AzureVision (version 1.0.2)

publish_model: Publish, export and unpublish a Custom Vision model iteration

Description

Publish, export and unpublish a Custom Vision model iteration

Usage

publish_model(model, name, prediction_resource)
unpublish_model(model, confirm = TRUE)
export_model(model, format, destfile = basename(httr::parse_url(dl_link)$path))
list_model_exports(model)

Arguments

model

A Custom Vision model iteration object.

name

For publish_model, the name to assign to the published model on the prediction endpoint.

prediction_resource

For publish_model, the Custom Vision prediction resource to publish to. This can either be a string containing the Azure resource ID, or an AzureRMR resource object.

confirm

For unpublish_model, whether to ask for confirmation first.

format

For export_model, the format to export to. See below for supported formats.

destfile

For export_model, the destination file for downloading. Set this to NULL to skip downloading.

Value

export_model returns the URL of the exported file, invisibly if it was downloaded.

list_model_exports returns a data frame detailing the formats the current model has been exported to, along with their download URLs.

Details

Publishing a model makes it available to clients as a predictive service. Exporting a model serialises it to a file of the given format in Azure storage, which can then be downloaded. Each iteration of the model can be published or exported separately.

The format argument to export_model can be one of the following. Note that exporting a model requires that the project was created with support for it.

• "onnx": ONNX 1.2

• "coreml": CoreML, for iOS 11 devices

• "tensorflow": TensorFlow

• "tensorflow lite": TensorFlow Lite for Android devices

• "linux docker", "windows docker", "arm docker": A Docker image for the given platform (Raspberry Pi 3 in the case of ARM)

• "vaidk": Vision AI Development Kit

See Also

train_model, get_model, customvision_predictive_service, predict.classification_service, predict.object_detection_service

Examples

# NOT RUN {
endp <- customvision_training_endpoint(url="endpoint_url", key="key")
myproj <- get_project(endp, "myproject")
mod <- get_model(myproj)

export_model(mod, "tensorflow", download=FALSE)
export_model(mod, "onnx", destfile="onnx.zip")

rg <- AzureRMR::get_azure_login("yourtenant")$
    get_subscription("sub_id")$
    get_resource_group("rgname")

pred_res <- rg$get_cognitive_service("mycustvis_prediction")
publish_model(mod, "mypublishedmod", pred_res)

unpublish_model(mod)

# }