Distributed Learning

Overview

• Tutorial: 20 Minutes

  Objectives:

  • Learn how to do distributed learning in Dask.

Distributed learning in Dask splits both the data and computation across multiple machines or nodes in a cluster, leveraging parallelism for faster training. Incremental learning focuses on processing data in smaller chunks sequentially, updating the model after each chunk, and does not require the whole dataset in memory. While Distributed learning distributes the entire dataset across a cluster of workers, where each worker processes a portion of the data in parallel.

Dask distributes the data and computation across the cluster, where each worker processes its portion of the data in parallel. After each worker processes its chunk of the data, the results are aggregated to update the model. The model’s parameters are synchronized across the workers, and training happens concurrently across the distributed setup.

from dask.distributed import Client
from dask_ml.datasets import make_classification
from dask_ml.model_selection import train_test_split
from sklearn.linear_model import SGDClassifier
from dask_ml.model_selection import GridSearchCV
from scipy.stats import uniform, loguniform

# Step 1: Start a Dask client
client = Client()

# Step 2: Create a synthetic classification dataset with Dask
X, y = make_classification(n_samples=5000, n_features=20, chunks=25, random_state=0)

# Step 3: Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y)

# Step 4: Initialize the estimator (SGDClassifier)
estimator = SGDClassifier(random_state=10, max_iter=100)

# Step 5: Define the parameter grid for hyperparameter tuning
params = {
    'alpha': loguniform(1e-5, 1e-1),
    'l1_ratio': uniform(0, 1)
}

# Step 6: Set up GridSearchCV using Dask-ML to distribute hyperparameter search
search = GridSearchCV(estimator, params, cv=3)

# Step 7: Fit the model (this will distribute the computation across the Dask cluster)
search.fit(X_train, y_train)

# Step 8: Output the best hyperparameters and best score
print("Best parameters:", search.best_params_)
print("Best score:", search.best_score_)

# Step 9: Evaluate the model on the test set
test_score = search.score(X_test, y_test)
print("Test score:", test_score)

# Close the Dask client when done
client.close()

Key Points

• Distributed learning can help with large dataset in Dask..