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Deploy a vetiver model API to Amazon SageMaker with modular functions

Source: R/sagemaker.R
vetiver_sm_build.Rd

Use the function vetiver_deploy_sagemaker() for basic deployment on SageMaker, or these three functions together for more advanced use cases:

  • vetiver_sm_build() generates and builds a Docker image on SageMaker for a vetiver model

  • vetiver_sm_model() creates an Amazon SageMaker model

  • vetiver_sm_endpoint() deploys an Amazon SageMaker model endpoint

Usage

vetiver_sm_build(
  board,
  name,
  version = NULL,
  path = fs::dir_create(tempdir(), "vetiver"),
  predict_args = list(),
  docker_args = list(),
  repository = NULL,
  compute_type = c("BUILD_GENERAL1_SMALL", "BUILD_GENERAL1_MEDIUM",
    "BUILD_GENERAL1_LARGE", "BUILD_GENERAL1_2XLARGE"),
  role = NULL,
  bucket = NULL,
  vpc_id = NULL,
  subnet_ids = list(),
  security_group_ids = list(),
  log = TRUE,
  ...
)

vetiver_sm_model(
  image_uri,
  model_name,
  role = NULL,
  vpc_config = list(),
  enable_network_isolation = FALSE,
  tags = list()
)

vetiver_sm_endpoint(
  model_name,
  instance_type,
  endpoint_name = NULL,
  initial_instance_count = 1,
  accelerator_type = NULL,
  tags = list(),
  kms_key = NULL,
  data_capture_config = list(),
  volume_size = NULL,
  model_data_download_timeout = NULL,
  wait = TRUE
)

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.

version

Retrieve a specific version of a pin. Use pin_versions() to find out which versions are available and when they were created.

path

A path to write the Plumber file, Dockerfile, and lockfile, capturing the model's dependencies.

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).

repository

The ECR repository and tag for the image as a character. Defaults to sagemaker-studio-${domain_id}:latest.

compute_type

The CodeBuild compute type as a character. Defaults to BUILD_GENERAL1_SMALL.

role

The ARN IAM role name (as a character) to be used with:

  • CodeBuild for vetiver_sm_build()

  • the SageMaker model for vetiver_sm_model()

Defaults to the SageMaker Studio execution role.

bucket

The S3 bucket to use for sending data to CodeBuild as a character. Defaults to the SageMaker SDK default bucket.

vpc_id

ID of the VPC that will host the CodeBuild project such as "vpc-05c09f91d48831c8c".

subnet_ids

List of subnet IDs for the CodeBuild project, such as list("subnet-0b31f1863e9d31a67").

security_group_ids

List of security group IDs for the CodeBuild project, such as list("sg-0ce4ec0d0414d2ddc").

log

A logical to show the logs of the running CodeBuild build. Defaults to TRUE.

...

Docker build parameters (Use "_" instead of "-"; for example, Docker optional parameter build-arg becomes build_arg).

image_uri

The AWS ECR image URI for the Amazon SageMaker Model to be created (for example, as returned by vetiver_sm_build()).

model_name

The Amazon SageMaker model name to be deployed.

vpc_config

A list containing the VPC configuration for the Amazon SageMaker model API VpcConfig (optional).

  • Subnets: List of subnet ids

  • SecurityGroupIds: List of security group ids

enable_network_isolation

A logical to specify whether the container will run in network isolation mode. Defaults to FALSE.

tags

A named list of tags for labeling the Amazon SageMaker model or model endpint to be created.

instance_type

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

endpoint_name

The name to use for the Amazon SageMaker model endpoint to be created, if to be different from model_name.

initial_instance_count

The initial number of instances to run in the endpoint.

accelerator_type

Type of Elastic Inference accelerator to attach to an endpoint for model loading and inference, for example, "ml.eia1.medium".

kms_key

The ARN of the KMS key used to encrypt the data on the storage volume attached to the instance hosting the endpoint.

data_capture_config

A list for configuration to control how Amazon SageMaker captures inference data.

volume_size

The size, in GB, of the ML storage volume attached to the individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.

model_data_download_timeout

The timeout value, in seconds, to download and extract model data from Amazon S3.

wait

A logical for whether to wait for the endpoint to be deployed. Defaults to TRUE.

Value

vetiver_sm_build() returns the AWS ECR image URI and vetiver_sm_model() returns the model name (both as characters). vetiver_sm_endpoint() returns a new vetiver_endpoint_sagemaker() object.

Details

The function vetiver_sm_build() generates the files necessary to build a Docker container to deploy a vetiver model in SageMaker and then builds the image on AWS CodeBuild. The resulting image is stored in AWS ECR. This function creates a Plumber file and Dockerfile appropriate for SageMaker, for example, with path = "/invocations" and port = 8080.

If you run into problems with Docker rate limits, then either

See also

vetiver_prepare_docker(), vetiver_deploy_sagemaker(), 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)

new_image_uri <- vetiver_sm_build(
    board = b,
    name = "cars_linear",
    predict_args = list(type = "class", debug = TRUE),
    docker_args = list(
        base_image = "FROM public.ecr.aws/docker/library/r-base:4.2.2"
    )
)

model_name <- vetiver_sm_model(
    new_image_uri,
    tags = list("my_custom_tag" = "fuel_efficiency")
)

vetiver_sm_endpoint(model_name, "ml.t2.medium")
}