Scikit-Learn

Scikit-learn is a Python module for machine learning

To run this test with the Phoronix Test Suite, the basic command is: phoronix-test-suite benchmark scikit-learn.

Project Site

scikit-learn.org

Test Created

28 September 2016

Last Updated

10 January 2020

Test Maintainer

Victor Rodriguez 

Test Type

System

Average Install Time

1 Second

Average Run Time

2 Minutes, 6 Seconds

Test Dependencies

Python Scipy + Python Sklearn + Python

Accolades

60k+ Downloads

Supported Platforms


Public Result Uploads *Reported Installs **Reported Test Completions **Test Profile Page Views ***OpenBenchmarking.orgEventsScikit-Learn Popularity Statisticspts/scikit-learn2016.092016.112017.012017.032017.052017.072017.092017.112018.012018.032018.052018.072018.092018.112019.012019.032019.052019.072019.092019.112020.012020.032020.052020.072020.092020.112021.012021.032021.052021.072021.094K8K12K16K20K
* Uploading of benchmark result data to OpenBenchmarking.org is always optional (opt-in) via the Phoronix Test Suite for users wishing to share their results publicly.
** Data based on those opting to upload their test results to OpenBenchmarking.org and users enabling the opt-in anonymous statistics reporting while running benchmarks from an Internet-connected platform.
*** Test profile page view reporting began March 2021.
Data current as of 22 September 2021.

Revision History

pts/scikit-learn-1.1.0   [View Source]   Fri, 10 Jan 2020 09:06:48 GMT
Update test profile per https://github.com/phoronix-test-suite/test-profiles/pull/124 but bump version to 1.1.0 due to scikit-learn version change. Also explicitly use python3 binary name.

pts/scikit-learn-1.0.1   [View Source]   Thu, 04 May 2017 10:41:34 GMT
Use unzip -o

pts/scikit-learn-1.0.0   [View Source]   Wed, 28 Sep 2016 09:45:52 GMT
Initial commit.

Suites Using This Test

Machine Learning

CPU Massive

Server CPU Tests

HPC - High Performance Computing

Python


Performance Metrics

Analyze Test Configuration:

Scikit-Learn 0.22.1

OpenBenchmarking.org metrics for this test profile configuration based on 1,230 public results since 10 January 2020 with the latest data as of 7 September 2021.

Below is an overview of the generalized performance for components where there is sufficient statistically significant data based upon user-uploaded results. It is important to keep in mind particularly in the Linux/open-source space there can be vastly different OS configurations, with this overview intended to offer just general guidance as to the performance expectations.

Component
Percentile Rank
# Compatible Public Results
Seconds (Average)
Mid-Tier
75th
> 9
Median
50th
11
47th
3
11 +/- 1
36th
3
13 +/- 1
Low-Tier
25th
> 14
12th
3
78 +/- 6
OpenBenchmarking.orgDistribution Of Public Results1226 Results Range From 6 To 40373 Seconds681416222430323840464854566264707278808688949702105101131812126129341374214550153581616616974177821859019398202062101421822226302343824246250542586226670274782828629094299023071031518323263313433942347503555836366371743798238790395984040630060090012001500

Based on OpenBenchmarking.org data, the selected test / test configuration (Scikit-Learn 0.22.1) has an average run-time of 2 minutes. By default this test profile is set to run at least 3 times but may increase if the standard deviation exceeds pre-defined defaults or other calculations deem additional runs necessary for greater statistical accuracy of the result.

OpenBenchmarking.orgMinutesTime Required To Complete BenchmarkRun-Time1020304050Min: 1 / Avg: 2.15 / Max: 50

Based on public OpenBenchmarking.org results, the selected test / test configuration has an average standard deviation of 0.8%.

OpenBenchmarking.orgPercent, Fewer Is BetterAverage Deviation Between RunsDeviation246810Min: 0 / Avg: 0.76 / Max: 5

Does It Scale Well With Increasing Cores?

No, based on the automated analysis of the collected public benchmark data, this test / test settings does not generally scale well with increasing CPU core counts. Data based on publicly available results for this test / test settings, separated by vendor, result divided by the reference CPU clock speed, grouped by matching physical CPU core count, and normalized against the smallest core count tested from each vendor for each CPU having a sufficient number of test samples and statistically significant data.

AMDIntelOpenBenchmarking.orgRelative Core Scaling To BaseScikit-Learn CPU Core Scaling46816243248641.05692.11383.17074.22765.2845

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