Real-time serving

import aleialib
aleialib.user_services.invoke("post", "/add-numbers","<service id>",{"num1": 2,"num2": 5})

import requests
from requests_aws4auth import AWS4Auth

# make changes here according to Allonia APIs
payload = {'num1': 2, 'num2': 3}
path = '/user-services/invoke/<service id>/add-numbers'
url = 'https://api.k.prod.infra.aleia.com' + path
service = 's3'
region = ''

# access_key and secret_key can be created from IAM api
# https://api.k.prod.infra.aleia.com/iam/documentation/#/Tokens/post_tokens
awsauth = AWS4Auth('<id>', '<token>', region, service)

r = requests.post(url, auth=awsauth, json=payload)
print(r.text)

# routes to be written by users
@user_services_routes.route("/learn", methods=["POST"])
def learn():
    """Learns using AleiaModel. (1)

    One can specify which model to use, with which version, the data to learn on
    and which kwargs should be given to the prediction pipeline.

    Using this route instead of learning in a notebook is handy for heavy models
    whose learning takes hours. You can then use your notebook for something
    else while other pods are dedicated to your model's learning. Use a
    healt check of your model in the notebook though, to make sure the pipeline
    is correct.

    ---
    post:
      tags: (2)
        - Learner
      description: Learns using AleiaModel. (3)
      requestBody:
        content: (4)
          application/json:
            schema:
              type: object
              properties:
                model_name:
                  type: string
                  required: true
                raw_set:
                  $ref: '#/components/schemas/AleiaModelPathDescription'
                derived_set:
                  $ref: '#/components/schemas/AleiaModelPathDescription'
                train_set:
                  $ref: '#/components/schemas/AleiaModelPathDescription'
                validation_set:
                  $ref: '#/components/schemas/AleiaModelPathDescription'
                test_set:
                  $ref: '#/components/schemas/AleiaModelPathDescription'
                revision:
                  type: integer
                  required: false
                  example: -1
                  default: -1
                  description: 0 is invalid, None or -1 is the latest revision.
                kwargs:
                  type: object
                  required: false
                  properties:
                    reshape_x:
                      type: array
                      required: false
                      items:
                        type: integer
                      example: [-1, 1]
                    reshape_y:
                      type: array
                      required: false
                      items:
                        type: integer
                      example: [-1, 1]
                    model_kwargs:
                      type: object
                      required: false
                    feature_engineering_kwargs:
                      type: object
                      required: false
                    train_val_test_split_kwargs:
                      type: object
                      required: false
                    fit_kwargs:
                      type: object
                      required: false
                    predict_for_metrics_kwargs:
                      type: object
                      required: false
                    metrics_kwargs:
                      type: object
                      required: false
                save:
                  type: boolean
                  default: true
                  required: false

      responses: (5)
        200:
          description: Validation and Test metrics as dicts.
          content:
            application/json:
              schema:
                type: object
                properties:
                  validation:
                    type: object
                  test:
                    type: object

        400:
          description: Returns an error message
    """
    model, request_json, save = get_model()
    for path_name in request_json:
        if not path_name.endswith("_set"):
            continue
        handle_path(model, path_name, request_json)

    metrics = model.learn(**request_json.get("kwargs", {}))

    if save:
        model.save()
    model.close()

    return make_response({"validation": metrics[0], "test": metrics[1]})