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
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
410
PythonとApache Arrow
sinhrks
6
1.9k
大規模データの機械学習におけるDaskの活用
sinhrks
10
3.2k
機械学習と解釈可能性
sinhrks
7
5.7k
LIME
sinhrks
2
1.4k
データ分析言語R 1年の振り返り
sinhrks
5
2.5k
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
270
pandasでのOSS活動事例
sinhrks
0
790
Featured
See All Featured
Producing Creativity
orderedlist
PRO
347
40k
Typedesign – Prime Four
hannesfritz
42
2.8k
Optimising Largest Contentful Paint
csswizardry
37
3.5k
Building Applications with DynamoDB
mza
96
6.7k
Evolution of real-time – Irina Nazarova, EuRuKo, 2024
irinanazarova
9
990
Fashionably flexible responsive web design (full day workshop)
malarkey
407
66k
StorybookのUI Testing Handbookを読んだ
zakiyama
31
6.2k
Agile that works and the tools we love
rasmusluckow
331
21k
Into the Great Unknown - MozCon
thekraken
40
2.1k
Bootstrapping a Software Product
garrettdimon
PRO
307
110k
Code Review Best Practice
trishagee
72
19k
XXLCSS - How to scale CSS and keep your sanity
sugarenia
249
1.3M
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