@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class S3ModelDataSource extends Object implements Serializable, Cloneable, StructuredPojo
Specifies the S3 location of ML model data to deploy.
| Constructor and Description |
|---|
S3ModelDataSource() |
| Modifier and Type | Method and Description |
|---|---|
S3ModelDataSource |
clone() |
boolean |
equals(Object obj) |
String |
getCompressionType()
Specifies how the ML model data is prepared.
|
ModelAccessConfig |
getModelAccessConfig()
Specifies the access configuration file for the ML model.
|
String |
getS3DataType()
Specifies the type of ML model data to deploy.
|
String |
getS3Uri()
Specifies the S3 path of ML model data to deploy.
|
int |
hashCode() |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller. |
void |
setCompressionType(String compressionType)
Specifies how the ML model data is prepared.
|
void |
setModelAccessConfig(ModelAccessConfig modelAccessConfig)
Specifies the access configuration file for the ML model.
|
void |
setS3DataType(String s3DataType)
Specifies the type of ML model data to deploy.
|
void |
setS3Uri(String s3Uri)
Specifies the S3 path of ML model data to deploy.
|
String |
toString()
Returns a string representation of this object.
|
S3ModelDataSource |
withCompressionType(ModelCompressionType compressionType)
Specifies how the ML model data is prepared.
|
S3ModelDataSource |
withCompressionType(String compressionType)
Specifies how the ML model data is prepared.
|
S3ModelDataSource |
withModelAccessConfig(ModelAccessConfig modelAccessConfig)
Specifies the access configuration file for the ML model.
|
S3ModelDataSource |
withS3DataType(S3ModelDataType s3DataType)
Specifies the type of ML model data to deploy.
|
S3ModelDataSource |
withS3DataType(String s3DataType)
Specifies the type of ML model data to deploy.
|
S3ModelDataSource |
withS3Uri(String s3Uri)
Specifies the S3 path of ML model data to deploy.
|
public void setS3Uri(String s3Uri)
Specifies the S3 path of ML model data to deploy.
s3Uri - Specifies the S3 path of ML model data to deploy.public String getS3Uri()
Specifies the S3 path of ML model data to deploy.
public S3ModelDataSource withS3Uri(String s3Uri)
Specifies the S3 path of ML model data to deploy.
s3Uri - Specifies the S3 path of ML model data to deploy.public void setS3DataType(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.
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.
S3ModelDataTypepublic String getS3DataType()
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.
S3ModelDataTypepublic S3ModelDataSource withS3DataType(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.
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.
S3ModelDataTypepublic S3ModelDataSource withS3DataType(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.
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.
S3ModelDataTypepublic void setCompressionType(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.
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.
ModelCompressionTypepublic String getCompressionType()
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.
ModelCompressionTypepublic S3ModelDataSource withCompressionType(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.
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.
ModelCompressionTypepublic S3ModelDataSource withCompressionType(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.
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.
ModelCompressionTypepublic void setModelAccessConfig(ModelAccessConfig modelAccessConfig)
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license
agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with
any applicable license terms and making sure they are acceptable for your use case before downloading or using a
model.
modelAccessConfig - Specifies the access configuration file for the ML model. You can explicitly accept the model end-user
license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and
complying with any applicable license terms and making sure they are acceptable for your use case before
downloading or using a model.public ModelAccessConfig getModelAccessConfig()
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license
agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with
any applicable license terms and making sure they are acceptable for your use case before downloading or using a
model.
ModelAccessConfig. You are responsible for reviewing and
complying with any applicable license terms and making sure they are acceptable for your use case before
downloading or using a model.public S3ModelDataSource withModelAccessConfig(ModelAccessConfig modelAccessConfig)
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license
agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with
any applicable license terms and making sure they are acceptable for your use case before downloading or using a
model.
modelAccessConfig - Specifies the access configuration file for the ML model. You can explicitly accept the model end-user
license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and
complying with any applicable license terms and making sure they are acceptable for your use case before
downloading or using a model.public String toString()
toString in class ObjectObject.toString()public S3ModelDataSource clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojoProtocolMarshaller.marshall in interface StructuredPojoprotocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.