the debugger is unusable for e.g. breakpoints)
- UncleOxidant 6y ago
  I haven't tried it yet as it's still a release candidate, but startup time is said to be much improved in v1.6.0.
- adgjlsfhk1 6y ago
  I think the key is less that it has good multi-dimensional arrays built in, and more that Multiple dispatch makes Julia more composable than python. For an example of this, consider that BandedMatrices is a package that can be used in conjunction with any library that is expecting a Matrix like object, despite the fact that most of them weren't designed to do so.
fishmaster 6y ago
"Please don't think that Julia is only useful for 1. custom units 2. custom GPU kernels 3. Custom array types 4. custom bayesian priors. 5. AD through custom types 6. Task based parallelism 7. symbolic gradients with modeling toolkit 8. Agent based modeling 9. physics informed neural networks 10. abstract tables types ..."
deleted 6y ago
[deleted]

by amkkma 6y ago

glial 6y ago

Is Julia better for these use-cases? I'd interested and would love some links/examples.

UncleOxidant 6y ago
> 2. custom GPU kernels You can write them in Julia, whereas in Python you have to write them in C/C++ and then use the FFI to call them.
- helgie 6y ago
  You can write them in python using numba

WanderPanda 6y ago

I think the biggest thing that is "light years ahead" is that numpy is built right in and insanely extensible. Libraries can be very lightweight / maintainable because they don't need to roll their own numpy like tensorflow pytorch etc. do (which makes them depend on bigcorps to improve and maintain). In julia somehow everything is compatible and composable. On the other hand counting from 1 is is quite a big issue for me and leads to constant errors when I switch between python and julia. Also the startup time it takes is a huge bummer. They should have an interpreted mode for debugging (as long as the debugger is unusable for e.g. breakpoints)

UncleOxidant 6y ago
I haven't tried it yet as it's still a release candidate, but startup time is said to be much improved in v1.6.0.
adgjlsfhk1 6y ago
I think the key is less that it has good multi-dimensional arrays built in, and more that Multiple dispatch makes Julia more composable than python. For an example of this, consider that BandedMatrices is a package that can be used in conjunction with any library that is expecting a Matrix like object, despite the fact that most of them weren't designed to do so.

fishmaster 6y ago

"Please don't think that Julia is only useful for 1. custom units 2. custom GPU kernels 3. Custom array types 4. custom bayesian priors. 5. AD through custom types 6. Task based parallelism 7. symbolic gradients with modeling toolkit 8. Agent based modeling 9. physics informed neural networks 10. abstract tables types ..."

deleted 6y ago

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