Package com.pulumi.aws.sagemaker.inputs

Class GetPrebuiltEcrImagePlainArgs

  • public final class GetPrebuiltEcrImagePlainArgs
extends com.pulumi.resources.InvokeArgs

    • Method Detail

      • dnsSuffix

        public java.util.Optional<java.lang.String> dnsSuffix()
        Returns:
        DNS suffix to use in the registry path. If not specified, the AWS provider sets it to the DNS suffix for the current region.

      • imageTag

        public java.util.Optional<java.lang.String> imageTag()
        Returns:
        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

        public java.util.Optional<java.lang.String> region()
        Returns:
        Region to use in the registry path. Defaults to the Region set in the provider configuration.

      • repositoryName

        public java.lang.String repositoryName()
        Returns:
        Name of the repository, which is generally the algorithm or library. Values include `autogluon-inference`, `autogluon-training`, `blazingtext`, `djl-inference`, `factorization-machines`, `forecasting-deepar`, `huggingface-pytorch-inference`, `huggingface-pytorch-inference-neuron`, `huggingface-pytorch-inference-neuronx`, `huggingface-pytorch-tgi-inference`, `huggingface-pytorch-training`, `huggingface-pytorch-training-neuronx`, `huggingface-pytorch-trcomp-training`, `huggingface-tensorflow-inference`, `huggingface-tensorflow-training`, `huggingface-tensorflow-trcomp-training`, `image-classification`, `image-classification-neo`, `ipinsights`, `kmeans`, `knn`, `lda`, `linear-learner`, `mxnet-inference`, `mxnet-inference-eia`, `mxnet-training`, `ntm`, `object-detection`, `object2vec`, `pca`, `pytorch-inference`, `pytorch-inference-eia`, `pytorch-inference-graviton`, `pytorch-inference-neuronx`, `pytorch-training`, `pytorch-training-neuronx`, `pytorch-trcomp-training`, `randomcutforest`, `sagemaker-base-python`, `sagemaker-chainer`, `sagemaker-clarify-processing`, `sagemaker-data-wrangler-container`, `sagemaker-debugger-rules`, `sagemaker-geospatial-v1-0`, `sagemaker-inference-mxnet`, `sagemaker-inference-pytorch`, `sagemaker-inference-tensorflow`, `sagemaker-model-monitor-analyzer`, `sagemaker-mxnet`, `sagemaker-mxnet-eia`, `sagemaker-mxnet-serving`, `sagemaker-mxnet-serving-eia`, `sagemaker-neo-mxnet`, `sagemaker-neo-pytorch`, `sagemaker-neo-tensorflow`, `sagemaker-pytorch`, `sagemaker-rl-coach-container`, `sagemaker-rl-mxnet`, `sagemaker-rl-ray-container`, `sagemaker-rl-tensorflow`, `sagemaker-rl-vw-container`, `sagemaker-scikit-learn`, `sagemaker-spark-processing`, `sagemaker-sparkml-serving`, `sagemaker-tensorflow`, `sagemaker-tensorflow-eia`, `sagemaker-tensorflow-scriptmode`, `sagemaker-tensorflow-serving`, `sagemaker-tensorflow-serving-eia`, `sagemaker-tritonserver`, `sagemaker-xgboost`, `semantic-segmentation`, `seq2seq`, `stabilityai-pytorch-inference`, `tei`, `tei-cpu`, `tensorflow-inference`, `tensorflow-inference-eia`, `tensorflow-inference-graviton`, `tensorflow-training`, and `xgboost-neo`.