大規模データの機械学習におけるDaskの活用

Transcript

1. େن໛σʔλͷ ػցֶशʹ͓͚ΔDaskͷ׆༻ Masaaki Horikoshi @ ARISE analytics

2. ࣗݾ঺հ • OSS׆ಈͳͲ: • GitHub: https://github.com/sinhrks

3. ίϯςϯπ • Daskͱ͸ • ػցֶशʹ͓͚ΔDaskͷ׆༻ • Dask ML • Distributed

4. Python for Big Data • PythonͰେن໛ͳσʔλΛॲཧ͢Δͱ… • ܭࢉ͕(ݪଇ)୯ҰͷεϨουͰߦΘΕΔͨΊɺ ॲཧ଎౓͕஗͍ •

   σʔλ͕෺ཧతͳϝϞϦʹ͓͞·Βͳ͍

5. ฒྻॲཧͱOut-of-coreॲཧ ฒྻॲཧ 0VUPGDPSFॲཧ • ฒྻॲཧ: ෳ਺ͷλεΫΛฒྻͰॲཧ͢Δ • Out-of-coreॲཧ: ϝϞϦʹ৐Βͳ͍σʔλΛஞ࣍ॲཧ͢Δ •

   ഉଞͰ͸ͳ͍ (Out-of-coreॲཧΛฒྻͰߦ͏͜ͱ΋͋Δ)

6. Daskͱ͸ • ॊೈͳฒྻɾOut-of-coreॲཧύοέʔδ • σʔλॲཧɺ਺஋ܭࢉΛओ໨త • NumPy΍pandasͷαϒηοτͱͳΔσʔλ ߏ଄Λఏڙ • ॲཧΛܭࢉάϥϑͱͯ͠දݱ͠ɺಈతʹεέ

   δϡʔϦϯά࣮ͯ͠ߦ

7. (Incomplete) List of OSS uses Dask • (TFLearn) Deep learning

   library featuring a higher-level API for TensorFlow. • (Distributed Scheduler) A platform to author, schedule and monitor workflows. • Image Processing SciKit. • N-D labeled arrays and datasets in Python. • Executes end-to-end data science and analytics pipelines entirely on GPUs. Airflow

8. Daskͷσʔλߏ଄(API) %BTL
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9. Dask Array • ෳ਺ͷNumPy nd-arrayʹΑͬͯߏ੒ • ಺෦తʹ͸ɺ࣠ํ޲ʹԊͬͯChunkʹ෼ׂ /VN1Z
   OEBSSBZ %BTL
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   DIVOLTJ[F

10. array([[ 1., 1., 1., 1., 1., 1., 1., 1., 1.,

    1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]) Dask Array import numpy as np x = np.ones((10, 10)) x import dask.array as da dx = da.ones((10, 10), chunks=(5, 5)) dx dask.array<ones, shape=(10, 10), dtype=float64, chunksize=(5, 5)> 
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11. Dask Array import dask.array as da dx = da.ones((10, 10),

    chunks=(5, 5)) dx dask.array<ones, shape=(10, 10), dtype=float64, chunksize=(5, 5)> dx.visualize() ಺෦ͷܭࢉάϥϑΛදࣔ
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    Λ࡞੒ ಺෦͸
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    ͷͭͷDIVOLʹ෼ׂ
    

12. 
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    Λ࡞੒ Dask Array dy = dx.sum(axis=0) dy dask.array<sum-aggregate,

    shape=(10,), dtype=float64, chunksize=(5,)> BYJTʹԊͬͯ஋Λ߹ܭ dy.visualize() dy.compute() array([10., 10., 10., 10., 10., 10., 10., 10., 10., 10.]) 4VN 4VN

13. Dask Array dy2 = dx.sum() dy2 dask.array<sum-aggregate, shape=(), dtype=float64, chunksize=()>

    શͯͷ஋Λ߹ܭ dy2.visualize() dy2.compute() 100.0

14. Dask Array • Dask Array ͸೚ҙͷ shape ͱ chunk sizeΛ΋ͭ

    • chunkؒͷॲཧ͕ൃੜ͢Δ৔߹ɺchunk size ͸ Ұகͤͨ͞ํ͕ྑ͍ da.ones((30, 20, 1, 15), chunks=(3, 7, 1, 2)) dask.array<ones, shape=(30, 20, 1, 15), dtype=float64, chunksize=(3, 7, 1, 2)>

15. Dask DataFrame QBSUJUJPO EJWJTJPO EJWJTJPO • ෳ਺ͷpandas DataFrameʹΑͬͯߏ੒ • ಺෦తʹ͸ɺindex

    (ߦϥϕϧ)ʹԊͬͯPartitionʹ෼ׂ QBOEBT
    %BUB'SBNF %BTL
    %BUB'SBNF

16. 
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    Λ࡞੒ Dask DataFrame import dask.dataframe as dd ddf =

    dd.from_pandas(df, 2) ddf QBSUJUJPO QBSUJUJPO EJWJTJPO EJWJTJPO EJWJTJPO

17. Blocked Algorithm (Mean) ddf.mean().visualize() 4VN
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18. ܭࢉάϥϑͷ࣮ߦΠϝʔδ • ܭࢉάϥϑͷґଘؔ܎ͷղੳ • ࠷ऴ݁ՌʹෆཁͳλεΫͷ࡟আ • ಉҰλεΫͷϚʔδ • ܭࢉॱংͷ੩తɾಈతͳղੳ •

    ܭࢉάϥϑͷ࠷దԽ • ಛఆॲཧͷΠϯϥΠϯԽ • ࿈ଓ͢ΔλεΫΛಉҰͷϫʔΧʔʹׂΓ౰ͯ

19. Dask Internals • ͋ΒΏΔDaskͷॲཧ͸ܭࢉάϥϑͱͯ͠දݱ͞ ΕΔ • DaskͰ͸ઢܗ୅਺ͳͲͷෳࡶͳΞϧΰϦζϜ ΋࣮૷ (dask.array.linalg) •

    Ϣʔβࣗ਎ͷσʔλߏ଄΍ؔ਺΋ܭࢉάϥϑ Ͱ࣮૷Մೳ (dask.delayed)

20. Linear Algebra 'VODUJPO %FTDSJQUJPO MJOBMHDIPMFTLZ 3FUVSOT
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21. Example: LU decomposition • LU෼ղ • LU෼ղ͸νϟϯΫ(ϒϩοΫ)୯ҐͰͷܭࢉʹ෼ׂ Ͱ͖Δ "ɹɹɹ
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22. Blocked LU Decomposition • Diagonal Block • Row-direction(i < j)

    • Columns direction (i < j) ∴ ∴ * LU: Function to solve LU decomposition * Solve: Function to solve equation

23. Blocked LU Decomposition arr = da.random.random((9, 9), chunks=(3, 3)) arr

    dask.array<random_sample, shape=(9, 9), dtype=float64, chunksize=(3, 3)> from dask import compute t, l, u = da.linalg.lu(arr) t, l, u = compute(t, l, u) ͭͷܭࢉάϥϑΛ
    Ϛʔδ࣮ͯ͠ߦ

24. Blocked LU Decomposition from dask import visualize visualize(t, l, u)

25. DaskʹΑΔॲཧͷશମ૾ ஞ࣍ॲཧ /VN1Z QBOEBT TDJLJUMFBSO %BTL %BTL
    %JTUSJCVUFE ฒྻॲཧ
    ϊʔυ಺

    0VUPGDPSF
    ॲཧ ϊʔυ಺ ෼ࢄॲཧ
    ϊʔυؒ %BTL
    .- ߦྻܭࢉ ςʔϒϧ
    σʔλॲཧ ػցֶश

26. scikit-learnͷฒྻॲཧ • “n_jobs” Ҿ਺Ͱฒྻ࣮ߦ਺Λࢦఆ • ಺෦తʹ͸joblibΛར༻ • scikit-learnίϛολத৺ʹ։ൃ • ϊʔυ಺ฒྻ

    (threading, multiprocessing) • Out-of-coreॲཧ΍ϊʔυؒ෼ࢄॲཧ͸Ͱ͖ͳ͍ from sklearn.model_selection import GridSearchCV grid = GridSearchCV(pipe, cv=3, n_jobs=12, param_grid=param_grid)

27. DaskʹΑΔॲཧͷશମ૾ ஞ࣍ॲཧ /VN1Z QBOEBT TDJLJUMFBSO %BTL %BTL
    %JTUSJCVUFE ฒྻॲཧ
    ϊʔυ಺

    0VUPGDPSF
    ॲཧ ϊʔυ಺ ෼ࢄॲཧ
    ϊʔυؒ %BTL
    .- ߦྻܭࢉ ςʔϒϧ
    σʔλॲཧ ػցֶश

28. ຊ౰ʹશͯͷσʔλ͕ඞཁ͔ʁ • αϯϓϦϯάͰे෼Ͱ͸ʁ IUUQTDJLJUMFBSOPSHTUBCMFBVUP@FYBNQMFTNPEFM@TFMFDUJPOQMPU@MFBSOJOH@DVSWFIUNM

29. Dask ML • ػցֶशͰDaskΛ׆༻͢ΔͨΊͷύοέʔδ • Daskͷσʔλߏ଄ʹରͯ͠ػցֶशΛߦ͏ • ػցֶशΞϧΰϦζϜΛDaskΛ༻͍ͯฒྻɾ Out-of-coreԽ͢Δ

30. Dask ML • ػցֶशͷͨΊͷαϒύοέʔδΛఏڙ • Preprocessing • Model Selection •

    Cross validation • Hyper parameter search • GLM • Clustering • Incremental • ParallelPostFit • XGBoost • TensorFlow %BTLʹରԠͨ͠
    TDJLJUMFBSOޓ׵ͷ
    ֶशثΛఏڙ TDJLJUMFBSOΛϥοϓ͠
    ฒྻɾ0VUPGDPSFԽ TDJLJUMFBSOҎ֎ͷ
    ύοέʔδରԠ

31. • scikit-learnޓ׵ͷֶशثͰDaskͷσʔλߏ଄Λ ѻ͑Δ Dask ML TDJLJU MFBSO %BTL
    .- /VN1Z
    

    OEBSSBZ %BTL
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    ॲཧΛ࣮ߦ

32. k-means IUUQTFOXJLJQFEJBPSHXJLJ,NFBOT@DMVTUFSJOH

33. k-means (Dask ML) ʜ $IVOL ʜ $IVOL ʜ $IVOL 4BNQMJOH

    σʔληοτ͔Βη ϯτϩΠυͷॳظ஋ Λܾఆ ηϯτϩΠυ͔Βͷ ڑ཭Λܭࢉ͠ɺΫϥ ελʹ෼ྨ
    $IVOL͝ͱʹ࣮ߦ Ϋϥελ͝ͱʹϨίʔ υΛू໿͠ɺηϯτ ϩΠυΛߋ৽
    $IVOL͝ͱʹ࣮ߦˠ ू໿ 4VN 4VN
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34. Incremental • partial_fitΛॱʹద༻͢Δ 
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    "SSBZ *ODSFNFOUBM QBSUJBM@pU $IVOL

    $IVOL ʜ ʜ QBSUJBM@pUΛஞ࣮࣍ߦ
    ฒྻॲཧ͞Εͳ͍

35. Incremental • IncrementalͰֶशثΛϥοϓ • ෼ྨ໰୊ͷ৔߹ɺΫϥεΛclassesҾ਺Ͱ౉͢ • ෳ਺ΤϙοΫͰֶश͢Δ৔߹͸ Incremental.partial_fit • for

    _ in range(10): inc.partial_fit(X_train, y_train, classes=classes) print('Score:', inc.score(X_test, y_test)) from sklearn.linear_model import SGDClassifier from dask_ml.wrappers import Inclemental clf = SGDClassifier(loss='log', penalty='l2', tol=1e-3)) inc = Incremental(clf, scoring='accuracy') inc.fit(X_train, y_train, classes=classes)

36. 
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    Λ࡞੒ ParallelPostFit • ֶशࡁΈEstimatorͷtransform΍predictΛฒྻͰ ద༻͢Δ QSFEJDU %BTL
    "SSBZ 1BSBMMFM1PTU'JU QSFEJDU

    $IVOL QSFEJDU $IVOL ʜ ʜ

37. • ParallelPostFitͰֶशثΛϥοϓ • fit࣌͸Կ΋ߦΘͳ͍ (scikit-learnͷॲཧͱಉҰ) • ΑΓେ͖ͳσʔλʹରͯ͠ predict ΛฒྻͰߦ͏ ParallelPostFit

    y_pred = clf.predict(X_large) y_pred from sklearn.linear_model import LogisticRegressionCV from dask_ml.wrappers import ParallelPostFit clf = ParallelPostFit(LogisticRegressionCV(cv=3)) clf.fit(X_train, y_train)

38. DaskʹΑΔॲཧͷશମ૾ ஞ࣍ॲཧ /VN1Z QBOEBT TDJLJUMFBSO %BTL %BTL
    %JTUSJCVUFE ฒྻॲཧ
    ϊʔυ಺

    0VUPGDPSF
    ॲཧ ϊʔυ಺ ෼ࢄॲཧ
    ϊʔυؒ %BTL
    .- ߦྻܭࢉ ςʔϒϧ
    σʔλॲཧ ػցֶश

39. Dask Distributed • Daskຊମ͸ҎԼ2छྨͷεέδϡʔϥΛαϙʔ τ: • threading • multiprocessing •

    Dask Distributedύοέʔδ͸ϊʔυؒ෼ࢄΛߦ ͏εέδϡʔϥΛఏڙ

40. Dask Distributed • εέδϡʔϥͰͷܭࢉ࣮ߦΛෳ਺ϊʔυͰ෼ࢄͰ͖Δ • ௿ϨΠςϯγ: λεΫຖͷΦʔόʔϔου͸1msఔ౓ • WorkerؒͰͷσʔλڞ༗: σʔλసૹ͸WorkerؒͰ௚઀࣮ࢪ

    • ෳࡶͳεέδϡʔϦϯά: ೚ҙͷܭࢉάϥϑΛ࣮ߦՄ • ہॴੑ: WorkerؒͷσʔλసૹΛͳΔ΂͘ߦΘͳ͍ %JTUSJCVUFE
    8PSLFS %JTUSJCVUFE
    8PSLFS %JTUSJCVUFE
    4DIFEVMFS %JTUSJCVUFE
    $MJFOU

41. Distributed joblib • ϓϥΨϒϧAPI • with ϒϩοΫͰ joblib.Parallel ͷطఆόοΫΤϯυΛมߋՄ •

    ஫ҙ఺ • scikit-learnʹόϯυϧ͞Ε͍ͯΔjoblibΛ࢖͏ (sklearn.externals.joblib) • ෼ࢄͰ͖ͳ͍৔߹΋͋Δ • backendͱͯ͠threading / multiprocessing͕໌ࣔ͞Ε͍ͯΔ΋ͷ 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)

42. ·ͱΊ • Daskͱ͸ • NumPy΍ pandas ޓ׵ͷσʔλߏ଄Λఏڙ • ػցֶशʹ͓͚ΔDaskͷ׆༻ •

    Dask ML: ػցֶशͰDaskͷσʔλߏ଄Λѻ͑ Δ • Distributed: scikit-learnͷॲཧΛϊʔυؒͰ෼ࢄ ॲཧͰ͖Δ