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A simple machine learning inference library.

Project description

Spine API

Access your ML models through HTTP requests.

It is simple, clean, and easy to use. It works with jupyter notebooks too.

POST http://your_server_ip/api/your_project/your_function => your_result


$ pip instal spine-api


1. Import the library

from spine import Connection

2. Define your inference function

def hello_function (input):
  # ...
  # do something with input
  # ...
  return output

3. Specify name and description to initialize your project

spine_connection = Connection(
  description="An inference API for my ML model",
  author="", # Optional
  link="" # Optional

4. Register your function(s)

  # ============ Optional ==================
  # Set True if you want to protect this API
  # ========================================

The function will be accessible through /api/hello_project/hello_function

5. Run

That's it! You can now communicate with your ML model through HTTP post requests.

6. Send requests

Note You have to first run JSON.stringify(input) for the request data.

const data = {
  input: JSON.stringify(YOUR_INPUT)
  // ============ Optional ==================
  // Required if the API is protected
  authToken: "xxx",
  // ========================================

Post request body to the endpoint.

const url = '';

    method: 'POST',
    body: JSON.stringify(data),
    headers: new Headers({ 'Content-Type': 'application/json' })
.then(res => res.json())
.catch(error => console.error('Error:', error))
.then(response => console.log('Success:', response));

Self hosted server


  1. Make sure your server is accessible through port 3000
  2. Have Node.js installed on your server


$ git clone
$ cd spine-api/server
$ npm install

Run server

$ node server.js

Now you can access your server through http://YOUR_SERVER_IP:3000. Copy the passcode.

Specify url and passcode For self hosted server, you'll have to specify url and passcode.

from spine import Connection
spine_connection = Connection(
  description="a demo app for my ML model",

For other steps, it's all the same.



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