node-calls-python - call Python from NodeJS directly in-process without spawning processes

Suitable for running your ML or deep learning models from Node directly

Installation

npm install node-calls-python

Installation FAQ

Sometimes you have to install prerequisites to make it work.

Linux: install node, npm, node-gyp, python3, python3-dev, g++ and make

Install Node

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

Install Python

sudo apt install python3
sudo apt install python3-dev

Install Node-gyp

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

Windows: install NodeJS and Python

Install Node-gyp if missing

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

Mac: install XCode from AppStore, NodeJS and Python

npm install node-calls-python

If you see installation problems on Mac with ARM (E.g. using M1 Pro), try to specify 'arch' and/or 'target_arch' parameters for npm

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

Examples

Calling a simple python function

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

import numpy as np

def multiple(a, b):
    return np.multiply(a, b).tolist()

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

const nodecallspython = require("node-calls-python");

const py = nodecallspython.interpreter;

py.import("path/to/test.py").then(async function(pymodule) {
    const result = await py.call(pymodule, "multiple", [1, 2, 3, 4], [2, 3, 4, 5]);
    console.log(result);
});

Or to call this function by using the synchronous version

const nodecallspython = require("node-calls-python");

const py = nodecallspython.interpreter;

py.import("path/to/test.py").then(async function(pymodule) {
    const result = py.callSync(pymodule, "multiple", [1, 2, 3, 4], [2, 3, 4, 5]);
    console.log(result);
});

Creating python objects

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

import numpy as np

class Calculator:
    vector = []

    def __init__(self, vector):
        self.vector = vector

    def multiply(self, scalar, vector):
        return np.add(np.multiply(scalar, self.vector), vector).tolist()

Then to instance the class directly in Node

const nodecallspython = require("node-calls-python");

const py = nodecallspython.interpreter;

py.import("path/to/test.py").then(async function(pymodule) {
    const pyobj = await py.create(pymodule, "Calculator", [1.4, 5.5, 1.2, 4.4]);
    const result = await py.call(pyobj, "multiply", 2, [10.4, 50.5, 10.2, 40.4]);
});

Or to instance the class synchronously and directly in Node

const nodecallspython = require("node-calls-python");

const py = nodecallspython.interpreter;

py.import("path/to/test.py").then(async function(pymodule) {
    const pyobj = py.createSync(pymodule, "Calculator", [1.4, 5.5, 1.2, 4.4]);
    const result = await py.callSync(pyobj, "multiply", 2, [10.4, 50.5, 10.2, 40.4]); // you can use async version (call) as well
});

Running python code

const nodecallspython = require("node-calls-python");

const py = nodecallspython.interpreter;

py.import("path/to/test.py").then(async function(pymodule) {
    await py.exec(pymodule, "run_my_code(1, 2, 3)"); // exec will run any python code but the return value is not propagated
    const result = await py.eval(pymodule, "run_my_code(1, 2, 3)"); // result will hold the output of run_my_code
    console.log(result);
});

Running python code synchronously

const nodecallspython = require("node-calls-python");

const py = nodecallspython.interpreter;

const pymodule = py.importSync("path/to/test.py");
await py.execSync(pymodule, "run_my_code(1, 2, 3)"); // exec will run any python code but the return value is not propagated
const result = py.evalSync(pymodule, "run_my_code(1, 2, 3)"); // result will hold the output of run_my_code
console.log(result);

Doing some ML with Python and Node

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

from sklearn.datasets import load_iris, load_digits
from sklearn.linear_model import LogisticRegression

class LogReg:
    logreg = None

    def __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)

    def predict(self, X):
        return self.logreg.predict_proba(X).tolist()

Then you can do this in Node

const nodecallspython = require("node-calls-python");

const py = nodecallspython.interpreter;

py.import("logreg.py")).then(async function(pymodule) { // import the python module
    const logreg = await py.create(pymodule, "LogReg", "iris"); // create the instance of the classifier

    const predict = await py.call(logreg, "predict", [[1.4, 5.5, 1.2, 4.4]]); // call predict
    console.log(predict);
});

Working Around Linking Errors on Linux

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

const nodecallspython = require("node-calls-python");

const py = nodecallspython.interpreter;
py.fixlink('libpython3.7m.so');

See more examples here

Supported data mapping

From Node to Python

  - 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

From Python to Node

  - 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)