Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Dask Distributedによる分散機械学習
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
Sinhrks
June 28, 2017
4
1.5k
Dask Distributedによる分散機械学習
@PyData Tokyo #13 Lightning Talk
https://pydatatokyo.connpass.com/event/58954/
Sinhrks
June 28, 2017
Tweet
Share
More Decks by Sinhrks
See All by Sinhrks
daskperiment: Reproducibility for Humans
sinhrks
1
420
PythonとApache Arrow
sinhrks
6
2k
大規模データの機械学習におけるDaskの活用
sinhrks
10
3.3k
機械学習と解釈可能性
sinhrks
7
5.8k
LIME
sinhrks
2
1.4k
データ分析言語R 1年の振り返り
sinhrks
5
2.6k
pandasでのOSS活動事例と最初の一歩
sinhrks
2
19k
Data processing using pandas and Dask
sinhrks
1
280
pandasでのOSS活動事例
sinhrks
0
810
Featured
See All Featured
Joys of Absence: A Defence of Solitary Play
codingconduct
1
280
Data-driven link building: lessons from a $708K investment (BrightonSEO talk)
szymonslowik
1
900
Digital Ethics as a Driver of Design Innovation
axbom
PRO
1
160
How STYLIGHT went responsive
nonsquared
100
6k
XXLCSS - How to scale CSS and keep your sanity
sugarenia
249
1.3M
Measuring Dark Social's Impact On Conversion and Attribution
stephenakadiri
1
110
Color Theory Basics | Prateek | Gurzu
gurzu
0
190
The Language of Interfaces
destraynor
162
26k
Building a Modern Day E-commerce SEO Strategy
aleyda
45
8.6k
What's in a price? How to price your products and services
michaelherold
247
13k
Statistics for Hackers
jakevdp
799
230k
実際に使うSQLの書き方 徹底解説 / pgcon21j-tutorial
soudai
PRO
196
71k
Transcript
Dask DistributedʹΑΔ ࢄػցֶश Masaaki Horikoshi @ ARISE analytics
ࣗݾհ • OSS׆ಈ: • GitHub: https://github.com/sinhrks
Daskͱ • ॊೈͳฒྻɾOut of CoreॲཧϑϨʔϜϫʔΫ • NumPy, pandasޓ(αϒηοτ)ͷσʔλߏΛఏڙ • λεΫಈతͳܭࢉάϥϑͱͯ͠දݱ͞Εɺεέδϡʔ
ϥʹΑͬͯฒྻ࣮ߦ • DaskΛར༻͢Δύοέʔδ(Ұ෦): Airflow
Dask DataFrame • ෳͷpandas DataFramesʹΑΓߏ • ॎʹׂ͞ΕͨDataFrame͝ͱʹॲཧΛฒྻԽ QBOEBT%BUB'SBNF %BTL%BUB'SBNF QBSUJUJPO
EJWJTJPO EJWJTJPO
import pandas as pd df = pd.DataFrame({'X': np.arange(10), 'Y': np.arange(10,
20), 'Z': np.arange(20, 30)}, index=list('abcdefghij')) df import dask.dataframe as dd ddf = dd.from_pandas(df, 2) ddf ߦྻͷ QBOEBT%BUB'SBNFΛ࡞ Dask DataFrame QBSUJUJPO QBSUJUJPO EJWJTJPO EJWJTJPO EJWJTJPO
Blocked Algorithm (߹ܭ) ddf.sum().compute() 4VN 4VN $PODBU 4VN ߹ܭ શମ
࿈݁ ߹ܭ QBSUJUJPO͝ͱ
Dask Distributed • εέδϡʔϥͰͷܭࢉ࣮ߦΛෳϊʔυͰࢄͰ͖Δ • ϨΠςϯγ: λεΫຖͷΦʔόʔϔου1msఔ • WorkerؒͰͷσʔλڞ༗: σʔλసૹWorkerؒͰ࣮ࢪ
• ෳࡶͳεέδϡʔϦϯά: ҙͷܭࢉάϥϑΛ࣮ߦՄ • ہॴੑ: WorkerؒͷσʔλసૹΛͳΔ͘ߦΘͳ͍ %JTUSJCVUFE 8PSLFS %JTUSJCVUFE 8PSLFS %JTUSJCVUFE 4DIFEVMFS %JTUSJCVUFE $MJFOU
Scikit-Learnͷฒྻॲཧ • “n_jobs” ҾͰฒྻ࣮ߦΛࢦఆ • ෦తʹjoblibΛར༻ • Scikit-Learnίϛολத৺ʹ։ൃ • ϊʔυฒྻ
(threading, multiprocessing) from sklearn.model_selection import GridSearchCV grid = GridSearchCV(pipe, cv=3, n_jobs=12, param_grid=param_grid)
Distributed joblib • ϓϥΨϒϧAPI (0.10.0-) • with ϒϩοΫͰ joblib.Parallel ͷطఆόοΫΤϯυΛมߋՄ
• ҙ • scikit-learnʹόϯυϧ͞Ε͍ͯΔjoblibΛ͏ (sklearn.externals.joblib) • ࢄͰ͖ͳ͍߹͋Δ • backendͱͯ͠threading / multiprocessing͕໌ࣔ͞Ε͍ͯΔͷ import distributed.joblib from sklearn.externals.joblib import parallel_backend with parallel_backend('dask.distributed', scheduler_host=‘scheduler-addr:8786’): grid.fit(digits.data, digits.target)
dask-searchcv • Scikit-LearnͷϋΠύʔύϥϝʔλαʔνΛ Dask ޓʹͨ͠ͷ: • GridSearchCVͱRandomizedSearchCVΛαϙʔτ • APIScikit-Learnͱڞ௨ •
Dask Array DataFrameΛೖྗͱͯͤ͠Δ • ಉҰɺಉύϥϝʔλͷֶशثͷ܁Γฦ࣮͠ߦΛආ͚Δ • PipelineॲཧͰ༗༻ ※աڈʹ dklearn ͱͯ͠ެ։͞Ε͍ͯͨύοέʔδͷҰ෦