Huge News!Announcing our $40M Series B led by Abstract Ventures.Learn More
Socket
Sign inDemoInstall
Socket

node-red-contrib-model-asset-exchange

Package Overview
Dependencies
Maintainers
4
Versions
29
Alerts
File Explorer

Advanced tools

Socket logo

Install Socket

Detect and block malicious and high-risk dependencies

Install

node-red-contrib-model-asset-exchange

Node-RED node for Model Asset eXchange

  • 0.2.8
  • latest
  • Source
  • npm
  • Socket score

Version published
Weekly downloads
9
increased by80%
Maintainers
4
Weekly downloads
 
Created
Source

Build Status npm version

Node-RED nodes for deep learning microservices from the Model Asset eXchange, providing support for common audio, image, video, and text processing tasks.

Sample Node-RED Flow for MAX Object Detector

Getting started

To get started follow the comprehensive tutorial or complete the quick start steps listed below.

Setup

Docker installation

If you have Docker installed, you can use this Docker image to try out the examples.

Native installation
  1. Install Node-RED.

    Before you can install Node-RED, you'll need a working install of Node.js. We recommend the use of Node.js LTS 8.x or 10.x, as Node-RED no longer supports Node.js 6.x or earlier.

  2. Run the following command in your Node-RED user directory - typically ~/.node-red to install the node-red-contrib-model-asset-exchange module:

     $ cd ~/.node-red
     $ npm install node-red-contrib-model-asset-exchange
    

You can also install the module in the Node-RED editor. Choose > Manage palette > Install and enter model-asset as the search term.

  1. Launch Node-RED and open the displayed URL in a web browser to access the flow editor.

     $ node-red
       ...
       ... - [info] Server now running at http://127.0.0.1:1880/
     
    
  2. The nodes are displayed in the palette under the Model-Asset-eXchange category.

Explore the sample flows

The node-red-contrib-model-asset-exchange module includes a couple of example flows to get you started. To import the flows into the workspace:

  1. In the Node-RED editor open > Import > Examples > model asset exchange.

  2. Select one of the sub-directories to choose between the basic flows in getting started, some more complex examples in beyond the basics, or some flows designed to run on the raspberry pi.

  3. Choose a flow.

    import sample flows

Note: The flows utilize nodes from the node-red-contrib-browser-util and node-red-contrib-image-output modules. See the flow description for more details on which nodes are used in a particular example.

You can deploy and run these flows as is. The deep learning nodes in these flows have been pre-configured (service: cloud) to connect to hosted evaluation instances of the deep learning microservices.

Use the nodes in your own flows

Microservice evaluation instances are not suitable for production use. We recommend running microservice instance(s) on your local machine or in the cloud using IBM Cloud Kubernetes, Azure Kubernetes Service, or Google Kubernetes Engine:

  1. Deploy the deep learning microservice in the desired environment.
  2. Take note of its URL (e.g. http://localhost:5000)
  3. Add the corresponding deep learning node to your canvas.
  4. Open the node properties.
  5. Add a service entry for the URL and assign it a unique name.

configure microservice connectivity

Supported application domains

This Node-RED node module supports the following application domains:

Note: file inject node in node-red-contrib-browser-utils is useful to test these nodes.

License

Apache-2.0

Keywords

FAQs

Package last updated on 08 May 2020

Did you know?

Socket

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts

SocketSocket SOC 2 Logo

Product

  • Package Alerts
  • Integrations
  • Docs
  • Pricing
  • FAQ
  • Roadmap
  • Changelog

Packages

npm

Stay in touch

Get open source security insights delivered straight into your inbox.


  • Terms
  • Privacy
  • Security

Made with ⚡️ by Socket Inc