Use Decision Optimization to plan your diet with ibm-watsonx-ai¶
This notebook facilitates Decision Optimization and watsonx.ai Runtime services. It contains steps and code to work with ibm-watsonx-ai library available in PyPI repository. It also introduces commands for getting model and training data, persisting model, deploying model and scoring it.
Some familiarity with Python is helpful.
Learning goals¶
The learning goals of this notebook are:
- Load a Decision Optimization model into a watsonx.ai Runtime repository.
- Prepare data for training and evaluation.
- Create a watsonx.ai Runtime job.
- Persist a Decision Optimization model in a watsonx.ai Runtime repository.
- Deploy a model for batch scoring using watsonx.ai API.
Contents¶
This notebook contains the following parts:
1. Set up the environment¶
Before you use the sample code in this notebook, you must:
- create a watsonx.ai Runtime Service instance. A free plan is offered and information about how to create the instance can be found at https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-overview.html?context=cpdaas.
Install and then import the watsonx.ai client library.
Note: ibm-watsonx-ai documentation can be found here.
In [1]:
# Install watsonx.ai client API
!pip install ibm-watsonx-ai
Requirement already satisfied: ibm-watsonx-ai in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (1.3.17) Requirement already satisfied: requests in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from ibm-watsonx-ai) (2.32.2) Requirement already satisfied: httpx<0.29,>=0.27 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from ibm-watsonx-ai) (0.28.1) Requirement already satisfied: urllib3 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from ibm-watsonx-ai) (1.26.19) Requirement already satisfied: pandas<2.3.0,>=0.24.2 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from ibm-watsonx-ai) (2.1.4) Requirement already satisfied: certifi in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from ibm-watsonx-ai) (2025.4.26) Requirement already satisfied: lomond in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from ibm-watsonx-ai) (0.3.3) Requirement already satisfied: tabulate in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from ibm-watsonx-ai) (0.8.10) Requirement already satisfied: packaging in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from ibm-watsonx-ai) (23.2) Requirement already satisfied: ibm-cos-sdk<2.15.0,>=2.12.0 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from ibm-watsonx-ai) (2.13.4) Requirement already satisfied: anyio in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from httpx<0.29,>=0.27->ibm-watsonx-ai) (3.5.0) Requirement already satisfied: httpcore==1.* in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from httpx<0.29,>=0.27->ibm-watsonx-ai) (1.0.9) Requirement already satisfied: idna in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from httpx<0.29,>=0.27->ibm-watsonx-ai) (3.7) Requirement already satisfied: h11>=0.16 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from httpcore==1.*->httpx<0.29,>=0.27->ibm-watsonx-ai) (0.16.0) Requirement already satisfied: ibm-cos-sdk-core==2.13.4 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from ibm-cos-sdk<2.15.0,>=2.12.0->ibm-watsonx-ai) (2.13.4) Requirement already satisfied: ibm-cos-sdk-s3transfer==2.13.4 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from ibm-cos-sdk<2.15.0,>=2.12.0->ibm-watsonx-ai) (2.13.4) Requirement already satisfied: jmespath<=1.0.1,>=0.10.0 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from ibm-cos-sdk<2.15.0,>=2.12.0->ibm-watsonx-ai) (1.0.1) Requirement already satisfied: python-dateutil<3.0.0,>=2.8.2 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from ibm-cos-sdk-core==2.13.4->ibm-cos-sdk<2.15.0,>=2.12.0->ibm-watsonx-ai) (2.8.2) Requirement already satisfied: numpy<2,>=1.23.2 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from pandas<2.3.0,>=0.24.2->ibm-watsonx-ai) (1.26.4) Requirement already satisfied: pytz>=2020.1 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from pandas<2.3.0,>=0.24.2->ibm-watsonx-ai) (2024.1) Requirement already satisfied: tzdata>=2022.1 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from pandas<2.3.0,>=0.24.2->ibm-watsonx-ai) (2023.3) Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from requests->ibm-watsonx-ai) (2.0.4) Requirement already satisfied: six>=1.10.0 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from lomond->ibm-watsonx-ai) (1.16.0) Requirement already satisfied: sniffio>=1.1 in /opt/conda/envs/Python-RT24.1/lib/python3.11/site-packages (from anyio->httpx<0.29,>=0.27->ibm-watsonx-ai) (1.3.0)
In [2]:
!pip install -U wget
Collecting wget Downloading wget-3.2.zip (10 kB) Preparing metadata (setup.py) ... done Building wheels for collected packages: wget Building wheel for wget (setup.py) ... done Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9657 sha256=cb3c19dce8429ae4338d99049d6725168094a0bb13f5fafb4584dc45856ba79b Stored in directory: /tmp/wsuser/.cache/pip/wheels/40/b3/0f/a40dbd1c6861731779f62cc4babcb234387e11d697df70ee97 Successfully built wget Installing collected packages: wget Successfully installed wget-3.2
In [3]:
from ibm_watsonx_ai import APIClient
from ibm_watsonx_ai import Credentials
In [4]:
# Instantiate a client using credentials
credentials = Credentials(
api_key = "<API_key>",
url = "<instance_url>"
)
client = APIClient(credentials)
Working with spaces¶
First of all, you need to create a space that will be used for your work. If you do not have space already created, you can use Deployment Spaces Dashboard to create one.
- Click New deployment space
- Create an empty space
- Select Cloud Object Storage
- Select watsonx.ai Runtime instance and press Create
Tip: You can also use SDK to prepare the space for your work. More information can be found here.
In [5]:
# Find the space ID
space_name = "<space_name>"
space_id = [x['metadata']['id'] for x in client.spaces.get_details()['resources'] if x['entity']['name'] == space_name][0]
client = APIClient(credentials, space_id = space_id)
2. Download externally created Decision Optimization model and data¶
In this section, you will download externally created Decision Optimization model and data used for training it.
Action: Get your Decision Optimization model.
In [6]:
import os
import wget
model_path = 'do-model.tar.gz'
if not os.path.isfile(model_path):
wget.download("https://github.com/IBM/watson-machine-learning-samples/raw/master/cloud/models/decision_optimization/do-model.tar.gz")
3. Persist externally created Decision Optimization model¶
In this section, you will learn how to store your model in watsonx.ai Runtime repository by using the watsonx.ai Client.
3.1 Publish model¶
Publish model in watsonx.ai Runtime repository on Cloud.¶
Define model name, autor name and email.
Get software specification for Decision Optimization model
In [7]:
sofware_spec_id = client.software_specifications.get_id_by_name("do_22.1")
Output data schema for storing model in watsonx.ai Runtime repository
In [8]:
output_data_schema = [{'id': 'stest',
'type': 'list',
'fields': [{'name': 'age', 'type': 'float'},
{'name': 'sex', 'type': 'float'},
{'name': 'cp', 'type': 'float'},
{'name': 'restbp', 'type': 'float'},
{'name': 'chol', 'type': 'float'},
{'name': 'fbs', 'type': 'float'},
{'name': 'restecg', 'type': 'float'},
{'name': 'thalach', 'type': 'float'},
{'name': 'exang', 'type': 'float'},
{'name': 'oldpeak', 'type': 'float'},
{'name': 'slope', 'type': 'float'},
{'name': 'ca', 'type': 'float'},
{'name': 'thal', 'type': 'float'}]
},
{'id': 'teste2',
'type': 'test',
'fields': [{'name': 'age', 'type': 'float'},
{'name': 'sex', 'type': 'float'},
{'name': 'cp', 'type': 'float'},
{'name': 'restbp', 'type': 'float'},
{'name': 'chol', 'type': 'float'},
{'name': 'fbs', 'type': 'float'},
{'name': 'restecg', 'type': 'float'},
{'name': 'thalach', 'type': 'float'},
{'name': 'exang', 'type': 'float'},
{'name': 'oldpeak', 'type': 'float'},
{'name': 'slope', 'type': 'float'},
{'name': 'ca', 'type': 'float'},
{'name': 'thal', 'type': 'float'}]
}]
In [9]:
model_meta_props = {
client.repository.ModelMetaNames.NAME: "LOCALLY created DO model",
client.repository.ModelMetaNames.TYPE: "do-docplex_22.1",
client.repository.ModelMetaNames.SOFTWARE_SPEC_ID: sofware_spec_id,
client.repository.ModelMetaNames.OUTPUT_DATA_SCHEMA: output_data_schema
}
published_model = client.repository.store_model(model=model_path, meta_props=model_meta_props)
Note: You can see that model is successfully stored in watsonx.ai Runtime Service.
3.2 Get model details¶
In [10]:
import json
published_model_uid = client.repository.get_model_id(published_model)
model_details = client.repository.get_details(published_model_uid)
print(json.dumps(model_details, indent=2))
{
"entity": {
"hybrid_pipeline_software_specs": [],
"schemas": {
"input": [],
"output": [
{
"fields": [
{
"name": "age",
"type": "float"
},
{
"name": "sex",
"type": "float"
},
{
"name": "cp",
"type": "float"
},
{
"name": "restbp",
"type": "float"
},
{
"name": "chol",
"type": "float"
},
{
"name": "fbs",
"type": "float"
},
{
"name": "restecg",
"type": "float"
},
{
"name": "thalach",
"type": "float"
},
{
"name": "exang",
"type": "float"
},
{
"name": "oldpeak",
"type": "float"
},
{
"name": "slope",
"type": "float"
},
{
"name": "ca",
"type": "float"
},
{
"name": "thal",
"type": "float"
}
],
"id": "stest",
"type": "list"
},
{
"fields": [
{
"name": "age",
"type": "float"
},
{
"name": "sex",
"type": "float"
},
{
"name": "cp",
"type": "float"
},
{
"name": "restbp",
"type": "float"
},
{
"name": "chol",
"type": "float"
},
{
"name": "fbs",
"type": "float"
},
{
"name": "restecg",
"type": "float"
},
{
"name": "thalach",
"type": "float"
},
{
"name": "exang",
"type": "float"
},
{
"name": "oldpeak",
"type": "float"
},
{
"name": "slope",
"type": "float"
},
{
"name": "ca",
"type": "float"
},
{
"name": "thal",
"type": "float"
}
],
"id": "teste2",
"type": "test"
}
]
},
"software_spec": {
"id": "e51999ba-6452-5f1f-8287-17228b88b652",
"name": "do_22.1"
},
"type": "do-docplex_22.1"
},
"metadata": {
"created_at": "2025-05-09T14:39:53.512Z",
"id": "0ff957af-fbea-455f-8d1a-b808081684e7",
"modified_at": "2025-05-09T14:39:56.774Z",
"name": "LOCALLY created DO model",
"owner": "IBMid-270006YQEG",
"resource_key": "af488239-afc0-4f94-ad90-178e0fb65ab7",
"space_id": "8ee32d8c-6a63-4212-ae42-c8eebe220eb3"
},
"system": {
"warnings": []
}
}
3.3 Get all models¶
In [11]:
client.repository.list_models()
Out[11]:
| ID | NAME | CREATED | TYPE | SPEC_STATE | SPEC_REPLACEMENT | |
|---|---|---|---|---|---|---|
| 0 | 0ff957af-fbea-455f-8d1a-b808081684e7 | LOCALLY created DO model | 2025-05-09T14:39:53.512Z | do-docplex_22.1 | supported |
4. Deploy in a Cloud¶
In this section you will learn how to create batch deployment to create job using the watsonx.ai Client.
You can use commands bellow to create batch deployment for stored model (web service).
4.1 Create model deployment¶
In [12]:
meta_data = {
client.deployments.ConfigurationMetaNames.NAME: "deployment_DO",
client.deployments.ConfigurationMetaNames.BATCH: {},
client.deployments.ConfigurationMetaNames.HARDWARE_SPEC: {"name": "S", "num_nodes": 1}
}
deployment_details = client.deployments.create(published_model_uid, meta_props=meta_data)
###################################################################################### Synchronous deployment creation for id: '0ff957af-fbea-455f-8d1a-b808081684e7' started ###################################################################################### ready. ----------------------------------------------------------------------------------------------- Successfully finished deployment creation, deployment_id='5a1d56e4-b672-4236-b8a5-c9f7db0d9e8d' -----------------------------------------------------------------------------------------------
Note: Here we use deployment url saved in published_model object. In next section, we show how to retrive deployment url from watsonx.ai Runtime instance.
In [13]:
deployment_uid = client.deployments.get_id(deployment_details)
Now, you can list all deployments.
In [14]:
client.deployments.list()
Out[14]:
| ID | NAME | STATE | CREATED | ARTIFACT_TYPE | SPEC_STATE | SPEC_REPLACEMENT | |
|---|---|---|---|---|---|---|---|
| 0 | 5a1d56e4-b672-4236-b8a5-c9f7db0d9e8d | deployment_DO | ready | 2025-05-09T14:40:00.702Z | do | supported |
4.2 Get deployment details¶
In [15]:
client.deployments.get_details(deployment_uid)
Out[15]:
{'entity': {'asset': {'id': '0ff957af-fbea-455f-8d1a-b808081684e7'},
'batch': {},
'chat_enabled': False,
'custom': {},
'deployed_asset_type': 'do',
'hardware_spec': {'id': 'e7ed1d6c-2e89-42d7-aed5-863b972c1d2b',
'name': 'S',
'num_nodes': 1},
'name': 'deployment_DO',
'space_id': '8ee32d8c-6a63-4212-ae42-c8eebe220eb3',
'status': {'state': 'ready'}},
'metadata': {'created_at': '2025-05-09T14:40:00.702Z',
'id': '5a1d56e4-b672-4236-b8a5-c9f7db0d9e8d',
'modified_at': '2025-05-09T14:40:00.702Z',
'name': 'deployment_DO',
'owner': 'IBMid-270006YQEG',
'space_id': '8ee32d8c-6a63-4212-ae42-c8eebe220eb3'}}
Prepare test data
In [16]:
# Import pandas library
import pandas as pd
# Initialize list of lists
diet_food = pd.DataFrame([ ["Roasted Chicken", 0.84, 0, 10],
["Spaghetti W/ Sauce", 0.78, 0, 10],
["Tomato,Red,Ripe,Raw", 0.27, 0, 10],
["Apple,Raw,W/Skin", 0.24, 0, 10],
["Grapes", 0.32, 0, 10],
["Chocolate Chip Cookies", 0.03, 0, 10],
["Lowfat Milk", 0.23, 0, 10],
["Raisin Brn", 0.34, 0, 10],
["Hotdog", 0.31, 0, 10]] , columns = ["name","unit_cost","qmin","qmax"])
diet_food_nutrients = pd.DataFrame([
["Spaghetti W/ Sauce", 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2],
["Roasted Chicken", 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2],
["Tomato,Red,Ripe,Raw", 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1],
["Apple,Raw,W/Skin", 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3],
["Grapes", 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2],
["Chocolate Chip Cookies", 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9],
["Lowfat Milk", 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1],
["Raisin Brn", 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4],
["Hotdog", 242.1, 23.5, 2.3, 0, 0, 18, 10.4 ]
] , columns = ["Food","Calories","Calcium","Iron","Vit_A","Dietary_Fiber","Carbohydrates","Protein"])
diet_nutrients = pd.DataFrame([
["Calories", 2000, 2500],
["Calcium", 800, 1600],
["Iron", 10, 30],
["Vit_A", 5000, 50000],
["Dietary_Fiber", 25, 100],
["Carbohydrates", 0, 300],
["Protein", 50, 100]
], columns = ["name","qmin","qmax"])
In [17]:
job_payload_ref = {
client.deployments.DecisionOptimizationMetaNames.INPUT_DATA: [
{
"id": "diet_food.csv",
"values": diet_food
},
{
"id": "diet_food_nutrients.csv",
"values": diet_food_nutrients
},
{
"id": "diet_nutrients.csv",
"values": diet_nutrients
}
],
client.deployments.DecisionOptimizationMetaNames.OUTPUT_DATA: [
{
"id": ".*.csv"
}
]
}
Create job using watsonx.ai client
In [18]:
job = client.deployments.create_job(deployment_uid, meta_props=job_payload_ref)
Checking created job status and calculated KPI.
In [19]:
import time
job_id = client.deployments.get_job_id(job)
elapsed_time = 0
while client.deployments.get_job_status(job_id).get('state') != 'completed' and elapsed_time < 300:
elapsed_time += 10
time.sleep(10)
if client.deployments.get_job_status(job_id).get('state') == 'completed':
job_details_do = client.deployments.get_job_details(job_id)
kpi = job_details_do['entity']['decision_optimization']['solve_state']['details']['KPI.Total Calories']
print(f"KPI: {kpi}")
else:
print("Job hasn't completed successfully in 5 minutes.")
KPI: 2000.0
6. Clean up¶
If you want to clean up all created assets:
- experiments
- trainings
- pipelines
- model definitions
- models
- functions
- deployments
follow up this sample notebook.
7. Summary and next steps¶
You've successfully completed this notebook!
You've learned how to:
- work with the watsonx.ai client
- upload your model on watsonx.ai Runtime
- create a deployment
- create and monitor a job with inline data for your deployed model
Check out our online documentation for more samples, tutorials and documentation:
Authors¶
Wojciech Jargielo, Software Engineer
Copyright © 2020-2025 IBM. This notebook and its source code are released under the terms of the MIT License.