Wallaroo MLOps API Essentials Guide: Inference Management

How to use Wallaroo MLOps Api for inferencing

Table of Contents

Deployed pipelines have their own Inference URL that accepts HTTP POST submissions.

For connections that are external to the Kubernetes cluster hosting the Wallaroo instance, model endpoints must be enabled.

HTTP Headers

The following headers are required for connecting the the Pipeline Deployment URL:

  • Authorization: This requires the JWT token in the format 'Bearer ' + token. For example:

    Authorization: Bearer abcdefg==

  • Content-Type:

    • For DataFrame formatted JSON:

      Content-Type:application/json; format=pandas-records

    • For Arrow binary files, the Content-Type is application/vnd.apache.arrow.file.

      Content-Type:application/vnd.apache.arrow.file

  • Accept

    • Accept: application/json; format=pandas-records: The inference result is returned as a JSON in pandas Record format.
    • Accept: application/vnd.apache.arrow.file: The inference result is returned as a binary in Apache Arrow format.

  • IMPORTANT NOTE: Verify that the pipeline deployed has status Running before attempting an inference.

# Retrieve the token
headers = wl.auth.auth_header()

# set Content-Type type
headers['Content-Type']='application/json; format=pandas-records'

## Inference through external URL using dataframe

# retrieve the json data to submit
data = [
    {
        "tensor":[
            1.0678324729,
            0.2177810266,
            -1.7115145262,
            0.682285721,
            1.0138553067,
            -0.4335000013,
            0.7395859437,
            -0.2882839595,
            -0.447262688,
            0.5146124988,
            0.3791316964,
            0.5190619748,
            -0.4904593222,
            1.1656456469,
            -0.9776307444,
            -0.6322198963,
            -0.6891477694,
            0.1783317857,
            0.1397992467,
            -0.3554220649,
            0.4394217877,
            1.4588397512,
            -0.3886829615,
            0.4353492889,
            1.7420053483,
            -0.4434654615,
            -0.1515747891,
            -0.2668451725,
            -1.4549617756
        ]
    }
]

# submit the request via POST, import as pandas DataFrame
response = pd.DataFrame.from_records(
    requests.post(
        deployurl, 
        json=data, 
        headers=headers)
        .json()
    )

display(response)
timeinoutcheck_failuresmetadata
01684356836285{'tensor': [1.0678324729, 0.2177810266, -1.7115145262, 0.682285721, 1.0138553067, -0.4335000013, 0.7395859437, -0.2882839595, -0.447262688, 0.5146124988, 0.3791316964, 0.5190619748, -0.4904593222, 1.1656456469, -0.9776307444, -0.6322198963, -0.6891477694, 0.1783317857, 0.1397992467, -0.3554220649, 0.4394217877, 1.4588397512, -0.3886829615, 0.4353492889, 1.7420053483, -0.4434654615, -0.1515747891, -0.2668451725, -1.4549617756]}{'dense_1': [0.0014974177]}[]{'last_model': '{"model_name":"apimodel","model_sha":"bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507"}', 'pipeline_version': '', 'elapsed': [163502, 309804]}