vetiver (version 0.2.7)

vetiver_deploy_sagemaker: Deploy a vetiver model API to Amazon SageMaker

Description

Use vetiver_deploy_sagemaker() to deploy a vetiver_model() that has been versioned and stored via vetiver_pin_write() as a Plumber API on Amazon SageMaker.

Usage

vetiver_deploy_sagemaker(
  board,
  name,
  instance_type,
  ...,
  predict_args = list(),
  docker_args = list(),
  build_args = list(),
  endpoint_args = list(),
  repo_name = glue("vetiver-sagemaker-{name}")
)

Value

The deployed vetiver_endpoint_sagemaker().

Arguments

board

An AWS S3 board created with pins::board_s3(). This board must be in the correct region for your SageMaker instance.

name

Pin name.

instance_type

Type of EC2 instance to use; see Amazon SageMaker pricing.

...

Not currently used.

predict_args

A list of optional arguments passed to vetiver_api() such as the prediction type.

docker_args

A list of optional arguments passed to vetiver_write_docker() such as the lockfile name or whether to use rspm. Do not pass additional_pkgs here, as this function uses additional_pkgs = required_pkgs(board).

build_args

A list of optional arguments passed to vetiver_sm_build() such as the model version or the compute_type.

endpoint_args

A list of optional arguments passed to vetiver_sm_endpoint() such as accelerator_type or data_capture_config.

repo_name

The name for the AWS ECR repository to store the model.

Details

This function stores your model deployment image in the same bucket used by board.

The function vetiver_deploy_sagemaker() uses:

• vetiver_sm_build() to build and push a Docker image to ECR,

• vetiver_sm_model() to create a SageMaker model, and

• vetiver_sm_endpoint() to deploy a SageMaker model endpoint.

These modular functions are available for more advanced use cases.

If you are working locally, you will likely need to explicitly set up your execution role to work correctly. Check out "Execution role requirements" in the smdocker documentation, and especially note that the bucket containing your vetiver model needs to be added as a resource in your IAM role policy.

See Also

vetiver_sm_build(), vetiver_sm_model(), vetiver_sm_endpoint()

Examples

if (FALSE) {
library(pins)
b <- board_s3(bucket = "my-existing-bucket")
cars_lm <- lm(mpg ~ ., data = mtcars)
v <- vetiver_model(cars_lm, "cars_linear")
vetiver_pin_write(b, v)

endpoint <- vetiver_deploy_sagemaker(
    board = b,
    name = "cars_linear",
    instance_type = "ml.t2.medium",
    predict_args = list(type = "class", debug = TRUE)
)
}