Get information about prebuilt Amazon SageMaker Docker images.
Basic usage:
data "aws_sagemaker_prebuilt_ecr_image" "test" {
repository_name = "sagemaker-scikit-learn"
image_tag = "2.2-1.0.11.0"
}
This data source supports the following arguments:
repository_name
- (Required) Name of the repository, which is generally the algorithm or library. Values include blazingtext
, factorization-machines
, forecasting-deepar
, image-classification
, ipinsights
, kmeans
, knn
, lda
, linear-learner
, mxnet-inference-eia
, mxnet-inference
, mxnet-training
, ntm
, object-detection
, object2vec
, pca
, pytorch-inference-eia
, pytorch-inference
, pytorch-training
, randomcutforest
, sagemaker-scikit-learn
, sagemaker-sparkml-serving
, sagemaker-xgboost
, semantic-segmentation
, seq2seq
, tensorflow-inference-eia
, tensorflow-inference
, tensorflow-training
, huggingface-tensorflow-training
, huggingface-tensorflow-inference
, huggingface-pytorch-training
, and huggingface-pytorch-inference
.dns_suffix
- (Optional) DNS suffix to use in the registry path. If not specified, the AWS provider sets it to the DNS suffix for the current region.image_tag
- (Optional) Image tag for the Docker image. If not specified, the AWS provider sets the value to 1
, which for many repositories indicates the latest version. Some repositories, such as XGBoost, do not support 1
or latest
and specific version must be used.region
(Optional) - Region to use in the registry path. If not specified, the AWS provider sets it to the current region.This data source exports the following attributes in addition to the arguments above:
registry_id
- Account ID containing the image. For example, 469771592824
.registry_path
- Docker image URL. For example, 341280168497.dkr.ecr.ca-central-1.amazonaws.com/sagemaker-sparkml-serving:2.4
.