You can try Dask-ML on a small cloud instance by clicking the following button:
import dask.dataframe as dd df = dd.read_parquet('...') data = df[['age', 'income', 'married']] labels = df['outcome'] from dask_ml.linear_model import LogisticRegression lr = LogisticRegression() lr.fit(data, labels)
What does this offer?¶
The overarching goal of Dask-ML is to enable scalable machine learning. See the navigation pane to the left for details on specific features.
How does this work?¶
Modern machine learning algorithms employ a wide variety of techniques. Scaling these requires a similarly wide variety of different approaches. Generally solutions fall into the following three categories:
Parallelize Scikit-Learn Directly¶
Scikit-Learn already provides parallel computing on a single machine with Joblib. Dask extends this parallelism to many machines in a cluster. This works well for modest data sizes but large computations, such as random forests, hyper-parameter optimization, and more.
from dask.distributed import Client import joblib client = Client() # Connect to a Dask Cluster with joblib.parallel_backend('dask'): # Your normal scikit-learn code here
See Dask-ML Joblib documentation for more information.
Note that this is an active collaboration with the Scikit-Learn development team. This functionality is progressing quickly but is in a state of rapid change.
Reimplement Scalable Algorithms with Dask Array¶
Some machine learning algorithms are easy to write down as Numpy algorithms. In these cases we can replace Numpy arrays with Dask arrays to achieve scalable algorithms easily. This is employed for linear models, pre-processing, and clustering.
from dask_ml.preprocessing import Categorizer, DummyEncoder from dask_ml.linear_model import LogisticRegression lr = LogisticRegression() lr.fit(data, labels)
Partner with other distributed libraries¶
Other machine learning libraries like XGBoost already have distributed solutions that work quite well. Dask-ML makes no attempt to re-implement these systems. Instead, Dask-ML makes it easy to use normal Dask workflows to prepare and set up data, then it deploys XGBoost alongside Dask, and hands the data over.
from dask_ml.xgboost import XGBRegressor est = XGBRegressor(...) est.fit(train, train_labels)
See Dask-ML + XGBoost for more information.
In all cases Dask-ML endeavors to provide a single unified interface around the familiar NumPy, Pandas, and Scikit-Learn APIs. Users familiar with Scikit-Learn should feel at home with Dask-ML.