vetiver_deploy_sagemaker: Deploy a vetiver model API to Amazon SageMaker
In tidymodels/vetiver: Version, Share, Deploy, and Monitor Models

vetiver_deploy_sagemaker

R Documentation

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}")
)

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.

Value

The deployed vetiver_endpoint_sagemaker().

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)
)
}

tidymodels/vetiver documentation built on Oct. 15, 2024, 4:16 p.m.