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Yes it’s not at 1.0, but my point when I mention to people actually doing deep learning that Swift is coming and has support for native differentiation and stat

Yes it’s not at 1.0, but my point when I mention to people actually doing deep learning that Swift is coming and has support for native differentiation and static typing, the response is underwhelming to the point that I wonder if either of those features provide enough pull to get people to ever drop Python for Swift.

skohan 7y ago
Yeah I think it will be a really hard sell since my perception is that programming is python for a lot of data scientists. Also static typing is something you have to use for a bit before you can understand that the little bit of extra formality is actually saving you a lot of trouble in the medium and long term. Still given the growth of that industry, it's hard to imagine that Python can bear the burden of the increasingly complex work there forever.
- m_ke 7y ago
  Static typing is useless for exploratory data analysis and data wrangling. Once you get to the modelling step all of the data is in typed arrays. Static types start to shine once the project grows and the new type annotation syntax works great for that. I've tried doing machine learning in C++, Java, Scala and JavaScript and all of them are a pain compared to python. Python would be perfect if I could annotate parts of my code and compile them. (Pretty much what's commonly done with cython or numba jit.
  - skohan 7y ago
    > Static typing is useless for exploratory data analysis and data wrangling. In my experience, doing this kind of task in an weak-typed or duck-typed language is already tedious. Function signatures don't tell me enough detail to know exactly what I'm working with, so I end up spending a lot of time digging through documentation or print debugging. Why not let the compiler, or the function signatures do this work for you?
pjmlp 7y ago
Julia is the only sound alternative to Python. If they want to make an Apple only version of Tensorflow, so be it, but don't expect it to take off on Linux and Windows research labs.
- skohan 7y ago
  What’s with the FUD? You can already run S4TF notebooks on Colab, and it’s trivial to spin up S4TF in any environment using Docker.
  - pjmlp 7y ago
    How do you spin S4TF on Windows Containers or any kind of Linux distribution in-house?
    - skohan 7y ago
      Using Docker Edit: or on Ubuntu just download the toolchain.
      - pjmlp 7y ago
        So instead of using the native tooling provided by Julia, ML.NET, PyTorch, the solution is to somehow either emulate Linux on Windows, or being stuck with Ubuntu as distribution.
        skohan 7y ago
        I mean containerization is not the same as emulation. But I would assume a lot of people just want a notebook to do their work in. Does it really matter if it’s running in a container or natively? Most python projects run inside a virtual environment anyway, which is comparable if not more poorly encapsulated. Also the cloud use-case is expanding. Environment basically doesn’t matter at all in that case.
        7y ago

skohan 7y ago

Yeah I think it will be a really hard sell since my perception is that programming is python for a lot of data scientists. Also static typing is something you have to use for a bit before you can understand that the little bit of extra formality is actually saving you a lot of trouble in the medium and long term. Still given the growth of that industry, it's hard to imagine that Python can bear the burden of the increasingly complex work there forever.

m_ke 7y ago
Static typing is useless for exploratory data analysis and data wrangling. Once you get to the modelling step all of the data is in typed arrays. Static types start to shine once the project grows and the new type annotation syntax works great for that. I've tried doing machine learning in C++, Java, Scala and JavaScript and all of them are a pain compared to python. Python would be perfect if I could annotate parts of my code and compile them. (Pretty much what's commonly done with cython or numba jit.
- skohan 7y ago
  > Static typing is useless for exploratory data analysis and data wrangling. In my experience, doing this kind of task in an weak-typed or duck-typed language is already tedious. Function signatures don't tell me enough detail to know exactly what I'm working with, so I end up spending a lot of time digging through documentation or print debugging. Why not let the compiler, or the function signatures do this work for you?

pjmlp 7y ago

Julia is the only sound alternative to Python. If they want to make an Apple only version of Tensorflow, so be it, but don't expect it to take off on Linux and Windows research labs.

skohan 7y ago
What’s with the FUD? You can already run S4TF notebooks on Colab, and it’s trivial to spin up S4TF in any environment using Docker.
- pjmlp 7y ago
  How do you spin S4TF on Windows Containers or any kind of Linux distribution in-house?
  - skohan 7y ago
    Using Docker Edit: or on Ubuntu just download the toolchain.
    - pjmlp 7y ago
      So instead of using the native tooling provided by Julia, ML.NET, PyTorch, the solution is to somehow either emulate Linux on Windows, or being stuck with Ubuntu as distribution.
      - skohan 7y ago
        I mean containerization is not the same as emulation. But I would assume a lot of people just want a notebook to do their work in. Does it really matter if it’s running in a container or natively? Most python projects run inside a virtual environment anyway, which is comparable if not more poorly encapsulated. Also the cloud use-case is expanding. Environment basically doesn’t matter at all in that case.
        7y ago