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scikit-learn

scikit-learn is a machine learning toolkit for Python.

Below you will find an example on how to use an OSG-provided software container that contains scikit-learn. However, it is good to keep in mind that you have two options when it comes to integrating your own code:

  1. If the code is simple, send it with the job (this is what the example uses)
  2. For more complex codes, consider extending the provided containers and integrate the code into the new custom container

Containers are detailed in our general documentation:

Scikit-learn Python Code

An example scikit-learn machine learning executable is:

#!/usr/bin/env python3

# example adopted from https://scikit-learn.org/stable/tutorial/basic/tutorial.html

from sklearn import datasets
from sklearn import svm

iris = datasets.load_iris()
digits = datasets.load_digits()

# learning
clf = svm.SVC(gamma=0.001, C=100.)
clf.fit(digits.data[:-1], digits.target[:-1])

# predicting
print(clf.predict(digits.data[-1:]))

Submit File

universe = container
container_image = /cvmfs/singularity.opensciencegrid.org/htc/scikit-learn:1.3

log = job_$(Cluster)_$(Process).log
error = job_$(Cluster)_$(Process).err
output = job_$(Cluster)_$(Process).out

executable = run-scikit-learn.py
#arguments =

# specify both general requirements and gpu requirements if there are any
# requirements = True
# require_gpus =

+JobDurationCategory = "Medium"

request_gpus = 0
request_cpus = 1
request_memory = 4GB
request_disk = 4GB

queue 1