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I know it's also gained a following at MIT's Broad Lab amongst genomics folks. And that seems to be Julia's sweet spot: easy parallelism for scientists who can'

by indescions_2017 9y ago

I know it's also gained a following at MIT's Broad Lab amongst genomics folks. And that seems to be Julia's sweet spot: easy parallelism for scientists who can't spare the time futzing with HPC internals. Congrats to the Julia language team and to the great scientific discoveries that will be enabled with this investment! http://julia.mit.edu/ http://julia.mit.edu/

Q6T46nT668w6i3m 9y ago
Interesting. I'm a member of a Broad Institute lab that recently evaluated Julia. We reached the conclusion that Julia, while promising, lacked the flexibility and performance of our existing tools. I suppose this is a problem that investment could address!
- mac01021 9y ago
  What are the existing tools that it doesn't match up to?
  - Q6T46nT668w6i3m 9y ago
    Python’s scientific ecosystem (i.e. Keras, matplotlib, NumPy, pandas, scikit-image, scikit-learn, SciPy, and TensorFlow, et al.)
    - thebooktocome 9y ago
      * matplotlib is wrapped by PyPlot.jl. * TensorFlow is wrapped by TensorFlow.jl, improves on Python API (https://github.com/malmaud/TensorFlow.jl/blob/master/docs/src/why_julia.md https://github.com/malmaud/TensorFlow.jl/blob/master/docs/sr...) * Pandas is wrapped by Pandas.jl, interfaces with Julia's DataFrame, generally excellent. * Images.jl is a 60%-to-70% replacement for scikit-image. * NumPy and SciPy are mostly covered by StatsBase and Base, can't think of anything off-hand that's missing. Julia's still immature in the machine learning domain, but a lot of new work is being done. One interesting somewhat-new package is Madeleine Udell's LowRankModels.jl, which has a particularly Julian approach to low rank fitting.
    - ihnorton 9y ago
      This is totally fair. Even the Julia creators regularly mention "stay with existing tools for now, if you are happy." Julia is an increasingly attractive option for greenfield, high-performance development, but for folks who aren't doing a lot of that, a more stable language and mature ecosystem is obviously important. Julia and many libraries can still require some elbow-grease, and the maturity and coverage varies between fields. However, this is rapidly changing as folks do new research in Julia and build up everything else they need along the way. The current situation is very similar to SciPy in the mid-2000s, though with perhaps a somewhat different mix of backgrounds. Here are a few examples: https://github.com/JuliaOpt https://github.com/JuliaOpt https://github.com/jeff-regier/Celeste.jl https://github.com/jeff-regier/Celeste.jl https://github.com/dlfivefifty https://github.com/dlfivefifty https://github.com/JuliaInv https://github.com/JuliaInv http://docs.juliadiffeq.org/latest/ http://docs.juliadiffeq.org/latest/ https://github.com/denizyuret/Knet.jl https://github.com/denizyuret/Knet.jl http://nemocas.org/ http://nemocas.org/ https://juliaplots.github.io/ https://juliaplots.github.io/ So, hopefully you'll have reason to take another look in a few years. The most important thing, to me, is that all of these communities want more open, reproducible, and accessible science. There is plenty of room for growth, because users of closed-source software still probably outnumber py/r/jl by 5:1 or more.
- KenoFischer 9y ago
  I'd encourage you to reach out to the Julia lab at MIT. They're generally very happy to help spread the use of Julia at MIT.

Q6T46nT668w6i3m 9y ago

Interesting. I'm a member of a Broad Institute lab that recently evaluated Julia. We reached the conclusion that Julia, while promising, lacked the flexibility and performance of our existing tools. I suppose this is a problem that investment could address!

mac01021 9y ago
What are the existing tools that it doesn't match up to?
- Q6T46nT668w6i3m 9y ago
  Python’s scientific ecosystem (i.e. Keras, matplotlib, NumPy, pandas, scikit-image, scikit-learn, SciPy, and TensorFlow, et al.)
  - thebooktocome 9y ago
    * matplotlib is wrapped by PyPlot.jl. * TensorFlow is wrapped by TensorFlow.jl, improves on Python API (https://github.com/malmaud/TensorFlow.jl/blob/master/docs/src/why_julia.md https://github.com/malmaud/TensorFlow.jl/blob/master/docs/sr...) * Pandas is wrapped by Pandas.jl, interfaces with Julia's DataFrame, generally excellent. * Images.jl is a 60%-to-70% replacement for scikit-image. * NumPy and SciPy are mostly covered by StatsBase and Base, can't think of anything off-hand that's missing. Julia's still immature in the machine learning domain, but a lot of new work is being done. One interesting somewhat-new package is Madeleine Udell's LowRankModels.jl, which has a particularly Julian approach to low rank fitting.
  - ihnorton 9y ago
    This is totally fair. Even the Julia creators regularly mention "stay with existing tools for now, if you are happy." Julia is an increasingly attractive option for greenfield, high-performance development, but for folks who aren't doing a lot of that, a more stable language and mature ecosystem is obviously important. Julia and many libraries can still require some elbow-grease, and the maturity and coverage varies between fields. However, this is rapidly changing as folks do new research in Julia and build up everything else they need along the way. The current situation is very similar to SciPy in the mid-2000s, though with perhaps a somewhat different mix of backgrounds. Here are a few examples: https://github.com/JuliaOpt https://github.com/JuliaOpt https://github.com/jeff-regier/Celeste.jl https://github.com/jeff-regier/Celeste.jl https://github.com/dlfivefifty https://github.com/dlfivefifty https://github.com/JuliaInv https://github.com/JuliaInv http://docs.juliadiffeq.org/latest/ http://docs.juliadiffeq.org/latest/ https://github.com/denizyuret/Knet.jl https://github.com/denizyuret/Knet.jl http://nemocas.org/ http://nemocas.org/ https://juliaplots.github.io/ https://juliaplots.github.io/ So, hopefully you'll have reason to take another look in a few years. The most important thing, to me, is that all of these communities want more open, reproducible, and accessible science. There is plenty of room for growth, because users of closed-source software still probably outnumber py/r/jl by 5:1 or more.
KenoFischer 9y ago
I'd encourage you to reach out to the Julia lab at MIT. They're generally very happy to help spread the use of Julia at MIT.