@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class S3DataSource extends Object implements Serializable, Cloneable, StructuredPojo
Describes the S3 data source.
Your input bucket must be in the same Amazon Web Services region as your training job.
| Constructor and Description |
|---|
S3DataSource() |
| Modifier and Type | Method and Description |
|---|---|
S3DataSource |
clone() |
boolean |
equals(Object obj) |
List<String> |
getAttributeNames()
A list of one or more attribute names to use that are found in a specified augmented manifest file.
|
List<String> |
getInstanceGroupNames()
A list of names of instance groups that get data from the S3 data source.
|
String |
getS3DataDistributionType()
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model
training, specify
FullyReplicated. |
String |
getS3DataType()
If you choose
S3Prefix, S3Uri identifies a key name prefix. |
String |
getS3Uri()
Depending on the value specified for the
S3DataType, identifies either a key name prefix or a
manifest. |
int |
hashCode() |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller. |
void |
setAttributeNames(Collection<String> attributeNames)
A list of one or more attribute names to use that are found in a specified augmented manifest file.
|
void |
setInstanceGroupNames(Collection<String> instanceGroupNames)
A list of names of instance groups that get data from the S3 data source.
|
void |
setS3DataDistributionType(String s3DataDistributionType)
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model
training, specify
FullyReplicated. |
void |
setS3DataType(String s3DataType)
If you choose
S3Prefix, S3Uri identifies a key name prefix. |
void |
setS3Uri(String s3Uri)
Depending on the value specified for the
S3DataType, identifies either a key name prefix or a
manifest. |
String |
toString()
Returns a string representation of this object.
|
S3DataSource |
withAttributeNames(Collection<String> attributeNames)
A list of one or more attribute names to use that are found in a specified augmented manifest file.
|
S3DataSource |
withAttributeNames(String... attributeNames)
A list of one or more attribute names to use that are found in a specified augmented manifest file.
|
S3DataSource |
withInstanceGroupNames(Collection<String> instanceGroupNames)
A list of names of instance groups that get data from the S3 data source.
|
S3DataSource |
withInstanceGroupNames(String... instanceGroupNames)
A list of names of instance groups that get data from the S3 data source.
|
S3DataSource |
withS3DataDistributionType(S3DataDistribution s3DataDistributionType)
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model
training, specify
FullyReplicated. |
S3DataSource |
withS3DataDistributionType(String s3DataDistributionType)
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model
training, specify
FullyReplicated. |
S3DataSource |
withS3DataType(S3DataType s3DataType)
If you choose
S3Prefix, S3Uri identifies a key name prefix. |
S3DataSource |
withS3DataType(String s3DataType)
If you choose
S3Prefix, S3Uri identifies a key name prefix. |
S3DataSource |
withS3Uri(String s3Uri)
Depending on the value specified for the
S3DataType, identifies either a key name prefix or a
manifest. |
public void setS3DataType(String s3DataType)
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects
that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file
containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file
in JSON lines format. This file contains the data you want to use for model training.
AugmentedManifestFile can only be used if the Channel's input mode is Pipe.
s3DataType - If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all
objects that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file
containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest
file in JSON lines format. This file contains the data you want to use for model training.
AugmentedManifestFile can only be used if the Channel's input mode is Pipe.
S3DataTypepublic String getS3DataType()
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects
that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file
containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file
in JSON lines format. This file contains the data you want to use for model training.
AugmentedManifestFile can only be used if the Channel's input mode is Pipe.
S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all
objects that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file
containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented
manifest file in JSON lines format. This file contains the data you want to use for model training.
AugmentedManifestFile can only be used if the Channel's input mode is Pipe.
S3DataTypepublic S3DataSource withS3DataType(String s3DataType)
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects
that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file
containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file
in JSON lines format. This file contains the data you want to use for model training.
AugmentedManifestFile can only be used if the Channel's input mode is Pipe.
s3DataType - If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all
objects that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file
containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest
file in JSON lines format. This file contains the data you want to use for model training.
AugmentedManifestFile can only be used if the Channel's input mode is Pipe.
S3DataTypepublic S3DataSource withS3DataType(S3DataType s3DataType)
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects
that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file
containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file
in JSON lines format. This file contains the data you want to use for model training.
AugmentedManifestFile can only be used if the Channel's input mode is Pipe.
s3DataType - If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all
objects that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file
containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest
file in JSON lines format. This file contains the data you want to use for model training.
AugmentedManifestFile can only be used if the Channel's input mode is Pipe.
S3DataTypepublic void setS3Uri(String s3Uri)
Depending on the value specified for the S3DataType, identifies either a key name prefix or a
manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix/
A manifest might look like this: s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix
which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set
of S3Uri. Note that the prefix must be a valid non-empty S3Uri that precludes users
from specifying a manifest whose individual S3Uri is sourced from different S3 buckets.
The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following S3Uri list:
s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of S3Uri in this manifest is the input data for the channel for this data source.
The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform
tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
s3Uri - Depending on the value specified for the S3DataType, identifies either a key name prefix or a
manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix/
A manifest might look like this: s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a
prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to
get a full set of S3Uri. Note that the prefix must be a valid non-empty S3Uri
that precludes users from specifying a manifest whose individual S3Uri is sourced from
different S3 buckets.
The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following S3Uri list:
s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of S3Uri in this manifest is the input data for the channel for this data
source. The object that each S3Uri points to must be readable by the IAM role that SageMaker
uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
public String getS3Uri()
Depending on the value specified for the S3DataType, identifies either a key name prefix or a
manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix/
A manifest might look like this: s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix
which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set
of S3Uri. Note that the prefix must be a valid non-empty S3Uri that precludes users
from specifying a manifest whose individual S3Uri is sourced from different S3 buckets.
The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following S3Uri list:
s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of S3Uri in this manifest is the input data for the channel for this data source.
The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform
tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataType, identifies either a key name prefix or
a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix/
A manifest might look like this: s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is
a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix
to get a full set of S3Uri. Note that the prefix must be a valid non-empty
S3Uri that precludes users from specifying a manifest whose individual S3Uri is
sourced from different S3 buckets.
The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following S3Uri list:
s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of S3Uri in this manifest is the input data for the channel for this data
source. The object that each S3Uri points to must be readable by the IAM role that SageMaker
uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
public S3DataSource withS3Uri(String s3Uri)
Depending on the value specified for the S3DataType, identifies either a key name prefix or a
manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix/
A manifest might look like this: s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix
which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set
of S3Uri. Note that the prefix must be a valid non-empty S3Uri that precludes users
from specifying a manifest whose individual S3Uri is sourced from different S3 buckets.
The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following S3Uri list:
s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of S3Uri in this manifest is the input data for the channel for this data source.
The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform
tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
s3Uri - Depending on the value specified for the S3DataType, identifies either a key name prefix or a
manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix/
A manifest might look like this: s3://bucketname/example.manifest
A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a
prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to
get a full set of S3Uri. Note that the prefix must be a valid non-empty S3Uri
that precludes users from specifying a manifest whose individual S3Uri is sourced from
different S3 buckets.
The following code example shows a valid manifest format:
[ {"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
"relative/path/custdata-N"
]
This JSON is equivalent to the following S3Uri list:
s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-2
...
s3://customer_bucket/some/prefix/relative/path/custdata-N
The complete set of S3Uri in this manifest is the input data for the channel for this data
source. The object that each S3Uri points to must be readable by the IAM role that SageMaker
uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
public void setS3DataDistributionType(String s3DataDistributionType)
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model
training, specify FullyReplicated.
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model
training, specify ShardedByS3Key. If there are n ML compute instances launched for a training
job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on
each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when
TrainingInputMode is set to File), this copies 1/n of the number of objects.
s3DataDistributionType - If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for
model training, specify FullyReplicated.
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model
training, specify ShardedByS3Key. If there are n ML compute instances launched for a
training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model
training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume
(when TrainingInputMode is set to File), this copies 1/n of the number of
objects.
S3DataDistributionpublic String getS3DataDistributionType()
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model
training, specify FullyReplicated.
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model
training, specify ShardedByS3Key. If there are n ML compute instances launched for a training
job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on
each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when
TrainingInputMode is set to File), this copies 1/n of the number of objects.
FullyReplicated.
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for
model training, specify ShardedByS3Key. If there are n ML compute instances launched
for a training job, each instance gets approximately 1/n of the number of S3 objects. In this
case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume
(when TrainingInputMode is set to File), this copies 1/n of the number
of objects.
S3DataDistributionpublic S3DataSource withS3DataDistributionType(String s3DataDistributionType)
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model
training, specify FullyReplicated.
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model
training, specify ShardedByS3Key. If there are n ML compute instances launched for a training
job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on
each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when
TrainingInputMode is set to File), this copies 1/n of the number of objects.
s3DataDistributionType - If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for
model training, specify FullyReplicated.
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model
training, specify ShardedByS3Key. If there are n ML compute instances launched for a
training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model
training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume
(when TrainingInputMode is set to File), this copies 1/n of the number of
objects.
S3DataDistributionpublic S3DataSource withS3DataDistributionType(S3DataDistribution s3DataDistributionType)
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model
training, specify FullyReplicated.
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model
training, specify ShardedByS3Key. If there are n ML compute instances launched for a training
job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on
each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when
TrainingInputMode is set to File), this copies 1/n of the number of objects.
s3DataDistributionType - If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for
model training, specify FullyReplicated.
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model
training, specify ShardedByS3Key. If there are n ML compute instances launched for a
training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model
training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose
ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume
(when TrainingInputMode is set to File), this copies 1/n of the number of
objects.
S3DataDistributionpublic List<String> getAttributeNames()
A list of one or more attribute names to use that are found in a specified augmented manifest file.
public void setAttributeNames(Collection<String> attributeNames)
A list of one or more attribute names to use that are found in a specified augmented manifest file.
attributeNames - A list of one or more attribute names to use that are found in a specified augmented manifest file.public S3DataSource withAttributeNames(String... attributeNames)
A list of one or more attribute names to use that are found in a specified augmented manifest file.
NOTE: This method appends the values to the existing list (if any). Use
setAttributeNames(java.util.Collection) or withAttributeNames(java.util.Collection) if you want
to override the existing values.
attributeNames - A list of one or more attribute names to use that are found in a specified augmented manifest file.public S3DataSource withAttributeNames(Collection<String> attributeNames)
A list of one or more attribute names to use that are found in a specified augmented manifest file.
attributeNames - A list of one or more attribute names to use that are found in a specified augmented manifest file.public List<String> getInstanceGroupNames()
A list of names of instance groups that get data from the S3 data source.
public void setInstanceGroupNames(Collection<String> instanceGroupNames)
A list of names of instance groups that get data from the S3 data source.
instanceGroupNames - A list of names of instance groups that get data from the S3 data source.public S3DataSource withInstanceGroupNames(String... instanceGroupNames)
A list of names of instance groups that get data from the S3 data source.
NOTE: This method appends the values to the existing list (if any). Use
setInstanceGroupNames(java.util.Collection) or withInstanceGroupNames(java.util.Collection) if
you want to override the existing values.
instanceGroupNames - A list of names of instance groups that get data from the S3 data source.public S3DataSource withInstanceGroupNames(Collection<String> instanceGroupNames)
A list of names of instance groups that get data from the S3 data source.
instanceGroupNames - A list of names of instance groups that get data from the S3 data source.public String toString()
toString in class ObjectObject.toString()public S3DataSource clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojoProtocolMarshaller.marshall in interface StructuredPojoprotocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.