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).
post
https://studio.edgeimpulse.com/v1
/api/{projectId}/jobs/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.
Parameters
Path
projectId*
integer
Project ID
Body
Example
Responses
200: OK
OK
cURL
Python
Node.js
curl --request POST \
--url https://studio.edgeimpulse.com/v1/api/{projectId}/jobs/deploy-pretrained-model \
--header 'content-type: application/json' \
--header 'x-jwt-token: REPLACE_KEY_VALUE' \
--data '{"modelFileBase64":"string","modelFileType":"tflite","deploymentType":"string","engine":"tflite","modelInfo":{"input":{},"model":{}}}'
import http.client
conn = http.client.HTTPSConnection("studio.edgeimpulse.com")
payload = "{\"modelFileBase64\":\"string\",\"modelFileType\":\"tflite\",\"deploymentType\":\"string\",\"engine\":\"tflite\",\"modelInfo\":{\"input\":{},\"model\":{}}}"
headers = {
'content-type': "application/json",
'x-jwt-token': "REPLACE_KEY_VALUE"
}
conn.request("POST", "/v1/api/{projectId}/jobs/deploy-pretrained-model", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
const request = require('request');
const options = {
method: 'POST',
url: 'https://studio.edgeimpulse.com/v1/api/{projectId}/jobs/deploy-pretrained-model',
headers: {'content-type': 'application/json', 'x-jwt-token': 'REPLACE_KEY_VALUE'},
body: {
modelFileBase64: 'string',
modelFileType: 'tflite',
deploymentType: 'string',
engine: 'tflite',
modelInfo: {input: {}, model: {}}
},
json: true
};
request(options, function (error, response, body) {
if (error) throw new Error(error);
console.log(body);
});
Last modified 1mo ago