You can run this notebook in a live session Binder or view it on Github.

Train Models on Large Datasets

Most estimators in scikit-learn are designed to work with NumPy arrays or scipy sparse matricies. These data structures must fit in the RAM on a single machine.

Estimators implemented in Dask-ML work well with Dask Arrays and DataFrames. This can be much larger than a single machine’s RAM. They can be distributed in memory on a cluster of machines.

In [ ]:
%matplotlib inline
In [ ]:
from dask.distributed import Client

# Scale up: connect to your own cluster with more resources
# see http://dask.pydata.org/en/latest/setup.html
client = Client(processes=False, threads_per_worker=4,
                n_workers=1, memory_limit='2GB')
client
In [ ]:
import dask_ml.datasets
import dask_ml.cluster
import matplotlib.pyplot as plt

In this example, we’ll use dask_ml.datasets.make_blobs to generate some random dask arrays.

In [ ]:
# Scale up: increase n_samples or n_features
X, y = dask_ml.datasets.make_blobs(n_samples=1000000,
                                   chunks=100000,
                                   random_state=0,
                                   centers=3)
X = X.persist()
X

We’ll use the k-means implemented in Dask-ML to cluster the points. It uses the k-means|| (read: “k-means parallel”) initialization algorithm, which scales better than k-means++. All of the computation, both during and after initialization, can be done in parallel.

In [ ]:
km = dask_ml.cluster.KMeans(n_clusters=3, init_max_iter=2, oversampling_factor=10)
km.fit(X)

We’ll plot a sample of points, colored by the cluster each falls into.

In [ ]:
fig, ax = plt.subplots()
ax.scatter(X[::1000, 0], X[::1000, 1], marker='.', c=km.labels_[::1000],
           cmap='viridis', alpha=0.25);

For all the estimators implemented in Dask-ML, see the API documentation.