Install Deep Learning Model

URL:
https://<rasteranalysistools-url>/InstallDeepLearningModel
Methods:
GET
Version Introduced:
10.7

Description

Install Deep Learning Model

The InstallDeepLearningModel operation is used to install the uploaded deep learning model package (.dlpk ) from portal to the raster analysis image server. The upload model package will be unpacked and saved to the server configuration store.

Request parameters

ParameterDetails

modelPackage

(Required)

The portal item Id or URL of a deep learning model package.

Syntax: JSON object describes the input model package.

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{"itemId": <portal item id>}
{"url": <portal item url>}

f

The response format. The default response format is html .

Values: html | json

Response

When you submit a request, the task assigns a unique job ID for the transaction.

Syntax:

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{
"jobId": "<unique job identifier>",
"jobStatus": "<job status>"
}

After the initial request is submitted, you can use jobId to periodically review the status of the job and messages as described in Checking job status. Once the job has successfully completed, use jobId to retrieve the results. To track the status, you can make a request of the following form:

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https://<raster analysis tools url>/InstallDeepLearningModel/jobs/<jobId>

When the status of the job request is esriJobSucceeded , you can access the results of the analysis by making a request of the following form:

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https://<raster analysis tools url>/InstallDeepLearningModel/jobs/<jobId>/installSucceed

Example usage

Below is a sample request URL for InstallDeepLearningModel .

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https://services.myserver.com/arcgis/rest/services/System/RasterAnalysisTools/GPServer/InstallDeepLearningModel/submitJob

JSON Response example

The response returns the installSucceed parameter, which has properties for parameter name, data type, and value. The content of the value is the installed model location at the image server configuration store.

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{
   "paramName": "installSucceed",
   "dataType": "GPString",
   "value": "[resources]models\\raster\\f0e602cf29dd4674ad814773ed361fb6\\tensorflow_objectdetectionapi_tree.emd"
}

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