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  3. Parallel Programming with Dask in Python
  • 1

    Lazy Evaluation and Parallel Computing

    Gratuito

    This chapter will teach you the basics of Dask and lazy evaluation. At the end of this chapter, you'll be able to speed up almost any Python code by using parallel processing or multi-threading. You'll learn the difference between these two task scheduling methods and which one is better under which circumstances.

  • 2

    Parallel Processing of Big, Structured Data

    Here you’ll learn how to analyze big structured data using Dask arrays and Dask DataFrames. You'll learn how everything you know about NumPy and pandas can easily be applied to data that is too large to fit in memory.

  • 3

    Dask Bags for Unstructured Data

    Process any kind of data. You'll learn how Dask bags can be used to efficiently process unstructured text data, semi-structured JSON data, and even recorded audio.

  • 4

    Dask Machine Learning and Final Pieces

    Harness the power of Dask to train machine learning models. You'll learn how to train machine learning models on big data using the Dask-ML package, and how to split Dask calculations across a mixture of processes and threads for even greater computing speed.

Initializing

Exercicio

Making lazy predictions

The model you trained last time was good, but it could be better if you passed through the training data a few more times. Also, it is a shame to see a good model go to waste, so you should use this one to make some predictions on a separate dataset from the one you train on.

An unfitted version of the model you created in the last exercise is available in your environment as dask_model. Dask DataFrames of training data are available as dask_X and dask_y.

Instruções

100 XP
  • Create a for loop and use it to train dask_model on dask_X and dask_y 5 times.
  • Use the fitted model to make predictions for the input variables dask_X.
  • Compute these predictions using the default scheduler.