ecosystem-notebooks Python Package

The ecosystem-notebooks python package provides access to all of the ecosystem.Ai configuration options through a python interface.

Installation

Install ecosystem.Ai by following either the Local Installation Guide or the cloud Marketplace App Guide.

To use ecosystem-notebooks, install it using pip:

   (.venv) $ pip install ecosystem-notebooks

Connect to ecosystem.Ai using the url of your ecosystem.Ai server and a valid username and password for your server:

   from prediction import jwt_access
   auth = jwt_access.Authenticate("http://ecosystem-server:3001/api", "ecosystem_username", "ecosystem_password")

If everything is set up correctly a Login Successful. message will be displayed.

Get Started

The ecosystem-notebooks package provides two main modules: prediction and runtime. The prediction module is used to interact with the server APIs of ecosystem.Ai, while the runtime module is used to interact with the runtime APIs of ecosystem.Ai.

Here we’ll work through configuring your first deployment using a pretrained H2O.ai model using the server. Pushing the deployment to a runtime instance and testing the response received from the runtime instance.

   #Import the required ecosystem.Ai modules
   from prediction.apis import deployment_management as dm
   from prediction.apis import ecosystem_generation_engine as ge
   from prediction.apis import data_management_engine as dme
   from prediction.apis import online_learning_management as ol
   from prediction.apis import prediction_engine as pe
   from prediction.apis import worker_file_service as fs
   from prediction import jwt_access
   from runtime.apis import predictor_engine as o
   from runtime import access
   import pprint
 
   #Authenticate with the ecosystem.Ai server
   auth = jwt_access.Authenticate("http://ecosystem-server:3001/api", "ecosystem_username", "ecosystem_password")
 
   #The name and details of of the project and deployment on which you will be working.
   project_id = "demo_project"
   project_description = "Demo project for ecosystem.Ai"
   project_type = "Recommender"
   purpose = "Demo"
   project_start_date = "2024-01-01"
   project_end_date = "2024-01-31"
   data_science_lead = "ecosystem.Ai"
   data_lead = "ecosystem.Ai"
   deployment_id = "simple_model_deployment"
   deployment_description = "Simple model deployment using a pretrained H2O.ai model"
   runtime_path="http://localhost:8014"
 
   #Create a project. This set is not required if you will be working in an existing project
   dm.create_project(auth,project_id,project_description,project_type,purpose,project_start_date,project_end_date,data_science_lead,data_lead)
 
   model_configuration = define_deployment_model_configuration(models_to_load=["model_id"])
 
   #Configure the lookup to the customer feature store
   parameter_access = dm.define_deployment_parameter_access(
       auth,
       lookup_key="msisdn",
       lookup_type="int",
       database="prod_estore_gsm_recommender",
       table_collection="fs_score_all_estore_gsm_recommender_rel_1",
       datasource="mongodb",
       virtual_variables=virtual_variables
   )
 
   #Create your deployment
   version = "001"
   deployment_step = dm.create_deployment(
       auth,
       project_id=project_id,
       deployment_id=deployment_id,
       description=deployment_description,
       version=version,
       plugin_post_score_class="PostScoreBasic.java",
       plugin_pre_score_class="PrePredictCustomer.java",
       scoring_engine_path_dev=runtime_path,
       mongo_connect="mongodb://mongouser:mongopassword@ecosystem-server:54445/?authSource=admin",
       parameter_access=parameter_access,
       model_configuration=model_configuration,
   )
 
   #Push deployment and produce properties file
   push_result = ge.process_push(auth_local,deployment_step)
   if "ErrorMessage" in push_result:
       print(push_result["ErrorMessage"])
   else:
       print(push_result["properties"])
 
   #Test your deployment
   post_invocations_input = {
                               "campaign": deployment_id
                             , "subcampaign": "none"
                             , "channel": "notebooks"
                             , "customer": 12345
                             , "userid": "test"
                             , "numberoffers": 2
                             , "params": "{}"
                           }
   offer_response = o.invocations(auth_runtime, post_invocations_input)
   pp.pprint(offer_response)

Logging Intro