Masahiro Tanaka ▶ Research Fellow at Center for Computational Sciences, University of Tsukuba ▶ RubyKaigi Speaker ○ RubyKaigi 2010 in Tsukuba • Topic: NArray • http://rubykaigi.org/2010/ja/events/83 ○ RubyKaigi 2016 in Kyoto • Topic: Pwrake • http://rubykaigi.org/2016/presentations/masa16tanaka.html ○ RubyKaigi 2017 in Hiroshima • Topic: Numo::NArray Nov 19, 2017 [email protected] 2
Popular Programming Language for Data Science ▶ R ○ Language for Statistical Computing ▶ Python ○ Versatile Language + Library for Scientific Computing ○ Supported by popular Deep Learning frameworks. Nov 19, 2017 [email protected] 4
Two approaches to do Data Science with Ruby ▶ Calls Existing Framework from Ruby ○ PyCall ▶ Build Libraries in Ruby ○ Data Science Libraries require Science Libraries ○ Science Libraries require NumPy-like library ○ Is there any library like NumPy in Ruby? Nov 19, 2017 [email protected] 9
Notation and Speed of Element-wise Operation a = [1,2,3,4,5] b = [10,20,30,40,50] p c = a.zip(b).map{|x,y| x + y} #=> [11, 22, 33, 44, 55] require "benchmark" a = (1..10000).to_a b = (10..100000).step(10).to_a Benchmark.bm do |r| r.report do 1000.times{ c = a.zip(b).map{|x,y| x+y} } end end # user system total real # 1.910000 0.010000 1.920000 ( 1.912284) require "numo/narray" a = Numo::NArray[1,2,3,4,5] b = Numo::NArray[10,20,30,40,50] p c = a + b #=> Numo::Int32#shape=[5] #[11, 22, 33, 44, 55] require "benchmark" a = Numo::Int32.new(10000).seq(1) b = Numo::Int32.new(10000).seq(10,10) Benchmark.bm do |r| r.report do 100000.times{ c = a + b } end end # user system total real # 0.750000 0.080000 0.830000 ( 0.830775) Nov 19, 2017 [email protected] 19 230 times faster Simple notation
NMatrix ▶ Main product of SciRuby. ▶ Supports ○ Multi-dimensional Numerical Array ○ Dense Matrix operation (wrapper to BLAS/LAPACK) ○ Sparse Matrix ▶ IMO, NMatrix cannot be an alternative to NumPy. Nov 19, 2017 [email protected] 26
Numo::Linalg ▶ Module for Linear Algebra ▶ Wrapper to BLAS/LAPACK ▶ Ruby Association Grant 2016 ○ Supported Kishimoto-san to increase coverage. ▶ Modules: (Similar to scipy.linalg) ○ Numo::Linalg::Blas, Numo::Linalg::Lapack • direct wrapper to BLAS/LAPACK ○ Numo::Linalg • Matrix product, Linear solver, Decompositions, Eigen problems, etc. Nov 19, 2017 [email protected] 30
Numo::Linalg coverage ▶ Most of BLAS ▶ LAPACK ○ covered: Non-Symmetric and Symmetric functions ○ not covered: Triangular and Banded functions Nov 19, 2017 [email protected] 31
Numo::GSL ▶ Wrapper to GSL (GNU Scientific Library) ○ GSL is a collection of numerical routines (> 1000 functions in total). ○ Corresponding to SciPy library ○ Operates efficiently on Numo::NArray ○ Wrapper code is automatically generated from texinfo. ▶ Why not using Ruby/GSL ? ○ Wrapper code is written by hand -- hard to maintain. Nov 19, 2017 [email protected] 33
Numo::Gnuplot ▶ One of many wrappers to Gnuplot ▶ Features: ○ Simple interface similar to Gnuplot command line ○ No class for data handling (use Array or NArray) ▶ Example: require "numo/gnuplot" x = (0..100).map{|i| i*0.1} y = x.map{|i| Math.sin(i)} Numo.gnuplot do set title:"X-Y data plot" plot x,y, w:'lines', t:'sin(x)' end Nov 19, 2017 [email protected] 36 set title "X-Y data plot" plot '-' w lines t "sin(x)" 0 0 ... e converted Gnuplot script
Numo::Gnuplot example require "numo/narray" require "numo/gnuplot" x = Numo::DFloat.new(1,21).seq-10 y = Numo::DFloat.new(21,1).seq-10 f = Numo::NMath.sin(-Numo::NMath.sqrt((x+5)**2+(y-7)**2)*0.5) Numo.gnuplot do set term:"pngcairo" set output:"hidden.png" set isosamples:[25,25] set :xyplane, at:0 unset :key set :palette, rgbformulae:[31,-11,32] set :style, fill_solid:0.5 set cbrange:-1..1 set :hidden3d, :front splot [f, with:"pm3d"], [x**2-y**2, with:"lines", lc_rgb:"black"] end Nov 19, 2017 [email protected] 37 https://github.com/ruby-numo/gnuplot-demo
Summary of Progress ▶ Numo::NArray ○ Numpy-like array is complete. ▶ Numo::Linalg ○ BLAS routines are complete. ○ Most of non-symmetric and symmetric routines are complete. ▶ Numo::GSL ○ 15 modules are complete. ▶ Numo::FFTW ○ Complex DFT is complete. ▶ Numo::Gnuplot ○ Almost complete. Nov 19, 2017 [email protected] 38
Deep Learning From Scratch ▶ Published from O'Reilly Japan ○ Implement DL with NumPy ○ https://www.oreilly.co.jp/books/9784873117584/ ▶ Blog post by Akanuma-san ○ Translation into Ruby/Numo ○ Elapsed time: • Ruby: 281 sec • Python: 39 sec Nov 19, 2017 [email protected] 44 http://blog.akanumahiroaki.com/entry/2017/04/15/160000
Measurement on My Laptop ruby train_neuralnet.rb # 372 sec python3.6 train_neuralnet.py # 32 sec ▶ Depends on the performance of dot method (matrix product) ○ NArray: Naïve implementation with single core ○ NumPy: ATLAS or OpenBLAS with 4 cores ▶ Use OpenBLAS with 4 cores by loading Linalg: ruby -r numo/linalg/use/openblas train_neuralnet.rb # 54 sec ○ NArray has room for more speed-up. Nov 19, 2017 [email protected] 45
Convert Deep Learning code from Python to Ruby ▶ Talk in RejectKaigi 2017 by Naitoh-san ▶ Purpose: – Translate Chainer code into Ruby ▶ py2rb.py – Converter from Python to Ruby – Replace NumPy with Numo::NArray Nov 19, 2017 [email protected] 46 Another way to "get on the shoulder of the giant" for machine learning with Ruby https://www.slideshare.net/naitoh1/reject-kaigi2017-naitoh
Incompatibility between NumPy and NArray Nov 19, 2017 [email protected] 47 NumPy Numo::NArray Why if condition: if condition != 0 zero is true in Ruby a[:] a[0..-1] or a[true] Range feature a[b:e] a[b..e-1] Range feature b = a.view() b += 1 b = a.view b.inplace + 1 b += 1 is syntax sugar for b = b + 1 a[i] (a.ndim >= 2) a[i,false] NArray feature a[[0,1],[1,0]] a.at([0,1],[1,0]) NArray feature
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