Long Time to Collect Results of Distributed Spark-Sklearn Training

[wjohnson]

I'm running 15 combinations of a Logistic Regression model with spark-sklearn and I'll see that all tasks have completed but there is a huge amount of time to collect all of the results. I'm guessing it's the number of my coefficients that I'm bringing back to the driver. But I've noticed it several times when I'm working with wide datasets or deep random forests. Is it just expected due to network traffic?

Data set size: 31,358 rows, 10000 columns

param_grid = [
  dict(
  penalty=['l2'], 
  C = [1.0, 0.5, 0.1], 
  solver = ['newton-cg', 'lbfgs', 'sag']
  ),
  dict(
  penalty=['l1', 'elasticnet'], 
  C = [1.0, 0.5, 0.1], 
  solver = ['saga',]
  )
]
grid = GridSearchCV(sc, estimator=LogisticRegression(max_iter=500), param_grid=param_grid, n_jobs=-1, cv=5)
grid_result = grid.fit(X_train, y_train)

Environment:

• Azure Databricks ML 5.5 Runtime
• 9 Worker nodes with 56GB and 8 cores each

[srowen]

It could be; how big is the data set / model? Is it definitely waiting on collecting the data, not fitting?