8 ms·

> we’ll be embracing portions of the Android stack, like the Android Linux kernel and Android frameworks (...) for Chrome OS Uhm, is there any plan to bring so

by a1o 2y ago

> we’ll be embracing portions of the Android stack, like the Android Linux kernel and Android frameworks (...) for Chrome OS Uhm, is there any plan to bring some openess to it so one can actually run python and do ML without using some alternative hack? How is the state of these things nowadays for chrome os? Haven't played with it in ages.

RadiozRadioz 2y ago
Semi-related question: Why did the ML people choose Python and make it so intertwined with ML? It seems like a bad choice because it's slow and bad at integrating with hardware, the actual hardcore ML stuff gets offloaded to C, which creates a weird system with two languages and a weird build step to achieve one thing. If I knew nothing about the ecosystem of available libraries and was asked to pick a language to use for ML, Python would be fairly low down the list. Is it just because it's easy, somebody picked it a while ago, made something influential, and now everybody uses it because everybody uses it?
- buildbot 2y ago
  IMO, it makes more sense to think about Python or another high level language as glue code for all these C/Rust/Cuda/OpenCL kernels. Why would you choose something like Rust to also write the glue code in? Most people will be slowed down quite a bit by the language overhead, and the people writing this code need results last month.
- logicchains 2y ago
  Python is a great language for ML because it allows gluing stuff together quickly. Using a 100% statically compiled language would slow things down, and the typing wouldn't help much because most languages can't properly type check tensor operations, and ones that can (like C++) tend to have awful compile times. The state of the art is tracing the execution and compiling it to a ML-specialised compiler (XLA for Jax, torch.compile for Torch), which is much harder or impossible to do in a compiled language.
- csours 2y ago
  Worse is better. Also, python is just the glue, the work happens in libraries.
- wiseowise 2y ago
  What would you pick?
  - RadiozRadioz 2y ago
    Probably one of the math ones like Julia. Or something closer to hardware, like C++ or Rust
    - hot_gril 2y ago
      Julia doesn't have broad adoption because it's too specific, and C++/Rust are more cumbersome, also Python for data science and ML was popular before Rust or Julia even existed. Python's biggest advantage is that it's good enough for general usage while also being convenient for data science.
      - pjmlp 2y ago
        Julia has more adoption than many wannabe languages. https://info.juliahub.com/case-studies https://info.juliahub.com/case-studies
        hot_gril 2y ago
        Sure, but not more adoption than Python. All the examples look math-focused; would you write a web backend or a batch script in Julia?
        pjmlp 2y ago
        No, that wasn't why it was designed for in first place anyway. Likewise I wouldn't use Python for anything besides teaching programming, or scripting OS tasks and native libraries.
        hot_gril 2y ago
        Well that's the problem. Part of why Python is nice for ML stuff is because it was adopted for several other purposes too. I've never used Julia but have used niche things like Erlang, and there's always more friction that way.
- hot_gril 2y ago
  What else? NodeJS would be sorta viable, but event loop doesn't mix well with this kind of workload, which is a moot point anyway because Python and all the killer libs like Numpy predate NodeJS. And I think Python's package management and parallelism are both awful in ways that could've been avoided, or fixed during that huge breaking Py2->3 change. And I prefer JS syntax. But it still works.
- mlinsey 2y ago
  Because modern ML is a descendant of data science/stats work being done in systems like MATLAB and R. Numpy and scipy were great libraries, and Python was a high-level language that statisticians could use, but also a reasonable enough choice to code a backend for a service in. If the field descended from systems programmers doing high-performance computing, a different language choice would probably have been made, but prior to deep learning becoming popular, compute was not nearly an obvious blocker to improved model performance.
- alaties 2y ago
  I went to college starting 2007 (right when CUDA was released) and recall a few things about python that made it attractive in both the comp sci and math departments at my school. - python integrated well with lesson plans instructors had - python integrated well with the software math departments were using (like MATLAB) - python had a very easy to use interpreter environment that worked well on linux, mac, and windows - python had (for the era) a massive standard library, making it way easier to consume and process common data file formats like XML or CSV without having to install any extensions (installing extensions usually required admin permissions and... very few at school had those for very good reasons) - exposing c/fortran libraries into python was pretty straightforward to do and was relatively stable compared to alternatives. Around 2007, the main competitors in the scripting space had a lot of issues: - tcl - somewhat popular for embedding but didn't really have a standalone interpreter that made it easy to interact with and the syntax didn't really look like C, and really not much of a library to work with - lua - similar to tcl, popular for embedding but didn't have a popular standalone runtime and interpreter. syntax was closer to C though. didn't have much of a library to work with - ruby - had a runtime for experimenting but a less stable API for integrating, plus the language was pretty different from C. the library was large but had a larger reliance on installing extensions than python at the time. - perl - both unstable and had a horrible reputation by this point as a standalone language that outside of some obtuse systems scripting, most people didn't want to touch this with a 12-foot pole. standard library was small and relied a lot on extensions. - php - similarly unstable (at the time... i think 5.3 had just come out) and had a horrible reputation... also not even sure it had a runtime that worked outside of an apache/nginx context... exposing a c library to PHP at this time was pretty straightforward though. relied very heavily on extensions at this point. - javascript - node.js hadn't come out at this point... so the idea of running javascript outside of a browser window wasn't really a thing yet. If one were looking to expose CUDA to programmers who didn't want to work in C during this era, I'd argue that python was just the best option at the time. I believe that led to a first-mover advantage that has held over time.
  - hot_gril 2y ago
    The usability without extensions part is underrated. Whether I'm a beginner in school or a pro trying to hammer out a quick script, I would prefer the thing that doesn't make me jump through hoops to deal with CSV JSON XML etc. Far more important than the language syntax or other things. And the little scripts I write at work tend to grow and then turn into full-fledged services in a shorter period of time than it would've taken to set up the C++ or whatever boilerplate.
- bitwize 2y ago
  It's largely because Python had excellent numeric libraries, like NumPy and SciPy. You could script Fortran linear-algebra kernels to crunch your numbers fast in a high-level language, and that's a killer app in the ML space.
nolist_policy 2y ago
On ChromeOS you have access to a fully featured Debian Linux in a VM with WSL-style seamless passthrough. Though on the graphics side only OpenGL and Vulkan are passed through.

by a1o 2y ago

RadiozRadioz 2y ago

Semi-related question: Why did the ML people choose Python and make it so intertwined with ML? It seems like a bad choice because it's slow and bad at integrating with hardware, the actual hardcore ML stuff gets offloaded to C, which creates a weird system with two languages and a weird build step to achieve one thing. If I knew nothing about the ecosystem of available libraries and was asked to pick a language to use for ML, Python would be fairly low down the list. Is it just because it's easy, somebody picked it a while ago, made something influential, and now everybody uses it because everybody uses it?

buildbot 2y ago
IMO, it makes more sense to think about Python or another high level language as glue code for all these C/Rust/Cuda/OpenCL kernels. Why would you choose something like Rust to also write the glue code in? Most people will be slowed down quite a bit by the language overhead, and the people writing this code need results last month.
logicchains 2y ago
Python is a great language for ML because it allows gluing stuff together quickly. Using a 100% statically compiled language would slow things down, and the typing wouldn't help much because most languages can't properly type check tensor operations, and ones that can (like C++) tend to have awful compile times. The state of the art is tracing the execution and compiling it to a ML-specialised compiler (XLA for Jax, torch.compile for Torch), which is much harder or impossible to do in a compiled language.
csours 2y ago
Worse is better. Also, python is just the glue, the work happens in libraries.
wiseowise 2y ago
What would you pick?
- RadiozRadioz 2y ago
  Probably one of the math ones like Julia. Or something closer to hardware, like C++ or Rust
  - hot_gril 2y ago
    Julia doesn't have broad adoption because it's too specific, and C++/Rust are more cumbersome, also Python for data science and ML was popular before Rust or Julia even existed. Python's biggest advantage is that it's good enough for general usage while also being convenient for data science.
    - pjmlp 2y ago
      Julia has more adoption than many wannabe languages. https://info.juliahub.com/case-studies https://info.juliahub.com/case-studies
      - hot_gril 2y ago
        Sure, but not more adoption than Python. All the examples look math-focused; would you write a web backend or a batch script in Julia?
        pjmlp 2y ago
        No, that wasn't why it was designed for in first place anyway. Likewise I wouldn't use Python for anything besides teaching programming, or scripting OS tasks and native libraries.
        hot_gril 2y ago
        Well that's the problem. Part of why Python is nice for ML stuff is because it was adopted for several other purposes too. I've never used Julia but have used niche things like Erlang, and there's always more friction that way.
hot_gril 2y ago
What else? NodeJS would be sorta viable, but event loop doesn't mix well with this kind of workload, which is a moot point anyway because Python and all the killer libs like Numpy predate NodeJS. And I think Python's package management and parallelism are both awful in ways that could've been avoided, or fixed during that huge breaking Py2->3 change. And I prefer JS syntax. But it still works.
mlinsey 2y ago
Because modern ML is a descendant of data science/stats work being done in systems like MATLAB and R. Numpy and scipy were great libraries, and Python was a high-level language that statisticians could use, but also a reasonable enough choice to code a backend for a service in. If the field descended from systems programmers doing high-performance computing, a different language choice would probably have been made, but prior to deep learning becoming popular, compute was not nearly an obvious blocker to improved model performance.
alaties 2y ago
I went to college starting 2007 (right when CUDA was released) and recall a few things about python that made it attractive in both the comp sci and math departments at my school. - python integrated well with lesson plans instructors had - python integrated well with the software math departments were using (like MATLAB) - python had a very easy to use interpreter environment that worked well on linux, mac, and windows - python had (for the era) a massive standard library, making it way easier to consume and process common data file formats like XML or CSV without having to install any extensions (installing extensions usually required admin permissions and... very few at school had those for very good reasons) - exposing c/fortran libraries into python was pretty straightforward to do and was relatively stable compared to alternatives. Around 2007, the main competitors in the scripting space had a lot of issues: - tcl - somewhat popular for embedding but didn't really have a standalone interpreter that made it easy to interact with and the syntax didn't really look like C, and really not much of a library to work with - lua - similar to tcl, popular for embedding but didn't have a popular standalone runtime and interpreter. syntax was closer to C though. didn't have much of a library to work with - ruby - had a runtime for experimenting but a less stable API for integrating, plus the language was pretty different from C. the library was large but had a larger reliance on installing extensions than python at the time. - perl - both unstable and had a horrible reputation by this point as a standalone language that outside of some obtuse systems scripting, most people didn't want to touch this with a 12-foot pole. standard library was small and relied a lot on extensions. - php - similarly unstable (at the time... i think 5.3 had just come out) and had a horrible reputation... also not even sure it had a runtime that worked outside of an apache/nginx context... exposing a c library to PHP at this time was pretty straightforward though. relied very heavily on extensions at this point. - javascript - node.js hadn't come out at this point... so the idea of running javascript outside of a browser window wasn't really a thing yet. If one were looking to expose CUDA to programmers who didn't want to work in C during this era, I'd argue that python was just the best option at the time. I believe that led to a first-mover advantage that has held over time.
- hot_gril 2y ago
  The usability without extensions part is underrated. Whether I'm a beginner in school or a pro trying to hammer out a quick script, I would prefer the thing that doesn't make me jump through hoops to deal with CSV JSON XML etc. Far more important than the language syntax or other things. And the little scripts I write at work tend to grow and then turn into full-fledged services in a shorter period of time than it would've taken to set up the C++ or whatever boilerplate.
bitwize 2y ago
It's largely because Python had excellent numeric libraries, like NumPy and SciPy. You could script Fortran linear-algebra kernels to crunch your numbers fast in a high-level language, and that's a killer app in the ML space.

nolist_policy 2y ago

On ChromeOS you have access to a fully featured Debian Linux in a VM with WSL-style seamless passthrough. Though on the graphics side only OpenGL and Vulkan are passed through.