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Top Parallel Processing Python Frameworks Data Scientists must know in 2022
Python libraries for distributing your machine learning and data science workloads on a cluster of CPU / GPU
Introduction
Machine Learning is easy when you are working with datasets that can fit into the memory of your laptop. There are so many amazing open-source libraries and frameworks in Python that are easy to learn and are really fun to work with such as scikit-learn, TensorFlow, PyTorch, PyCaret, etc. The question is what do you do when your dataset is too big to fit in the memory? Welcome to the world of Big Data! This article will introduce you to some of the most popular Python frameworks and libraries that Data Scientists use to distribute their machine learning tasks that they cannot do on their laptops.
PySpark
Spark has been a well-liked option for distributed computing frameworks for a time. Despite being an established technology, there is a significant learning curve. Users frequently need to convert code written in pandas to native Spark syntax, which can take effort and be challenging to maintain over time.
Spark is available for use in three languages: Python, R, and Scala. PySpark is the most popular interface and it allows users to use Spark in the Python. PySpark supports most of…
