* Specifies the S3 location of ML model data to deploy. *
* * @see AWS API * Documentation */ @Generated("com.amazonaws:aws-java-sdk-code-generator") public class S3ModelDataSource implements Serializable, Cloneable, StructuredPojo { /** ** Specifies the S3 path of ML model data to deploy. *
*/ private String s3Uri; /** ** 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.
*
* 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. *
** Specifies the S3 path of ML model data to deploy. *
* * @param s3Uri * Specifies the S3 path of ML model data to deploy. */ public void setS3Uri(String s3Uri) { this.s3Uri = s3Uri; } /** ** Specifies the S3 path of ML model data to deploy. *
* * @return Specifies the S3 path of ML model data to deploy. */ public String getS3Uri() { return this.s3Uri; } /** ** Specifies the S3 path of ML model data to deploy. *
* * @param s3Uri * Specifies the S3 path of ML model data to deploy. * @return Returns a reference to this object so that method calls can be chained together. */ public S3ModelDataSource withS3Uri(String s3Uri) { setS3Uri(s3Uri); return this; } /** ** 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.
*
* 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.
* @see S3ModelDataType
*/
public void setS3DataType(String s3DataType) {
this.s3DataType = 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.
*
* 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.
* @see S3ModelDataType
*/
public String getS3DataType() {
return this.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.
*
* 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.
* @return Returns a reference to this object so that method calls can be chained together.
* @see S3ModelDataType
*/
public S3ModelDataSource withS3DataType(String s3DataType) {
setS3DataType(s3DataType);
return this;
}
/**
*
* 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.
*
* 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.
* @return Returns a reference to this object so that method calls can be chained together.
* @see S3ModelDataType
*/
public S3ModelDataSource withS3DataType(S3ModelDataType s3DataType) {
this.s3DataType = s3DataType.toString();
return this;
}
/**
*
* 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. *
*
* 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. *
** 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. *
*
* 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. *
** 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. *
*
* 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. *
** 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. *
*
* 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. *
*