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Interactions between Dask and XGBoost

Project description

Distributed training with XGBoost and Dask.distributed

This repository enables you to perform distributed training with XGBoost on Dask.array and Dask.dataframe collections.

pip install dask-xgboost

Example

from dask.distributed import Client
client = Client('scheduler-address:8786')  # connect to cluster

import dask.dataframe as dd
df = dd.read_csv('...')  # use dask.dataframe to load and
df_train = ...           # preprocess data
labels_train = ...

import dask_xgboost as dxgb
params = {'objective': 'binary:logistic', ...}  # use normal xgboost params
bst = dxgb.train(client, params, df_train, labels_train)

>>> bst  # Get back normal XGBoost result
<xgboost.core.Booster at ... >

predictions = dxgb.predict(client, bsg, data_test)

How this works

For more information on using Dask.dataframe for preprocessing see the Dask.dataframe documentation.

Once you have created suitable data and labels we are ready for distributed training with XGBoost. Every Dask worker sets up an XGBoost slave and gives them enough information to find each other. Then Dask workers hand their in-memory Pandas dataframes to XGBoost (one Dask dataframe is just many Pandas dataframes spread around the memory of many machines). XGBoost handles distributed training on its own without Dask interference. XGBoost then hands back a single xgboost.Booster result object.

Larger Example

For a more serious example see

History

Conversation during development happened at dmlc/xgboost #2032

Project details


Release history Release notifications

This version
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0.1.5

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0.1.3

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0.1.2

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0.1.0

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Filename, size & hash SHA256 hash help File type Python version Upload date
dask-xgboost-0.1.5.tar.gz (11.5 kB) Copy SHA256 hash SHA256 Source None Nov 17, 2017

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