Parallel Processing With The Pool Object In Python

Kevin Feasel

2018-12-27

Python

Sanjay Kumar takes us through parallel processing in Python:

The parallel processing holds two varieties of execution: Synchronous and Asynchronous.
In synchronous execution, once a process starts execution, it puts a lock over the main program until its get accomplished.
While the asynchronous execution doesn’t require locking, it performs a task quickly but the outcome can be in the rearranged order.

Click through for a few examples using Pool.

Related Posts

Monte Carlo Simulation in Python

Kristian Larsen has a couple of posts on Monte Carlo style simulation in Python. First up is a post which covers how to generate data from different distributions: One method that is very useful for data scientist/data analysts in order to validate methods or data is Monte Carlo simulation. In this article, you learn how […]

Read More

Hyperparameter Tuning with MLflow

Joseph Bradley shows how you can perform hyperparameter tuning of an MLlib model with MLflow: Apache Spark MLlib users often tune hyperparameters using MLlib’s built-in tools CrossValidator and TrainValidationSplit.  These use grid search to try out a user-specified set of hyperparameter values; see the Spark docs on tuning for more info. Databricks Runtime 5.3 and 5.3 ML and above support […]

Read More

Categories

December 2018
MTWTFSS
« Nov Jan »
 12
3456789
10111213141516
17181920212223
24252627282930
31