Out-of-Process Execution of Python Functionality

Note

There is overhead associated with calling Python® functions out-of-process. This behavior affects performance. MathWorks recommends calling Python functions in-process, which is the default mode.

MATLAB® can run Python scripts and functions in a separate process. Running Python in a separate process enables you to:

  • Use some third-party libraries in the Python code that are not compatible with MATLAB.

  • Isolate the MATLAB process from crashes in the Python code.

To run out-of-process, call the pyenv function with the "ExecutionMode" argument set to "OutOfProcess". For example, suppose that you want to create this list variable in the Python environment.

['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']

To create this list out-of-process, set the MATLAB execution mode to "OutOfProcess". MATLAB displays information about your current Python environment.

pyenv(ExecutionMode="OutOfProcess")
ans = 
  PythonEnvironment with properties:

          Version: "3.11"
       Executable: "C:\Python311\pythonw.exe"
          Library: "C:\windows\system32\python311.dll"
             Home: "C:\Python311"
           Status: NotLoaded
    ExecutionMode: OutOfProcess

Create the variable.

py.list({'Monday','Tuesday','Wednesday','Thursday','Friday'})
ans = 

  Python list with no properties.

    ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']

MATLAB creates a process named MATLABPyHost.

pyenv
ans = 
  PythonEnvironment with properties:

          Version: "3.11"
       Executable: "C:\Python311\pythonw.exe"
          Library: "C:\windows\system32\python311.dll"
             Home: "C:\Python311"
           Status: Loaded
    ExecutionMode: OutOfProcess
        ProcessID: "8196"
      ProcessName: "MATLABPyHost"

Note

Clearing a Python object is asynchronous,? which means that the Python object might remain in Python after the invocation of a synchronous call. For example, in the following code it is possible that myList2 is created before myList is destroyed.

myList=py.list;
clear myList
myList2 = py.list;

Limitations

The size of variables passed between Python and MATLAB is limited to 2 GB when you call a Python function out-of-process. This limit applies to the data plus supporting information passed between the processes.

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Machine Learning in MATLAB

Train Classification Models in Classification Learner App

Train Regression Models in Regression Learner App

Distribution Plots

Explore the Random Number Generation UI

Design of Experiments

Machine Learning Models

Logistic regression

Logistic regression create generalized linear regression model - MATLAB fitglm 2

Support Vector Machines for Binary Classification

Support Vector Machines for Binary Classification 2

Support Vector Machines for Binary Classification 3

Support Vector Machines for Binary Classification 4

Support Vector Machines for Binary Classification 5

Assess Neural Network Classifier Performance

Naive Bayes Classification

ClassificationTree class

Discriminant Analysis Classification

Ensemble classifier

ClassificationTree class 2

Train Generalized Additive Model for Binary Classification

Train Generalized Additive Model for Binary Classification 2

Classification Using Nearest Neighbors

Classification Using Nearest Neighbors 2

Classification Using Nearest Neighbors 3

Classification Using Nearest Neighbors 4

Classification Using Nearest Neighbors 5

Linear Regression

Linear Regression 2

Linear Regression 3

Linear Regression 4

Nonlinear Regression

Nonlinear Regression 2

Visualizing Multivariate Data

Generalized Linear Models

Generalized Linear Models 2

RegressionTree class

RegressionTree class 2

Neural networks

Gaussian Process Regression Models

Gaussian Process Regression Models 2

Understanding Support Vector Machine Regression

Understanding Support Vector Machine Regression 2

RegressionEnsemble



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