$30 off During Our Annual Pro Sale. View Details »
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
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
5.7k
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
280
pandasでのOSS活動事例
sinhrks
0
800
Featured
See All Featured
The Cost Of JavaScript in 2023
addyosmani
55
9.4k
Redefining SEO in the New Era of Traffic Generation
szymonslowik
1
160
個人開発の失敗を避けるイケてる考え方 / tips for indie hackers
panda_program
122
21k
Visualizing Your Data: Incorporating Mongo into Loggly Infrastructure
mongodb
48
9.8k
No one is an island. Learnings from fostering a developers community.
thoeni
21
3.6k
Why Our Code Smells
bkeepers
PRO
340
57k
The Mindset for Success: Future Career Progression
greggifford
PRO
0
190
Getting science done with accelerated Python computing platforms
jacobtomlinson
0
76
Facilitating Awesome Meetings
lara
57
6.7k
Chrome DevTools: State of the Union 2024 - Debugging React & Beyond
addyosmani
9
1k
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
52
5.8k
Bootstrapping a Software Product
garrettdimon
PRO
307
120k
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