5 ms·

>>But if you're under pressure to build lots of small, partially documented, constantly changing parts every day, you might be better off using more flexible eq

>>But if you're under pressure to build lots of small, partially documented, constantly changing parts every day, you might be better off using more flexible equipment and materials (balsa wood, bailing wire, masking tape, etc.) to get the job done, even if the result will be messier and more prone to breaking. Python & Deno/Node fit the bill for the latter category. Would you consider Julia in the former or latter category for ML use-cases?

cs702 6y ago
For ML/scientific computing, it can fulfill both roles, I'd say. Julia looks about as flexible and easy to use as Python, R, and matlab for experimenting and iterating quickly with throwaway code, and superior to those languages in every other important dimension except one: it lacks Python's gigantic ecosystem.
- ChrisRackauckas 6y ago
  Julia has a much larger ecosystem in terms of things like numerical linear algebra, scientific computing, and differentiable programming. How do you use block banded Jacobians inside of ODE solvers? Python's ML ecosystems just barely got non-stiff methods, so advanced accelerations are fairly far away, whereas these are things that have worked with Julia's ML solvers for a long time now.
  - cs702 6y ago
    Before responding, first let me say thank you for all the work you have done and continue to do. Also as I wrote above, I view Julia as superior to Python in every other way. :-) By "ecosystem" I mean much more. PyPi currently offers around a quarter million Python packages, versus thousands for Julia. Github code and activity using Python is also currently around three orders of magnitude greater than for Julia. Python is currently the fourth most popular language on Stack Overflow (after JS, HTML, and SQL). It's a gigantic ecosystem that has evolved over three decades.
    - ChrisRackauckas 6y ago
      Yes true. I think it's a lot like the old Android vs iOS stuff though: counting the number of packages doesn't mean all that much because so many have been abandoned that Python probably has a much higher percentage of junk out there. That said, Python's ecosystem is still larger, but that doesn't mean it covers all domains well. I can name a bunch of holes that it has off of the top of my head, and same with Julia too, so it's not like either supersets each other. So I just think the "but Python has a bunch of packages" is a lot more nuanced than that: in many areas of ML or webdev it is much more developed, but when you get out of that realm it can get sparse in some ways.
      - cs702 6y ago
        It's not just packages and repos, but also installed base, developers, forums, discussions, corporate backers, etc. Otherwise, I agree that not all domains are well covered by any language :-)
        eigenspace 6y ago
        Something that I think is worth discussing is that julia has far more package developers per user than Python. That may seem like a weird thing to prop up, it seems to just suggest that Julia users more often find themselves needing to implement something themselves rather than use a pre-existing library, and that's definitely true. However, Julia also makes the experience of growing from a package user to a package developer basically seamless. A big part of what makes this seamless is that almost all julia packages are written in pure julia and thus relatively easy to read and understand for a julia programmer. This coupled with the strong introspection capabilities makes it so that users who would never be writing their own packages in Python end up contributing to the Julia ecosystem. Coupled to this is the fact that julia is a highly composable language meaning that it's very easy to combine functionality together from two separate packages that weren't designed to work together. This composability makes julia's fewer packages have far greater leverage and applicability than Python packages and also makes it so that when you do need to make your own package, you're less likely to be reinventing the wheel every time. Consider the fact that all the big machine learning libraries in Python all have basically implemented their own entire language with their own special compiler, all the requisite datastructures, etc. In Julia, that's rarely necessary. Flux.jl is just built using native julia datastructures and julia's own (very hackable) compiler.

cs702 6y ago

For ML/scientific computing, it can fulfill both roles, I'd say. Julia looks about as flexible and easy to use as Python, R, and matlab for experimenting and iterating quickly with throwaway code, and superior to those languages in every other important dimension except one: it lacks Python's gigantic ecosystem.

ChrisRackauckas 6y ago
Julia has a much larger ecosystem in terms of things like numerical linear algebra, scientific computing, and differentiable programming. How do you use block banded Jacobians inside of ODE solvers? Python's ML ecosystems just barely got non-stiff methods, so advanced accelerations are fairly far away, whereas these are things that have worked with Julia's ML solvers for a long time now.
- cs702 6y ago
  Before responding, first let me say thank you for all the work you have done and continue to do. Also as I wrote above, I view Julia as superior to Python in every other way. :-) By "ecosystem" I mean much more. PyPi currently offers around a quarter million Python packages, versus thousands for Julia. Github code and activity using Python is also currently around three orders of magnitude greater than for Julia. Python is currently the fourth most popular language on Stack Overflow (after JS, HTML, and SQL). It's a gigantic ecosystem that has evolved over three decades.
  - ChrisRackauckas 6y ago
    Yes true. I think it's a lot like the old Android vs iOS stuff though: counting the number of packages doesn't mean all that much because so many have been abandoned that Python probably has a much higher percentage of junk out there. That said, Python's ecosystem is still larger, but that doesn't mean it covers all domains well. I can name a bunch of holes that it has off of the top of my head, and same with Julia too, so it's not like either supersets each other. So I just think the "but Python has a bunch of packages" is a lot more nuanced than that: in many areas of ML or webdev it is much more developed, but when you get out of that realm it can get sparse in some ways.
    - cs702 6y ago
      It's not just packages and repos, but also installed base, developers, forums, discussions, corporate backers, etc. Otherwise, I agree that not all domains are well covered by any language :-)
      - eigenspace 6y ago
        Something that I think is worth discussing is that julia has far more package developers per user than Python. That may seem like a weird thing to prop up, it seems to just suggest that Julia users more often find themselves needing to implement something themselves rather than use a pre-existing library, and that's definitely true. However, Julia also makes the experience of growing from a package user to a package developer basically seamless. A big part of what makes this seamless is that almost all julia packages are written in pure julia and thus relatively easy to read and understand for a julia programmer. This coupled with the strong introspection capabilities makes it so that users who would never be writing their own packages in Python end up contributing to the Julia ecosystem. Coupled to this is the fact that julia is a highly composable language meaning that it's very easy to combine functionality together from two separate packages that weren't designed to work together. This composability makes julia's fewer packages have far greater leverage and applicability than Python packages and also makes it so that when you do need to make your own package, you're less likely to be reinventing the wheel every time. Consider the fact that all the big machine learning libraries in Python all have basically implemented their own entire language with their own special compiler, all the requisite datastructures, etc. In Julia, that's rarely necessary. Flux.jl is just built using native julia datastructures and julia's own (very hackable) compiler.