Invoking a Model Deployment

After a model deployment is in an active lifecycleState, the predict endpoint can successfully receive requests made by clients. Invoking a model deployment means that you can pass feature vectors or data samples to the predict endpoint, and then the model returns predictions for those data samples.

From your model deployment detail page, click Invoking Your Model. The following details are displayed:

  • The model HTTP endpoint.
  • Sample code that enables you to invoke the model endpoint using the OCI CLI. Alternatively, you could use the OCI Python, and Java SDKs to invoke the model with the provide code sample.
  • The payload size limit is 10 MB.

  • The timeout on invoking a model is 60 seconds for HTTP calls.

Use the sample code to invoke your model deployment.

Invoking a model deployment calls the predict endpoint of the model deployment URI. This endpoint takes sample data as input and is processed using the predict() function in the score.py model artifact file. The sample data is usually in JSON format though can be in other formats. Processing means that the sample data could be transformed then passed to a models inference method. The models can generate predictions that can be processed before being returned back to the client.

The API responses are:

HTTP Status Code Description

200 Success

{
  "data": {
    "prediction": [
      "virginica"
    ]
  },
  "headers": {
    "content-length": "28",
    "content-type": "application/json",
    "opc-request-id": "
  },
  "status": "200 OK"
}

Success.

404

Not Found or unauthorized.

413

The payload size limit is 10 MB.

429

Too Many Requests.

LB bandwidth limit exceeded

Consider increasing the provisioned load balancer bandwidth to avoid these errors by editing the model deployment.

Tenancy request-rate limit exceeded

Maximum number of requests per second per tenancy is set to 150.

If you are consistently receiving error messages after increasing the LB bandwidth, use the OCI Console to submit a support ticket for your tenancy. Include the following details in the ticket.

  • Describe your issue with the error message that occurred, and indicate the new request per second needed for your tenancy.

  • Indicate that it is a minor loss of service.
  • Indicate Analytics & AI and Data Science.

  • Indicate that the issue is creating and managing models.

500

Internal Server Error.

  • Timeout

  • score.py file prediction returning an exception

There is a 60 second timeout for the /predict endpoint. You can't customize this timeout value.

503

The model server is unavailable.

Invoking with the OCI Python SDK

This example code is a reference to help you invoke your model deployment:

import requests
import oci
from oci.signer import Signer
import json
 
# model deployment endpoint. Here we assume that the notebook region is the same as the region where the model deployment occurs.
# Alternatively you can also go in the details page of your model deployment in the OCI console. Under "Invoke Your Model", you will find the HTTP endpoint
# of your model.
endpoint = <your-model-deployment-uri>
# your payload:
input_data = <your-json-payload-str>
 
if using_rps: # using resource principal:    
    auth = oci.auth.signers.get_resource_principals_signer()
else: # using config + key:
    config = oci.config.from_file("~/.oci/config") # replace with the location of your oci config file
    auth = Signer(
        tenancy=config['tenancy'],
        user=config['user'],
        fingerprint=config['fingerprint'],
        private_key_file_location=config['key_file'],
        pass_phrase=config['pass_phrase'])
 
# post request to model endpoint:
response = requests.post(endpoint, json=input_data, auth=auth)
 
# Check the response status. Success should be an HTTP 200 status code
assert response.status_code == 200, "Request made to the model predict endpoint was unsuccessful"
 
# print the model predictions. Assuming the model returns a JSON object.
print(json.loads(response.content)) 

Invoking with the OCI CLI

The OCI-CLI is included in the OCI Cloud Shell environment and is preauthenticated. This example invokes a model deployment with the CLI:

oci raw-request --http-method POST --target-uri
<model-deployment-url>/predict --request-body '{"data": "data"}'