Current solutions spawn a new process whenever you want to run Python code in Node.js and communicate via IPC using sockets, stdin/stdout, etc. But creating new processes every time you want to run Python code could be a major overhead and can lead to significant performance penalties. If the execution time of your Python code is less than creating a new process, you will see significant performance problems because your Node.js code will keep creating new processes instead of executing your Python code. Suppose you have a few NumPy calls in Python: do you want to create a new process for that? I guess your answer is no. In this case, running the Python code in-process is a much better solution because using the embedded Python interpreter is much faster than creating new processes and does not require any IPC to pass the data around. The data can stay in memory and requires only some conversions between Python and Node types (using the N-API and Python C API).

npm install node-calls-python 

Sometimes you have to install prerequisites to make it work.

sudo apt install curl curl -sL https://deb.nodesource.com/setup_13.x | sudo -E bash - sudo apt install nodejs 

sudo apt install python3 sudo apt install python3-dev 

sudo apt install make sudo apt install g++ sudo npm install -g node-gyp 

npm install --global --production windows-build-tools npm install -g node-gyp 

npm install node-calls-python 

npm install --arch=arm64 --target_arch=arm64 node-calls-python 

Let's say you have the following python code in test.py

importnumpyasnpdefmultiple(a, b): returnnp.multiply(a, b).tolist()

Then to call this function directly you can do this in Node

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;py.import("path/to/test.py").then(asyncfunction(pymodule){constresult=awaitpy.call(pymodule,"multiple",[1,2,3,4],[2,3,4,5]);console.log(result);});

Or to call this function by using the synchronous version

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;py.import("path/to/test.py").then(asyncfunction(pymodule){constresult=py.callSync(pymodule,"multiple",[1,2,3,4],[2,3,4,5]);console.log(result);});

Let's say you have the following python code in test.py

importnumpyasnpclassCalculator: vector= [] def__init__(self, vector): self.vector=vectordefmultiply(self, scalar, vector): returnnp.add(np.multiply(scalar, self.vector), vector).tolist()

Then to instance the class directly in Node

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;py.import("path/to/test.py").then(asyncfunction(pymodule){constpyobj=awaitpy.create(pymodule,"Calculator",[1.4,5.5,1.2,4.4]);constresult=awaitpy.call(pyobj,"multiply",2,[10.4,50.5,10.2,40.4]);});

Or to instance the class synchronously and directly in Node

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;py.import("path/to/test.py").then(asyncfunction(pymodule){constpyobj=py.createSync(pymodule,"Calculator",[1.4,5.5,1.2,4.4]);constresult=awaitpy.callSync(pyobj,"multiply",2,[10.4,50.5,10.2,40.4]);// you can use async version (call) as well});

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;py.import("path/to/test.py").then(asyncfunction(pymodule){awaitpy.exec(pymodule,"run_my_code(1, 2, 3)");// exec will run any python code but the return value is not propagatedconstresult=awaitpy.eval(pymodule,"run_my_code(1, 2, 3)");// result will hold the output of run_my_codeconsole.log(result);});

Running python code synchronously

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;constpymodule=py.importSync("path/to/test.py");awaitpy.execSync(pymodule,"run_my_code(1, 2, 3)");// exec will run any python code but the return value is not propagatedconstresult=py.evalSync(pymodule,"run_my_code(1, 2, 3)");// result will hold the output of run_my_codeconsole.log(result);

You have to set allowReimport parameter to true when calling import/importSync.

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;letpymodule=py.importSync("path/to/test.py");pymodule=py.importSync("path/to/test.py",true);

During development, you may want to update your python code running inside Node without restarting your Node process. To achieve this you can reimport your python modules. All your python modules will be reimported where the filename of your python module matches the string parameter: path/to/your/python/code.

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;py.reimport('path/to/your/python/code');

Another option is to run node-calls-python in development mode. In this case, once you have updated your python code under path/to/your/python/code the runtime will automatically reimport the changed modules.

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;py.developmentMode('path/to/your/python/code');

Javascript has no similar concept to kwargs of Python. Therefore a little hack is needed here. If you pass an object with __kwargs property set to true as a parameter to call/callSync/create/createSync the object will be mapped to kwargs.

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;letpymodule=py.importSync("path/to/test.py");py.callSync(pymodule,"your_function",arg1,arg2,{"name1": value1,"name2": value2,"__kwargs": true})

defyour_function(arg1, arg2, **kwargs): print(kwargs)

If you want to trigger a call from your Python code back to JavaScript this feature could be useful.

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;letpymodule=py.importSync("path/to/test.py");functionjsFunction(arg1,arg2,arg3){console.log(arg1,arg2,arg3);returnarg3+1;}py.callSync(pymodule,"your_function",arg1,arg2,jsFunction);

defyour_function(arg1, arg2, jsFunction): jsResult=jsFunction(arg1+arg2, "any string", 42); print(jsResult);

You can also do this using the async API.

py.call(pymodule,"your_function",arg1,arg2,jsFunction);

By default, the async Python call will wait for the execution of the JavaScript function by synchronizing the libuv thread (used by the Python call) and the main thread (used by the JavaScript function). So the order of execution will look like this:

 - start of py.call - start of your_function - start of jsFunction - end of jsFunction - end of your_function - end of py.call 

If you do not want to synchronize the execution of your JavaScript and Python code, you have to turn this off by calling setSyncJsAndPyInCallback(false) on the interpreter.

py.setSyncJsAndPyInCallback(false);

In this case, one possible order of the execution could look like this (the actual order is determined by the runtime. jsFunction will run completely async).

 - start of py.call - start of your_function - put jsFunction to the queue of the runtime - end of your_function - end of py.call - start of jsFunction - end of jsFunction 

Because jsFunction runs async, it is not possible to pass the result of jsFunction back to Python. But passing arguments from Python to jsFunction is still possible.

Python uses sys.executable variable when creating new processes. Because the interpreter is embedded into Node, sys.executable points to the Node executable. node-calls-python automatically overrides this setting in the multiprocessing module to point to the real Python executable. In case it does not work or you want to use a different Python executable, call setPythonExecutable(absolute-path-to-your-python-executable) before using the multiprocessing module.

py.setPythonExecutable(absolute-path-to-your-python-executable);

Let's say you have the following python code in logreg.py

fromsklearn.datasetsimportload_iris, load_digitsfromsklearn.linear_modelimportLogisticRegressionclassLogReg: logreg=Nonedef__init__(self, dataset): if (dataset=="iris"): X, y=load_iris(return_X_y=True) else: X, y=load_digits(return_X_y=True) self.logreg=LogisticRegression(random_state=42, solver='lbfgs', multi_class='multinomial') self.logreg.fit(X, y) defpredict(self, X): returnself.logreg.predict_proba(X).tolist()

Then you can do this in Node

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;py.import("logreg.py")).then(asyncfunction(pymodule){// import the python moduleconstlogreg=awaitpy.create(pymodule,"LogReg","iris");// create the instance of the classifierconstpredict=awaitpy.call(logreg,"predict",[[1.4,5.5,1.2,4.4]]);// call predictconsole.log(predict);});

You can import node-calls-python as an ES module.

import{interpreteraspy}from'node-calls-python';letpymodule=py.importSync(pyfile);

If you see the following error when importing in Next.js Module not found: Can't resolve './build/Release/nodecallspython'

You have to add the following code to your next.config.mjs because currently Next.js cannot bundle native node addons properly. For more details, please see serverComponentsExternalPackages in Next.js

/** @type{import('next').NextConfig} */ const nextConfig ={experimental:{serverComponentsExternalPackages: [ 'node-calls-python' ] } }; export default nextConfig; 

You have to add the proper import path so that python could use your installed packages from your venv.

If you have created a venv by python -m venv your-venv your installed python packages can be found under your-venv/Lib/site-packages. So you have to use addImportPath before importing any module to pick up the python packages from your venv.

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;py.addImportPath(your-venv/Lib/site-packages)

If you get an error like this while trying to call Python code ImportError: /usr/local/lib/python3.7/dist-packages/cpython-37m-arm-linux-gnueabihf.so: undefined symbol: PyExc_RuntimeError

You can fix it by passing the name of your libpython shared library to fixlink

constnodecallspython=require("node-calls-python");constpy=nodecallspython.interpreter;py.fixlink('libpython3.7m.so');

 - undefined to None - null to None - boolean to boolean - number to double or long (as appropriate) - int32 to long - uint32 to long - int64 to long - string to unicode (string) - array to list - object to dictionary - ArrayBuffer to bytes - Buffer to bytes - TypedArray to bytes - Function to function 

 - None to undefined - boolean to boolean - double to number - long to int64 - unicode (string) to string - list to array - tuple to array - set to array - dictionary to object - numpy.array to array (this has limited support, will convert everything to number or string) - bytes to ArrayBuffer - bytearray to ArrayBuffer