vetiver_sm_build: Deploy a vetiver model API to Amazon SageMaker with modular...
In tidymodels/vetiver: Version, Share, Deploy, and Monitor Models

vetiver_sm_build

R Documentation

Deploy a vetiver model API to Amazon SageMaker with modular functions

Description

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`.

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

authenticate to Docker from SageMaker, or
use a public ECR base image, passed through docker_args

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.

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")
}

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