Meta tags:

Headings (most frequently used words):

worker, scikit, learn, for, scale, small, data, problems, distributed, info, scheduler, dask, examples, documentation, contents, training, parallel, prediction, client, cluster, localcluster, workers, create, pipeline, define, grid, parameter, search,

Text of the page (most frequently used words):
dask (42), for (24), the (22), data (18), import (16), with (16), 127 (16), from (15), #distributed (13), #worker (13), you (12), scikit (12), learn (12), examples (12), this (11), datasets (10), small (10), machine (10), pipeline (10), categories (10), large (9), cluster (9), training (9), estimator (9), fit (9), memory (9), tcp (9), and (8), parallelpostfit (8), scale (8), sklearn (8), dataframes (8), http (8), dashboard (8), status (8), total (8), client (7), your (7), use (7), train (7), that (7), learning (7), predict (6), grid_search (6), arrays (6), using (6), parameters (6), gib (6), threads (6), numpy (5), like (5), when (5), work (5), parallel (5), gridsearchcv (5), svc (5), parameter (5), prediction (5), grid (5), search (5), print (5), comm (5), 17970 (4), tasks (4), shape (4), array (4), will (4), dask_ml (4), wrappers (4), time (4), dataset (4), larger (4), joblib (4), parallelism (4), define (4), text (4), estimators (4), home (4), runner (4), space (4), local (4), directory (4), nanny (4), problems (4), user (4), score (3), ensemble (3), predicted (3), type (3), chunks (3), 1797 (3), kib (3), big_x (3), than (3), would (3), param_grid (3), true (3), most (3), need (3), meta (3), single (3), model (3), over (3), all (3), create (3), models (3), many (3), can (3), workers (3), 8787 (3), scheduler (3), survey (3), results (3), workloads (3), developers (2), blockwise (2), methods (2), ndarray (2), count (2), bytes (2), chunk (2), times (2), load (2), random_state (2), estimator__c (2), load_digits (2), simple (2), just (2), case (2), but (2), batch (2), handle (2), pandas (2), provides (2), during (2), probably (2), each (2), some (2), target (2), parallel_backend (2), backend (2), processes (2), machines (2), write (2), false (2), n_jobs (2), sgdclassifier (2), tfidftransformer (2), hashingvectorizer (2), loading (2), newsgroups (2), alt (2), atheism (2), talk (2), religion (2), misc (2), documents (2), len (2), fetch_20newsgroups (2), feature_extraction (2), pprint (2), may (2), hyper (2), method (2), too (2), lets (2), cores (2), info (2), localcluster (2), youtubevideo (2), demonstrates (2), example (2), notebook (2), live (2), contents (2), toggle (2), navigation (2), web (2), custom (2), documentation (2), copyright, 2018, next, previous, point, could, written, disk, aggregated, before, returning, int64, 140, operations, return, rather, compute, done, out, core, predict_proba, float64, 898, mib, range, from_array, concatenate, simulate, replicating, few, reality, file, system, usual, estimator__param, instead, param, gamma, return_x_y, svm, wrapping, original, used, inside, gridsearch, update, keys, any, only, complication, comes, another, prefix, names, estimator__, sometimes, much, exactly, does, nothing, underlying, etc, are, parallelized, org, modules, generated, html, had, open, notice, performs, fitting, folds, candidates, totalling, fits, default, multiple, which, perform, context, normally, refit, verbose, clf__n_iter, clf__penalty, elasticnet, 000001, 00001, clf__alpha, tfidf__norm, tfidf__use_idf, 1000, max_iter, clf, tfidf, vect, combines, feature, extraction, classifier, 857, target_names, filenames, subset, set, none, model_selection, linear_model, logging, useful, medium, sized, have, searching, individual, typically, enough, wide, isn, helpful, not, able, cope, though, other, ways, working, alternatively, those, without, significantly, changing, code, uses, anything, accepts, laptop, workstation, gfst4qpj, 32787, 37433, 43855, 2iddzp9o, 38883, 46021, 35545, sq4p4isk, 40509, 35987, 46751, gt1p0g05, 35293, 46693, 43429, now, started, 38375, 89c11552, 5d0d, 4279, 8917, 35605cc96a56, running, 4980102c, object, connection, 7bcf1e8b, 0de1, 11ed, a455, 000d3a8f7959, 2gb, memory_limit, threads_per_worker, n_workers, progress, 5zf6dqaf7jk, ipython, display, video, talks, same, how, cpu, bound, problem, run, view, github, session, pdf, ipynb, theme, executable, book, project, 2019, 2020, 2021, surveys, series, forecasting, stencil, computations, numba, etl, pipelines, prefect, image, processing, evolving, workflows, embarrassingly, asynchronous, computation, servers, async, await, non, blocking, execution, analyze, hosted, json, applications, singular, value, decomposition, generalized, linear, automate, tpot, voting, classifiers, xgboost, hyperparameter, optimization, vectorization, incrementally, pytorch, reading, messy, gotcha, groupby, read, resilience, against, hardware, failures, xarray, operating, sql, futures, delayed, bags, basic, sidebar, community, ecosystem,

Text of the page (random words):
scale scikit learn for small data problems dask examples documentation dask distributed dask ml examples ecosystem community toggle navigation sidebar dask examples documentation basic examples dask arrays dask bags dask dataframes custom workloads with dask delayed custom workloads with futures dask for machine learning operating on dask dataframes with sql xarray with dask arrays resilience against hardware failures dataframes dataframes read and write data dataframes groupby gotcha s from pandas to dask dataframes reading in messy data machine learning blockwise ensemble methods scale scikit learn for small data problems score and predict large datasets batch prediction with pytorch train models on large datasets incrementally train large datasets text vectorization pipeline hyperparameter optimization with dask scale xgboost use voting classifiers automate machine learning with tpot generalized linear models singular value decomposition applications analyze web hosted json data async await and non blocking execution asynchronous computation web servers dask embarrassingly parallel workloads handle evolving workflows image processing etl pipelines with prefect stencil computations with numba time series forecasting user surveys 2021 dask user survey results 2020 dask user survey results 2019 dask user survey results theme by the executable book project toggle navigation ipynb pdf contents distributed training create scikit learn pipeline define grid for parameter search parallel distributed prediction scale scikit learn for small data problems contents distributed training create scikit learn pipeline define grid for parameter search parallel distributed prediction live notebook you can run this notebook in a live session or view it on github scale scikit learn for small data problems this example demonstrates how dask can scale scikit learn to a cluster of machines for a cpu bound problem we ll fit a large model a grid search over many hyper parameters on a small dataset this video talks demonstrates the same example on a larger cluster 1 from ipython display import youtubevideo youtubevideo 5zf6dqaf7jk 1 2 from dask distributed import client progress client client n_workers 4 threads_per_worker 1 memory_limit 2gb client 2 client client 7bcf1e8b 0de1 11ed a455 000d3a8f7959 connection method cluster object cluster type distributed localcluster dashboard http 127 0 0 1 8787 status cluster info localcluster 4980102c dashboard http 127 0 0 1 8787 status workers 4 total threads 4 total memory 7 45 gib status running using processes true scheduler info scheduler scheduler 89c11552 5d0d 4279 8917 35605cc96a56 comm tcp 127 0 0 1 38375 workers 4 dashboard http 127 0 0 1 8787 status total threads 4 started just now total memory 7 45 gib workers worker 0 comm tcp 127 0 0 1 43429 total threads 1 dashboard http 127 0 0 1 46693 status memory 1 86 gib nanny tcp 127 0 0 1 35293 local directory home runner work dask examples dask examples machine learning dask worker space worker gt1p0g05 worker 1 comm tcp 127 0 0 1 46751 total threads 1 dashboard http 127 0 0 1 35987 status memory 1 86 gib nanny tcp 127 0 0 1 40509 local directory home runner work dask examples dask examples machine learning dask worker space worker sq4p4isk worker 2 comm tcp 127 0 0 1 35545 total threads 1 dashboard http 127 0 0 1 46021 status memory 1 86 gib nanny tcp 127 0 0 1 38883 local directory home runner work dask examples dask examples machine learning dask worker space worker 2iddzp9o worker 3 comm tcp 127 0 0 1 43855 total threads 1 dashboard http 127 0 0 1 37433 status memory 1 86 gib nanny tcp 127 0 0 1 32787 local directory home runner work dask examples dask examples machine learning dask worker space worker gfst4qpj distributed training scikit learn uses joblib for single machine parallelism this lets you train most estimators anything that accepts an n_jobs parameter using all the cores of your laptop or workstation alternatively scikit learn can use dask for parallelism this lets you train those estimators using all the cores of your cluster without significantly changing your code this is most useful for training large models on medium sized datasets you may have a large model when searching over many hyper parameters or when using an ensemble method with many individual estimators for too small datasets training times will typically be small enough that cluster wide parallelism isn t helpful for too large datasets larger than a single machine s memory the scikit learn estimators may not be able to cope though dask ml provides other ways for working with larger than memory datasets create scikit learn pipeline 3 from pprint import pprint from time import time import logging from sklearn datasets import fetch_20newsgroups from sklearn feature_extraction text import hashingvectorizer from sklearn feature_extraction text import tfidftransformer from sklearn linear_model import sgdclassifier from sklearn model_selection import gridsearchcv from sklearn pipeline import pipeline 4 scale up set categories none to use all the categories categories alt atheism talk religion misc print loading 20 newsgroups dataset for categories print categories data fetch_20newsgroups subset train categories categories print d documents len data filenames print d categories len data target_names print loading 20 newsgroups dataset for categories alt atheism talk religion misc 857 documents 2 categories we ll define a small pipeline that combines text feature extraction with a simple classifier 5 pipeline pipeline vect hashingvectorizer tfidf tfidftransformer clf sgdclassifier max_iter 1000 define grid for parameter search grid search over some parameters 6 parameters tfidf__use_idf true false tfidf__norm l1 l2 clf__alpha 0 00001 0 000001 clf__penalty l2 elasticnet clf__n_iter 10 50 80 7 grid_search gridsearchcv pipeline parameters n_jobs 1 verbose 1 cv 3 refit false to fit this normally we would write grid_search fit data data data target that would use the default joblib backend multiple processes for parallelism to use the dask distributed backend which will use a cluster of machines to train the model perform the fit in a parallel_backend context 8 import joblib with joblib parallel_backend dask grid_search fit data data data target fitting 3 folds for each of 8 candidates totalling 24 fits if you had your distributed dashboard open during that fit you ll notice that each worker performs some of the fit tasks parallel distributed prediction sometimes you re train on a small dataset but need to predict for a much larger batch of data in this case you d like your estimator to handle numpy arrays and pandas dataframes for training and dask arrays or dataframes for prediction dask_ml wrappers parallelpostfit http ml dask org modules generated dask_ml wrappers parallelpostfit html dask_ml wrappers parallelpostfit __ provides exactly that it s a meta estimator it does nothing during training the underlying estimator probably a scikit learn estimator will probably be in memory on a single machine but tasks like predict score etc are parallelized and distributed most of the time using parallelpostfit is as simple as wrapping the original estimator when used inside a gridsearch you ll need to update the keys of the parameters just like with any meta estimator the only complication comes when using parallelpostfit with another meta estimator like gridsearchcv in this case you ll need to prefix your parameter names with estimator__ 9 from sklearn datasets import load_digits from sklearn svm import svc from dask_ml wrappers import parallelpostfit we ll load the small numpy arrays for training 10 x y load_digits return_x_y true x shape 10 1797 64 11 svc parallelpostfit svc random_state 0 gamma scale param_grid use estimator__param instead of param estimator__c 0 01 1 0 10 grid_search gridsearchcv svc param_grid cv 3 and fit as usual 12 grid_search fit x y 12 gridsearchcv cv 3 estimator parallelpostfit estimator svc random_state 0 param_grid estimator__c 0 01 1 0 10 we ll simulate a large dask array by replicating the training data a few times in reality you would load this from your file system 13 import dask array as da 14 big_x da concatenate da from_array x chunks x shape for _ in range 10 big_x 14 array chunk bytes 8 77 mib 898 50 kib shape 17970 64 1797 64 count 11 tasks 10 chunks type float64 numpy ndarray 64 17970 operations like predict or predict_proba return dask rather than numpy arrays when you compute the work will be done in parallel out of core or distributed on the cluster 15 predicted grid_search predict big_x predicted 15 array chunk bytes 140 39 kib 14 04 kib shape 17970 1797 count 21 tasks 10 chunks type int64 numpy ndarray 17970 1 at this point predicted could be written to disk or aggregated before returning to the client previous blockwise ensemble methods next score and predict large datasets by dask developers copyright 2018 dask developers