Package software.amazon.awssdk.services.sagemaker.model

Interface S3ModelDataSource.Builder

All Superinterfaces:
Buildable, CopyableBuilder<S3ModelDataSource.Builder,S3ModelDataSource>, SdkBuilder<S3ModelDataSource.Builder,S3ModelDataSource>, SdkPojo
Enclosing class:
S3ModelDataSource
public static interface S3ModelDataSource.Builder extends SdkPojo, CopyableBuilder<S3ModelDataSource.Builder,S3ModelDataSource>

  • Method Details

    • s3Uri

      S3ModelDataSource.Builder s3Uri(String s3Uri)

      Specifies the S3 path of ML model data to deploy.

      Parameters:
      s3Uri - Specifies the S3 path of ML model data to deploy.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • s3DataType

      S3ModelDataSource.Builder s3DataType(String s3DataType)

      Specifies the type of ML model data to deploy.

      If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

      If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.

      Parameters:
      s3DataType - Specifies the type of ML model data to deploy.

      If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

      If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.

      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • s3DataType

      S3ModelDataSource.Builder s3DataType(S3ModelDataType s3DataType)

      Specifies the type of ML model data to deploy.

      If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

      If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.

      Parameters:
      s3DataType - Specifies the type of ML model data to deploy.

      If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

      If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.

      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • compressionType

      S3ModelDataSource.Builder compressionType(String compressionType)

      Specifies how the ML model data is prepared.

      If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

      If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.

      If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

      If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

      • If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

      • If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

      • Do not use any of the following as file names or directory names:

        • An empty or blank string

        • A string which contains null bytes

        • A string longer than 255 bytes

        • A single dot (.)

        • A double dot (..)

      • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

      • Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

      Parameters:
      compressionType - Specifies how the ML model data is prepared.

      If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

      If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.

      If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

      If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

      • If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

      • If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

      • Do not use any of the following as file names or directory names:

        • An empty or blank string

        • A string which contains null bytes

        • A string longer than 255 bytes

        • A single dot (.)

        • A double dot (..)

      • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

      • Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • compressionType

      S3ModelDataSource.Builder compressionType(ModelCompressionType compressionType)

      Specifies how the ML model data is prepared.

      If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

      If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.

      If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

      If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

      • If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

      • If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

      • Do not use any of the following as file names or directory names:

        • An empty or blank string

        • A string which contains null bytes

        • A string longer than 255 bytes

        • A single dot (.)

        • A double dot (..)

      • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

      • Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

      Parameters:
      compressionType - Specifies how the ML model data is prepared.

      If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

      If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.

      If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

      If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

      • If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

      • If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

      • Do not use any of the following as file names or directory names:

        • An empty or blank string

        • A string which contains null bytes

        • A string longer than 255 bytes

        • A single dot (.)

        • A double dot (..)

      • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

      • Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also: