Class InputConfig
- java.lang.Object
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- software.amazon.awssdk.services.sagemaker.model.InputConfig
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- All Implemented Interfaces:
Serializable
,SdkPojo
,ToCopyableBuilder<InputConfig.Builder,InputConfig>
@Generated("software.amazon.awssdk:codegen") public final class InputConfig extends Object implements SdkPojo, Serializable, ToCopyableBuilder<InputConfig.Builder,InputConfig>
Contains information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
- See Also:
- Serialized Form
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Nested Class Summary
Nested Classes Modifier and Type Class Description static interface
InputConfig.Builder
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static InputConfig.Builder
builder()
String
dataInputConfig()
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form.boolean
equals(Object obj)
boolean
equalsBySdkFields(Object obj)
Framework
framework()
Identifies the framework in which the model was trained.String
frameworkAsString()
Identifies the framework in which the model was trained.String
frameworkVersion()
Specifies the framework version to use.<T> Optional<T>
getValueForField(String fieldName, Class<T> clazz)
int
hashCode()
String
s3Uri()
The S3 path where the model artifacts, which result from model training, are stored.List<SdkField<?>>
sdkFields()
static Class<? extends InputConfig.Builder>
serializableBuilderClass()
InputConfig.Builder
toBuilder()
String
toString()
Returns a string representation of this object.-
Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
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Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder
copy
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Method Detail
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s3Uri
public final String s3Uri()
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
- Returns:
- The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
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dataInputConfig
public final String dataInputConfig()
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are
Framework
specific.-
TensorFlow
: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"input":[1,1024,1024,3]}
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If using the CLI,
{\"input\":[1,1024,1024,3]}
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-
Examples for two inputs:
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If using the console,
{"data1": [1,28,28,1], "data2":[1,28,28,1]}
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If using the CLI,
{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
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-
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KERAS
: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format,DataInputConfig
should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"input_1":[1,3,224,224]}
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If using the CLI,
{\"input_1\":[1,3,224,224]}
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-
Examples for two inputs:
-
If using the console,
{"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
-
If using the CLI,
{\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
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-
-
MXNET/ONNX/DARKNET
: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"data":[1,3,1024,1024]}
-
If using the CLI,
{\"data\":[1,3,1024,1024]}
-
-
Examples for two inputs:
-
If using the console,
{"var1": [1,1,28,28], "var2":[1,1,28,28]}
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If using the CLI,
{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
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-
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PyTorch
: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.-
Examples for one input in dictionary format:
-
If using the console,
{"input0":[1,3,224,224]}
-
If using the CLI,
{\"input0\":[1,3,224,224]}
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-
Example for one input in list format:
[[1,3,224,224]]
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Examples for two inputs in dictionary format:
-
If using the console,
{"input0":[1,3,224,224], "input1":[1,3,224,224]}
-
If using the CLI,
{\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
-
-
Example for two inputs in list format:
[[1,3,224,224], [1,3,224,224]]
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XGBOOST
: input data name and shape are not needed.
DataInputConfig
supports the following parameters forCoreML
TargetDevice
(ML Model format):-
shape
: Input shape, for example{"input_1": {"shape": [1,224,224,3]}}
. In addition to static input shapes, CoreML converter supports Flexible input shapes:-
Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example:
{"input_1": {"shape": ["1..10", 224, 224, 3]}}
-
Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example:
{"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
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default_shape
: Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example{"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
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type
: Input type. Allowed values:Image
andTensor
. By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such asbias
andscale
. -
bias
: If the input type is an Image, you need to provide the bias vector. -
scale
: If the input type is an Image, you need to provide a scale factor.
CoreML
ClassifierConfig
parameters can be specified using OutputConfigCompilerOptions
. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:-
Tensor type input:
-
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
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Tensor type input without input name (PyTorch):
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"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
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Image type input:
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"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
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"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
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Image type input without input name (PyTorch):
-
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
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"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
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Depending on the model format,
DataInputConfig
requires the following parameters forml_eia2
OutputConfig:TargetDevice.-
For TensorFlow models saved in the SavedModel format, specify the input names from
signature_def_key
and the input model shapes forDataInputConfig
. Specify thesignature_def_key
inOutputConfig:CompilerOptions
if the model does not use TensorFlow's default signature def key. For example:-
"DataInputConfig": {"inputs": [1, 224, 224, 3]}
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"CompilerOptions": {"signature_def_key": "serving_custom"}
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For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in
DataInputConfig
and the output tensor names foroutput_names
inOutputConfig:CompilerOptions
. For example:-
"DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
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"CompilerOptions": {"output_names": ["output_tensor:0"]}
-
- Returns:
- Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary
form. The data inputs are
Framework
specific.-
TensorFlow
: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"input":[1,1024,1024,3]}
-
If using the CLI,
{\"input\":[1,1024,1024,3]}
-
-
Examples for two inputs:
-
If using the console,
{"data1": [1,28,28,1], "data2":[1,28,28,1]}
-
If using the CLI,
{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
-
-
-
KERAS
: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format,DataInputConfig
should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"input_1":[1,3,224,224]}
-
If using the CLI,
{\"input_1\":[1,3,224,224]}
-
-
Examples for two inputs:
-
If using the console,
{"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
-
If using the CLI,
{\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
-
-
-
MXNET/ONNX/DARKNET
: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.-
Examples for one input:
-
If using the console,
{"data":[1,3,1024,1024]}
-
If using the CLI,
{\"data\":[1,3,1024,1024]}
-
-
Examples for two inputs:
-
If using the console,
{"var1": [1,1,28,28], "var2":[1,1,28,28]}
-
If using the CLI,
{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
-
-
-
PyTorch
: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.-
Examples for one input in dictionary format:
-
If using the console,
{"input0":[1,3,224,224]}
-
If using the CLI,
{\"input0\":[1,3,224,224]}
-
-
Example for one input in list format:
[[1,3,224,224]]
-
Examples for two inputs in dictionary format:
-
If using the console,
{"input0":[1,3,224,224], "input1":[1,3,224,224]}
-
If using the CLI,
{\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
-
-
Example for two inputs in list format:
[[1,3,224,224], [1,3,224,224]]
-
-
XGBOOST
: input data name and shape are not needed.
DataInputConfig
supports the following parameters forCoreML
TargetDevice
(ML Model format):-
shape
: Input shape, for example{"input_1": {"shape": [1,224,224,3]}}
. In addition to static input shapes, CoreML converter supports Flexible input shapes:-
Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example:
{"input_1": {"shape": ["1..10", 224, 224, 3]}}
-
Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example:
{"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
-
-
default_shape
: Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example{"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
-
type
: Input type. Allowed values:Image
andTensor
. By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such asbias
andscale
. -
bias
: If the input type is an Image, you need to provide the bias vector. -
scale
: If the input type is an Image, you need to provide a scale factor.
CoreML
ClassifierConfig
parameters can be specified using OutputConfigCompilerOptions
. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:-
Tensor type input:
-
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
-
-
Tensor type input without input name (PyTorch):
-
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
-
-
Image type input:
-
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
-
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
-
-
Image type input without input name (PyTorch):
-
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
-
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
-
Depending on the model format,
DataInputConfig
requires the following parameters forml_eia2
OutputConfig:TargetDevice.-
For TensorFlow models saved in the SavedModel format, specify the input names from
signature_def_key
and the input model shapes forDataInputConfig
. Specify thesignature_def_key
inOutputConfig:CompilerOptions
if the model does not use TensorFlow's default signature def key. For example:-
"DataInputConfig": {"inputs": [1, 224, 224, 3]}
-
"CompilerOptions": {"signature_def_key": "serving_custom"}
-
-
For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in
DataInputConfig
and the output tensor names foroutput_names
inOutputConfig:CompilerOptions
. For example:-
"DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
-
"CompilerOptions": {"output_names": ["output_tensor:0"]}
-
-
-
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framework
public final Framework framework()
Identifies the framework in which the model was trained. For example: TENSORFLOW.
If the service returns an enum value that is not available in the current SDK version,
framework
will returnFramework.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available fromframeworkAsString()
.- Returns:
- Identifies the framework in which the model was trained. For example: TENSORFLOW.
- See Also:
Framework
-
frameworkAsString
public final String frameworkAsString()
Identifies the framework in which the model was trained. For example: TENSORFLOW.
If the service returns an enum value that is not available in the current SDK version,
framework
will returnFramework.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available fromframeworkAsString()
.- Returns:
- Identifies the framework in which the model was trained. For example: TENSORFLOW.
- See Also:
Framework
-
frameworkVersion
public final String frameworkVersion()
Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.
For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks.
- Returns:
- Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch,
TensorFlow and TensorFlow Lite frameworks.
For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks.
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toBuilder
public InputConfig.Builder toBuilder()
- Specified by:
toBuilder
in interfaceToCopyableBuilder<InputConfig.Builder,InputConfig>
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builder
public static InputConfig.Builder builder()
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serializableBuilderClass
public static Class<? extends InputConfig.Builder> serializableBuilderClass()
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equalsBySdkFields
public final boolean equalsBySdkFields(Object obj)
- Specified by:
equalsBySdkFields
in interfaceSdkPojo
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toString
public final String toString()
Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
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