Red Hat build of MicroShift

Query your AI model

You can make an inference request against the AI model server that is using the ovms-resnet50 model.

Prerequisites

MicroShift is running.
You configured the model-serving runtime.
You uploaded your AI model to MicroShift.

Procedure

Make an inference request against the model server that is using the ovms-resnet50 model by running the following command:

$ curl \
    --data-binary "@./request.json" \
    --header "Inference-Header-Content-Length: ${HEADER_LEN}" \
    "${DOMAIN}/v2/models/ovms-resnet50/infer" \
    --connect-to "${DOMAIN}::${IP}:" > response.json

Example inferencing output, saved to a response.json

{
    "model_name": "ovms-resnet50",
    "model_version": "1",
    "outputs": [{
            "name": "1463",
            "shape": [1, 1000],
            "datatype": "FP32",
            "data": [ ....... ] 
        }]
}

The contents of .outputs[0].data were omitted from the example for brevity.

Verification

To determine the model’s prediction, get the index of the highest element in the .outputs[0].data to determine the model’s predicted value by using the following Python script:
```
import json
with open('response.json') as f:
    response = json.load(f)
data = response["outputs"][0]["data"]
argmax = data.index(max(data))
print(argmax)
```
Example output
```
309
```
In this example, the element labeled 309 is the model’s response.
Validate the output against resnet’s input data, for example:
```
../../../../demos/common/static/images/bee.jpeg 309
```

Next steps

Optional. Query the AI model using other images available in the resnet input data.