@Generated(value="software.amazon.awssdk:codegen") public final class S3DataSource extends Object implements SdkPojo, Serializable, ToCopyableBuilder<S3DataSource.Builder,S3DataSource>
Describes the S3 data source.
Modifier and Type | Class and Description |
---|---|
static interface |
S3DataSource.Builder |
Modifier and Type | Method and Description |
---|---|
List<String> |
attributeNames()
A list of one or more attribute names to use that are found in a specified augmented manifest file.
|
static S3DataSource.Builder |
builder() |
boolean |
equals(Object obj) |
<T> Optional<T> |
getValueForField(String fieldName,
Class<T> clazz) |
int |
hashCode() |
S3DataDistribution |
s3DataDistributionType()
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for
model training, specify
FullyReplicated . |
String |
s3DataDistributionTypeAsString()
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for
model training, specify
FullyReplicated . |
S3DataType |
s3DataType()
If you choose
S3Prefix , S3Uri identifies a key name prefix. |
String |
s3DataTypeAsString()
If you choose
S3Prefix , S3Uri identifies a key name prefix. |
String |
s3Uri()
Depending on the value specified for the
S3DataType , identifies either a key name prefix or a
manifest. |
List<SdkField<?>> |
sdkFields() |
static Class<? extends S3DataSource.Builder> |
serializableBuilderClass() |
S3DataSource.Builder |
toBuilder()
Take this object and create a builder that contains all of the current property values of this object.
|
String |
toString() |
copy
public S3DataType s3DataType()
If you choose S3Prefix
, S3Uri
identifies a key name prefix. Amazon 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 Amazon 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
.
If the service returns an enum value that is not available in the current SDK version, s3DataType
will
return S3DataType.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available from
s3DataTypeAsString()
.
S3Prefix
, S3Uri
identifies a key name prefix. Amazon 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 Amazon 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
public String s3DataTypeAsString()
If you choose S3Prefix
, S3Uri
identifies a key name prefix. Amazon 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 Amazon 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
.
If the service returns an enum value that is not available in the current SDK version, s3DataType
will
return S3DataType.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available from
s3DataTypeAsString()
.
S3Prefix
, S3Uri
identifies a key name prefix. Amazon 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 Amazon 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
public 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
The manifest is an S3 object which is a JSON file with the following format:
[
{"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
]
The preceding JSON matches the following s3Uris
:
s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-1
...
The complete set of s3uris
in this manifest is the input data for the channel for this datasource.
The object that each s3uris
points to must be readable by the IAM role that Amazon SageMaker uses to
perform tasks on your behalf.
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
The manifest is an S3 object which is a JSON file with the following format:
[
{"prefix": "s3://customer_bucket/some/prefix/"},
"relative/path/to/custdata-1",
"relative/path/custdata-2",
...
]
The preceding JSON matches the following s3Uris
:
s3://customer_bucket/some/prefix/relative/path/to/custdata-1
s3://customer_bucket/some/prefix/relative/path/custdata-1
...
The complete set of s3uris
in this manifest is the input data for the channel for this
datasource. The object that each s3uris
points to must be readable by the IAM role that
Amazon SageMaker uses to perform tasks on your behalf.
public S3DataDistribution s3DataDistributionType()
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for
model training, specify FullyReplicated
.
If you want Amazon 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 Pipemodes. 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.
If the service returns an enum value that is not available in the current SDK version,
s3DataDistributionType
will return S3DataDistribution.UNKNOWN_TO_SDK_VERSION
. The raw value
returned by the service is available from s3DataDistributionTypeAsString()
.
FullyReplicated
.
If you want Amazon 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 Pipemodes. 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.
S3DataDistribution
public String s3DataDistributionTypeAsString()
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for
model training, specify FullyReplicated
.
If you want Amazon 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 Pipemodes. 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.
If the service returns an enum value that is not available in the current SDK version,
s3DataDistributionType
will return S3DataDistribution.UNKNOWN_TO_SDK_VERSION
. The raw value
returned by the service is available from s3DataDistributionTypeAsString()
.
FullyReplicated
.
If you want Amazon 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 Pipemodes. 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.
S3DataDistribution
public List<String> attributeNames()
A list of one or more attribute names to use that are found in a specified augmented manifest file.
Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
public S3DataSource.Builder toBuilder()
ToCopyableBuilder
toBuilder
in interface ToCopyableBuilder<S3DataSource.Builder,S3DataSource>
public static S3DataSource.Builder builder()
public static Class<? extends S3DataSource.Builder> serializableBuilderClass()
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