Class OutputConfig
- All Implemented Interfaces:
Serializable
,SdkPojo
,ToCopyableBuilder<OutputConfig.Builder,
OutputConfig>
Contains information about the output location for the compiled model and the target device that the model runs on.
TargetDevice
and TargetPlatform
are mutually exclusive, so you need to choose one between
the two to specify your target device or platform. If you cannot find your device you want to use from the
TargetDevice
list, use TargetPlatform
to describe the platform of your edge device and
CompilerOptions
if there are specific settings that are required or recommended to use for particular
TargetPlatform.
- See Also:
-
Nested Class Summary
-
Method Summary
Modifier and TypeMethodDescriptionstatic OutputConfig.Builder
builder()
final String
Specifies additional parameters for compiler options in JSON format.final boolean
final boolean
equalsBySdkFields
(Object obj) Indicates whether some other object is "equal to" this one by SDK fields.final <T> Optional
<T> getValueForField
(String fieldName, Class<T> clazz) final int
hashCode()
final String
kmsKeyId()
The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job.final String
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts.static Class
<? extends OutputConfig.Builder> final TargetDevice
Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed.final String
Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed.final TargetPlatform
Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators.Take this object and create a builder that contains all of the current property values of this object.final String
toString()
Returns a string representation of this object.Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder
copy
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Method Details
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s3OutputLocation
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.- Returns:
- Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix
.
-
targetDevice
Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of
TargetPlatform
.Currently
ml_trn1
is available only in US East (N. Virginia) Region, andml_inf2
is available only in US East (Ohio) Region.If the service returns an enum value that is not available in the current SDK version,
targetDevice
will returnTargetDevice.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available fromtargetDeviceAsString()
.- Returns:
- Identifies the target device or the machine learning instance that you want to run your model on after
the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform
fields. It can be used instead of
TargetPlatform
.Currently
ml_trn1
is available only in US East (N. Virginia) Region, andml_inf2
is available only in US East (Ohio) Region. - See Also:
-
targetDeviceAsString
Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of
TargetPlatform
.Currently
ml_trn1
is available only in US East (N. Virginia) Region, andml_inf2
is available only in US East (Ohio) Region.If the service returns an enum value that is not available in the current SDK version,
targetDevice
will returnTargetDevice.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available fromtargetDeviceAsString()
.- Returns:
- Identifies the target device or the machine learning instance that you want to run your model on after
the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform
fields. It can be used instead of
TargetPlatform
.Currently
ml_trn1
is available only in US East (N. Virginia) Region, andml_inf2
is available only in US East (Ohio) Region. - See Also:
-
targetPlatform
Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of
TargetDevice
.The following examples show how to configure the
TargetPlatform
andCompilerOptions
JSON strings for popular target platforms:-
Raspberry Pi 3 Model B+
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
"CompilerOptions": {'mattr': ['+neon']}
-
Jetson TX2
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},
"CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
-
EC2 m5.2xlarge instance OS
"TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},
"CompilerOptions": {'mcpu': 'skylake-avx512'}
-
RK3399
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
-
ARMv7 phone (CPU)
"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},
"CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
-
ARMv8 phone (CPU)
"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
"CompilerOptions": {'ANDROID_PLATFORM': 29}
- Returns:
- Contains information about a target platform that you want your model to run on, such as OS,
architecture, and accelerators. It is an alternative of
TargetDevice
.The following examples show how to configure the
TargetPlatform
andCompilerOptions
JSON strings for popular target platforms:-
Raspberry Pi 3 Model B+
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
"CompilerOptions": {'mattr': ['+neon']}
-
Jetson TX2
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},
"CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
-
EC2 m5.2xlarge instance OS
"TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},
"CompilerOptions": {'mcpu': 'skylake-avx512'}
-
RK3399
"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
-
ARMv7 phone (CPU)
"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},
"CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
-
ARMv8 phone (CPU)
"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
"CompilerOptions": {'ANDROID_PLATFORM': 29}
-
-
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compilerOptions
Specifies additional parameters for compiler options in JSON format. The compiler options are
TargetPlatform
specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specifyCompilerOptions.
-
DTYPE
: Specifies the data type for the input. When compiling forml_*
(except forml_inf
) instances using PyTorch framework, provide the data type (dtype) of the model's input."float32"
is used if"DTYPE"
is not specified. Options for data type are:-
float32: Use either
"float"
or"float32"
. -
int64: Use either
"int64"
or"long"
.
For example,
{"dtype" : "float32"}
. -
-
CPU
: Compilation for CPU supports the following compiler options.-
mcpu
: CPU micro-architecture. For example,{'mcpu': 'skylake-avx512'}
-
mattr
: CPU flags. For example,{'mattr': ['+neon', '+vfpv4']}
-
-
ARM
: Details of ARM CPU compilations.-
NEON
: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.For example, add
{'mattr': ['+neon']}
to the compiler options if compiling for ARM 32-bit platform with the NEON support.
-
-
NVIDIA
: Compilation for NVIDIA GPU supports the following compiler options.-
gpu_code
: Specifies the targeted architecture. -
trt-ver
: Specifies the TensorRT versions in x.y.z. format. -
cuda-ver
: Specifies the CUDA version in x.y format.
For example,
{'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
-
-
ANDROID
: Compilation for the Android OS supports the following compiler options:-
ANDROID_PLATFORM
: Specifies the Android API levels. Available levels range from 21 to 29. For example,{'ANDROID_PLATFORM': 28}
. -
mattr
: Add{'mattr': ['+neon']}
to compiler options if compiling for ARM 32-bit platform with NEON support.
-
-
INFERENTIA
: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example,"CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\""
.For information about supported compiler options, see Neuron Compiler CLI Reference Guide.
-
CoreML
: Compilation for the CoreML OutputConfigTargetDevice
supports the following compiler options:-
class_labels
: Specifies the classification labels file name inside input tar.gz file. For example,{"class_labels": "imagenet_labels_1000.txt"}
. Labels inside the txt file should be separated by newlines.
-
-
EIA
: Compilation for the Elastic Inference Accelerator supports the following compiler options:-
precision_mode
: Specifies the precision of compiled artifacts. Supported values are"FP16"
and"FP32"
. Default is"FP32"
. -
signature_def_key
: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key. -
output_names
: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either:signature_def_key
oroutput_names
.
For example:
{"precision_mode": "FP32", "output_names": ["output:0"]}
-
- Returns:
- Specifies additional parameters for compiler options in JSON format. The compiler options are
TargetPlatform
specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specifyCompilerOptions.
-
DTYPE
: Specifies the data type for the input. When compiling forml_*
(except forml_inf
) instances using PyTorch framework, provide the data type (dtype) of the model's input."float32"
is used if"DTYPE"
is not specified. Options for data type are:-
float32: Use either
"float"
or"float32"
. -
int64: Use either
"int64"
or"long"
.
For example,
{"dtype" : "float32"}
. -
-
CPU
: Compilation for CPU supports the following compiler options.-
mcpu
: CPU micro-architecture. For example,{'mcpu': 'skylake-avx512'}
-
mattr
: CPU flags. For example,{'mattr': ['+neon', '+vfpv4']}
-
-
ARM
: Details of ARM CPU compilations.-
NEON
: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.For example, add
{'mattr': ['+neon']}
to the compiler options if compiling for ARM 32-bit platform with the NEON support.
-
-
NVIDIA
: Compilation for NVIDIA GPU supports the following compiler options.-
gpu_code
: Specifies the targeted architecture. -
trt-ver
: Specifies the TensorRT versions in x.y.z. format. -
cuda-ver
: Specifies the CUDA version in x.y format.
For example,
{'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
-
-
ANDROID
: Compilation for the Android OS supports the following compiler options:-
ANDROID_PLATFORM
: Specifies the Android API levels. Available levels range from 21 to 29. For example,{'ANDROID_PLATFORM': 28}
. -
mattr
: Add{'mattr': ['+neon']}
to compiler options if compiling for ARM 32-bit platform with NEON support.
-
-
INFERENTIA
: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example,"CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\""
.For information about supported compiler options, see Neuron Compiler CLI Reference Guide.
-
CoreML
: Compilation for the CoreML OutputConfigTargetDevice
supports the following compiler options:-
class_labels
: Specifies the classification labels file name inside input tar.gz file. For example,{"class_labels": "imagenet_labels_1000.txt"}
. Labels inside the txt file should be separated by newlines.
-
-
EIA
: Compilation for the Elastic Inference Accelerator supports the following compiler options:-
precision_mode
: Specifies the precision of compiled artifacts. Supported values are"FP16"
and"FP32"
. Default is"FP32"
. -
signature_def_key
: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key. -
output_names
: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either:signature_def_key
oroutput_names
.
For example:
{"precision_mode": "FP32", "output_names": ["output:0"]}
-
-
-
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kmsKeyId
The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
- Returns:
- The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses
to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't
provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
For more information, see KMS-Managed
Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KmsKeyId can be any of the following formats:
-
Key ID:
1234abcd-12ab-34cd-56ef-1234567890ab
-
Key ARN:
arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
-
Alias name:
alias/ExampleAlias
-
Alias name ARN:
arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
-
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toBuilder
Description copied from interface:ToCopyableBuilder
Take this object and create a builder that contains all of the current property values of this object.- Specified by:
toBuilder
in interfaceToCopyableBuilder<OutputConfig.Builder,
OutputConfig> - Returns:
- a builder for type T
-
builder
-
serializableBuilderClass
-
hashCode
public final int hashCode() -
equals
-
equalsBySdkFields
Description copied from interface:SdkPojo
Indicates whether some other object is "equal to" this one by SDK fields. An SDK field is a modeled, non-inherited field in anSdkPojo
class, and is generated based on a service model.If an
SdkPojo
class does not have any inherited fields,equalsBySdkFields
andequals
are essentially the same.- Specified by:
equalsBySdkFields
in interfaceSdkPojo
- Parameters:
obj
- the object to be compared with- Returns:
- true if the other object equals to this object by sdk fields, false otherwise.
-
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. -
getValueForField
-
sdkFields
-