'How to find an optimum number of processes in GridSearchCV( ..., n_jobs = ... )?
I'm wondering, which is better to use with GridSearchCV( ..., n_jobs = ... ) to pick the best parameter set for a model, n_jobs = -1 or n_jobs with a big number,
like n_jobs = 30 ?
Based on Sklearn documentation:
n_jobs = -1means that the computation will be dispatched on all the CPUs of the computer.
On my PC I have an Intel i3 CPU, which has 2 cores and 4 threads, so does that mean if I set n_jobs = -1, implicitly it will be equal to n_jobs = 2 ?
Solution 1:[1]
An additional simpler answer by Prof. Kevyn Collins-Thompson, from course Applied Machine Learning in Python:
If I have 4 cores in my system, n_jobs = 30 (30 as an example) will be the same as n_jobs = 4. So no additional effect
So the maximum performance that can be obtained always is using
n_jobs = -1
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source |
|---|---|
| Solution 1 | thepunitsingh |