To help you get started, we've collected the most common ways that enchannel-zmq-backend is being used within popular public projects.

nteract / nteract / applications / desktop / src / notebook / epics / zeromq-kernels.ts View on Github

"color: black"
          );

          spawn.stdout?.on("data", data => {
            const text = data.toString();
            logStd(text);
            observer.next(actions.kernelRawStdout({ text, kernelRef }));
          });
          spawn.stderr?.on("data", data => {
            const text = data.toString();
            logStd(text);
            observer.next(actions.kernelRawStderr({ text, kernelRef }));
          });

          // do dependency injection of jmp to make it match our ABI version of node
          const channels = await createMainChannel(
            config,
            undefined,
            undefined,
            undefined,
            jmp
          );
          observer.next(
            actions.setKernelspecInfo({
              contentRef,
              kernelInfo: kernelSpec
            })
          );

          const kernel: LocalKernelProps = {
            info: null,
            type: "zeromq",

View more ways to use enchannel-zmq-backend

enchannel-zmq-backend

Installation | Usage | Contributors and developers | Learn more about nteract

enchannel-zmq-backend offers the ZeroMQ backend implementation for enchannel.

Technical overview

As a refresher for the reader, enchannel details nteract's lightweight, implementation-flexible specification for communication between a user frontend and a backend, such as a language kernel. The enchannel specification offers a simple description of "what" messages may be passed between frontends and backends, while leaving a developer freedom in "how" to achieve message communication.

enchannel-zmq-backend takes a classic design approach using ZeroMQ, the foundation messaging protocol for the Jupyter project. enchannel-zmq-backend implements backend support for the messaging channels described in the Jupyter messaging specification. This spec explains how front end clients should communicate with backend language kernels which implement the Jupyter messaging specification.

Our backend

enchannel-zmq-backend implements the "how" to communicate messages to and from a backend.

We provide functions to create RxJS Subjects (two way Observables for four of the channels described in the Jupyter messaging specification):

• shell
• control
• iopub
• stdin

That's it. Functions for four channels; simplicity in action.

Installation

Prerequisite: Node.js and npm

You may use whichever package manager (npm or yarn) best suits your workflow. The nteract team internally uses yarn.

npm install enchannel-zmq-backend
# OR
yarn add enchannel-zmq-backend

Usage

Creating messaging channels

To get access to all of the channels for messaging (shell, control, iopub, and stdin), import and use the createChannels function:

import { createChannels } from 'enchannel-zmq-backend'

The createChannels function accepts two things:

• an identity

  You'll want to set up your identity, relying on the node uuid package:

  const uuidv4 = require('uuid/v4');
  const identity = uuidv4();
  

• a runtime object, such as a kernel (which matches the on-disk JSON). Using spawnteract with this project helps streamline spawning a kernel.

  const runtimeConfig = {
    stdin_port: 58786,
    ip: '127.0.0.1',
    control_port: 58787,
    hb_port: 58788,
    signature_scheme: 'hmac-sha256',
    key: 'dddddddd-eeee-aaaa-dddd-dddddddddddd',
    shell_port: 58784,
    transport: 'tcp',
    iopub_port: 58785
  }
  

To create the channels object:

const channels = createChannels(identity, runtimeConfig)
const { shell, iopub, stdin, control } = channels;

enchannel-zmq-backend also gives access to all of the channels via a single multipled channel exposed via createMainChannel.

import { createMainChannel } from 'enchannel-zmq-backend';

Similar to the createChannels function, the createMainChannel function accepts both an identity and a runtime object.

const channel = createMainChannel(identity, runtimeConfig);

Messages that are sent via the mutliplexed channel need to define a type property that outlines which channel they should be sent under.

const body = {
    header: {
        msg_id: `execute_9ed11a0f-707e-4f71-829c-a19b8ff8eed8`,
        username: "rgbkrk",
        session: "00000000-0000-0000-0000-000000000000",
        msg_type: "execute_request",
        version: "5.0"
    },
    content: {
        code: 'print("woo")',
        silent: false,
        store_history: true,
        user_expressions: {},
        allow_stdin: false
    }
};
const message = { type: "shell", body };

enchannel-zmq-backend also offers four convenience functions to easily create the messaging channels for control, stdin, iopub, and shell :

import {
  createControlSubject,
  createStdinSubject,
  createIOPubSubject,
  createShellSubject,
} from 'enchannel-zmq-backend';

Creating a subject for the shell channel:

const shell = createShellSubject(identity, runtimeConfig)

Subscribing to messages

Here's an example about how to subscribe to iopub messages:

const iopub = createIOPubSubject(identity, runtimeConfig);
var subscription = iopub.subscribe(msg => {
  console.log(msg);
}

// later, run subscription.unsubscribe()

Since these channels are RxJS Observables, you can use filter, map, scan and many other RxJS operators:

iopub.filter(msg => msg.header.msg_type === 'execute_result')
     .map(msg => msg.content.data)
     .subscribe(x => { console.log(`DATA! ${util.inspect(x)}`)})

Sending messages to the kernel

Executing code will rely on sending an execute_request to the shell channel.

var message = {
  header: {
    msg_id: `execute_9ed11a0f-707e-4f71-829c-a19b8ff8eed8`,
    username: 'rgbkrk',
    session: '00000000-0000-0000-0000-000000000000',
    msg_type: 'execute_request',
    version: '5.0',
  },
  content: {
    code: 'print("woo")',
    silent: false,
    store_history: true,
    user_expressions: {},
    allow_stdin: false,
  },
};

Currently, you'll need to have at least one subscription activated before you can send on a channel.

> shell.subscribe(console.log)

> shell.next(message)
> Message {
  header:
   { username: 'rgbkrk',
     msg_type: 'execute_reply',
     msg_id: '0f6d37f3-56a2-41fd-b3ed-90cc189ac423',
     version: '5.1',
     session: '40472e70-e008-48d1-9537-55837a905c05',
     date: '2016-01-12T00:39:44.686986' },
  parent_header:
   { username: 'rgbkrk',
     session: '00000000-0000-0000-0000-000000000000',
     version: '5.0',
     msg_id: 'execute_9ed11a0f-707e-4f71-829c-a19b8ff8eed8',
     msg_type: 'execute_request' },
  metadata:
   { dependencies_met: true,
     engine: '34d73425-4f04-4b57-9bc7-b46e3100e1fd',
     status: 'ok',
     started: '2016-01-12T00:39:44.684534' },
  content:
   { status: 'ok',
     execution_count: 60,
     user_expressions: {},
     payload: [] } }

Contributors and developers

ZeroMQ Dependency

If you plan to contribute to this project or extend it, you will need to have ZeroMQ installed on your system. The easiest way to do this is to install nteract's zmq-prebuilt binary for your operating system.

Install a local development environment

To set up a development environment, you'll need to install:

• npm
• zmq-prebuilt

Then, fork and clone this repo:

git clone https://github.com/nteract/enchannel-zmq-backend.git
cd enchannel-zmq-backend
yarn

Develop! We welcome new and first time contributors.

Learn more about nteract

• Visit our website https://nteract.io/.
• See our organization on GitHub https://github.com/nteract
• Join us on Slack if you need help or have questions. If you have trouble creating an account, either email rgbkrk@gmail.com or post an issue on GitHub.