Base Image for Node.js-based solutions. Node.js Base Image is an auxiliary solution that doesn’t work standalone. Specific orders placed through GUI may automatically add required base images. If you manually add Node.js Base Image to an order through CLI, ensure you use it with a compatible solution. Refer to the <a href="https://docs.superprotocol.com/developers/offers" target="_blank">documentation</a> for guides and examples.

Node.js Base Image

<a href="https://github.com/Super-Protocol/solutions" target="_blank">SP Solutions</a>

Super Protocol

Client side for tunnel deployment of Node.js-based applications. Tunnel clients run web servers while hiding from malicious attacks behind Tunnel Servers. This Tunnel Client is for Node.js-based dynamic web content. You can manually add this solution to an order through CLI. Ensure you use compatible Data. Also, you will need at least one Tunnel Server up and running. Refer to the <a href="https://docs.superprotocol.com/developers/deployment_guides/tunnels/" target="_blank">documentation</a> for guides and examples.

Tunnel Client: Dynamic Content

Text Generation Web UI

Client side for tunnel deployment of the Super Chat app. Tunnel clients run web servers while hiding from malicious attacks behind Tunnel Servers. This Tunnel Client is pre-packed with the Super Chat content. Tunnels Launcher deploys this solution automatically if you add Super Chat Config to the order as data. You can also manually add this solution to an order through CLI. Note that you will need at least one Tunnel Server up and running. Refer to the <a href="https://docs.superprotocol.com/developers/offers" target="_blank">documentation</a> for guides and examples.

Tunnel Client: Super Chat

Client side for tunnel deployment of static web content. Tunnel clients run web servers while hiding from malicious attacks behind Tunnel Servers. This Tunnel Client is for static web content. Tunnels Launcher deploys this solution automatically if you add Demo Static Website to the order as data. You can also manually add this solution to an order through CLI. Ensure you use compatible Data. Also, you will need at least one Tunnel Server up and running. Refer to the <a href="https://docs.superprotocol.com/developers/offers" target="_blank">documentation</a> for guides and examples.

Tunnel Client: Static Web Content

Pre-configured tunnel provisioner deploying two Tunnel Clients and two Tunnel Servers The Tunnels Launcher solution is compatible with the Super Chat Config and Demo Static Website data offers. It is also possible to deploy custom websites. Refer to the <a href="https://docs.superprotocol.com/developers/offers/launcher" target="_blank">documentation</a> for guides and examples.

Tunnels Launcher

Server side for tunnel deployment. Tunnel Servers are accessible from the internet via HTTPS through a browser or API. They are responsible for routing external requests to the Tunnel Clients and load balancing. You need to launch at least one Tunnel Server for any tunnel deployment. The Tunnels Launcher solution starts two Tunnel Servers automatically. If you manually launch tunnels through CLI, refer to the <a href="https://docs.superprotocol.com/developers/offers" target="_blank">documentation</a> for guides and examples.

Tunnel Server

Machine-trained Python model that determines the probability that two images contain the same person. This demo solution is compatible with Face Recognition Dataset #1 and #2. t is also possible to use custom datasets. Refer to the <a href="https://docs.superprotocol.com/developers/offers/python-face" target="_blank">documentation</a> for detailed instructions.

Face Recognition

Machine-trained Python model that transcribes speech audio files into text. This demo solution is compatible with Speech Recognition Dataset #1 and #2. It is also possible to use custom datasets. Refer to the <a href="https://docs.superprotocol.com/developers/offers/python-speech" target="_blank">documentation</a> for detailed instructions.

Speech Recognition

Machine-trained Python model that recognizes and classifies objects in an image. This demo solution is compatible with Image Classification Dataset #1 and #2. It is also possible to use custom datasets. Refer to the <a href="https://docs.superprotocol.com/developers/offers/python-image" target="_blank">documentation</a> for detailed instructions.