Deploy pretrained model

Deploy pretrained model

Takes in a TFLite file and builds the model and SDK. Updates are streamed over the websocket API (or can be retrieved through the /stdout endpoint). Use getProfileTfliteJobResult to get the results when the job is completed.

POSThttps://studio.edgeimpulse.com/v1/api/{projectId}/jobs/deploy-pretrained-model

Path parameters

projectId*integer

Project ID

Query parameters

Body

application/json

modelFileBase64*string

A base64 encoded pretrained model

modelFileType*enum

tfliteonnxsaved_modellgbmxgboostpickle

deploymentType*string

The name of the built target. You can find this by listing all deployment targets through listDeploymentTargetsForProject (via GET /v1/api/{projectId}/deployment/targets) and see the format type.

engineDeploymentTargetEngine (enum)

tflitetflite-eontflite-eon-ram-optimizedtensorrttensaiflowdrp-aitidlakidasyntiantmemryxneoxethos-linux

modelInfo*object

representativeFeaturesBase64string

A base64 encoded .npy file containing the features from your validation set (optional for onnx and saved_model) - used to quantize your model.

deployModelTypeenum

int8float32

useConverterenum

Optional, use a specific converter (only for ONNX models).

onnx-tfonnx2tf

Response

200

OK

Body

application/json

success*boolean

Whether the operation succeeded

errorstring

Optional error description (set if 'success' was false)

id*integer

Job identifier. Status updates will include this identifier.

Example: 12873488112

Request

JavaScriptCurlPython

const response = await fetch('https://studio.edgeimpulse.com/v1/api/{projectId}/jobs/deploy-pretrained-model', {
    method: 'POST',
    headers: {
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      "modelFileBase64": "text",
      "modelFileType": "tflite",
      "deploymentType": "text",
      "modelInfo": {
        "input": {
          "inputType": "time-series",
          "frequencyHz": 0,
          "windowLengthMs": 0
        },
        "model": {
          "modelType": "classification",
          "labels": [
            "text"
          ]
        }
      }
    }),
});
const data = await response.json();

Response

200

{
  "success": false,
  "error": "text",
  "id": 12873488112
}