@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class OutputConfig extends Object implements Serializable, Cloneable, StructuredPojo
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.
| Constructor and Description |
|---|
OutputConfig() |
| Modifier and Type | Method and Description |
|---|---|
OutputConfig |
clone() |
boolean |
equals(Object obj) |
String |
getCompilerOptions()
Specifies additional parameters for compiler options in JSON format.
|
String |
getKmsKeyId()
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume
after compilation job.
|
String |
getS3OutputLocation()
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts.
|
String |
getTargetDevice()
Identifies the target device or the machine learning instance that you want to run your model on after the
compilation has completed.
|
TargetPlatform |
getTargetPlatform()
Contains information about a target platform that you want your model to run on, such as OS, architecture, and
accelerators.
|
int |
hashCode() |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller. |
void |
setCompilerOptions(String compilerOptions)
Specifies additional parameters for compiler options in JSON format.
|
void |
setKmsKeyId(String kmsKeyId)
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume
after compilation job.
|
void |
setS3OutputLocation(String s3OutputLocation)
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts.
|
void |
setTargetDevice(String targetDevice)
Identifies the target device or the machine learning instance that you want to run your model on after the
compilation has completed.
|
void |
setTargetPlatform(TargetPlatform targetPlatform)
Contains information about a target platform that you want your model to run on, such as OS, architecture, and
accelerators.
|
String |
toString()
Returns a string representation of this object.
|
OutputConfig |
withCompilerOptions(String compilerOptions)
Specifies additional parameters for compiler options in JSON format.
|
OutputConfig |
withKmsKeyId(String kmsKeyId)
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume
after compilation job.
|
OutputConfig |
withS3OutputLocation(String s3OutputLocation)
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts.
|
OutputConfig |
withTargetDevice(String targetDevice)
Identifies the target device or the machine learning instance that you want to run your model on after the
compilation has completed.
|
OutputConfig |
withTargetDevice(TargetDevice targetDevice)
Identifies the target device or the machine learning instance that you want to run your model on after the
compilation has completed.
|
OutputConfig |
withTargetPlatform(TargetPlatform targetPlatform)
Contains information about a target platform that you want your model to run on, such as OS, architecture, and
accelerators.
|
public void setS3OutputLocation(String s3OutputLocation)
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix.
s3OutputLocation - Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix.public String getS3OutputLocation()
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix.
s3://bucket-name/key-name-prefix.public OutputConfig withS3OutputLocation(String s3OutputLocation)
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix.
s3OutputLocation - Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
s3://bucket-name/key-name-prefix.public void setTargetDevice(String 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.
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.TargetDevicepublic String getTargetDevice()
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.
TargetPlatform.TargetDevicepublic OutputConfig withTargetDevice(String 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.
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.TargetDevicepublic OutputConfig withTargetDevice(TargetDevice 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.
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.TargetDevicepublic void setTargetPlatform(TargetPlatform 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 and CompilerOptions
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}
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 and
CompilerOptions 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}
public TargetPlatform getTargetPlatform()
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 and CompilerOptions
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}
TargetDevice.
The following examples show how to configure the TargetPlatform and
CompilerOptions 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}
public OutputConfig withTargetPlatform(TargetPlatform 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 and CompilerOptions
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}
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 and
CompilerOptions 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}
public void setCompilerOptions(String 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 specify CompilerOptions.
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.
CoreML: Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler
options:
class_labels: Specifies the classification labels file name inside input tar.gz file. For example,
{"class_labels": "imagenet_labels_1000.txt"}. Labels inside the txt file should be separated by
newlines.
compilerOptions - Specifies additional parameters for compiler options in JSON format. The compiler options are
TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for
CPU compilations. For any other cases, it is optional to specify CompilerOptions.
CPU: Compilation for CPU supports the following compiler options.
mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
ARM: Details of ARM CPU compilations.
NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit
platform with the NEON support.
NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.
gpu_code: Specifies the targeted architecture.
trt-ver: Specifies the TensorRT versions in x.y.z. format.
cuda-ver: Specifies the CUDA version in x.y format.
For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
ANDROID: Compilation for the Android OS supports the following compiler options:
ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For
example, {'ANDROID_PLATFORM': 28}.
mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit
platform with NEON support.
INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string.
For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".
For information about supported compiler options, see Neuron Compiler CLI.
CoreML: Compilation for the CoreML OutputConfig$TargetDevice 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.
public String getCompilerOptions()
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 specify CompilerOptions.
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.
CoreML: Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler
options:
class_labels: Specifies the classification labels file name inside input tar.gz file. For example,
{"class_labels": "imagenet_labels_1000.txt"}. Labels inside the txt file should be separated by
newlines.
TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for
CPU compilations. For any other cases, it is optional to specify CompilerOptions.
CPU: Compilation for CPU supports the following compiler options.
mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
ARM: Details of ARM CPU compilations.
NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit
platform with the NEON support.
NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.
gpu_code: Specifies the targeted architecture.
trt-ver: Specifies the TensorRT versions in x.y.z. format.
cuda-ver: Specifies the CUDA version in x.y format.
For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
ANDROID: Compilation for the Android OS supports the following compiler options:
ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29.
For example, {'ANDROID_PLATFORM': 28}.
mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit
platform with NEON support.
INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string.
For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".
For information about supported compiler options, see Neuron Compiler CLI.
CoreML: Compilation for the CoreML OutputConfig$TargetDevice 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.
public OutputConfig withCompilerOptions(String 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 specify CompilerOptions.
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.
CoreML: Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler
options:
class_labels: Specifies the classification labels file name inside input tar.gz file. For example,
{"class_labels": "imagenet_labels_1000.txt"}. Labels inside the txt file should be separated by
newlines.
compilerOptions - Specifies additional parameters for compiler options in JSON format. The compiler options are
TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for
CPU compilations. For any other cases, it is optional to specify CompilerOptions.
CPU: Compilation for CPU supports the following compiler options.
mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
ARM: Details of ARM CPU compilations.
NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit
platform with the NEON support.
NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.
gpu_code: Specifies the targeted architecture.
trt-ver: Specifies the TensorRT versions in x.y.z. format.
cuda-ver: Specifies the CUDA version in x.y format.
For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
ANDROID: Compilation for the Android OS supports the following compiler options:
ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For
example, {'ANDROID_PLATFORM': 28}.
mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit
platform with NEON support.
INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string.
For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".
For information about supported compiler options, see Neuron Compiler CLI.
CoreML: Compilation for the CoreML OutputConfig$TargetDevice 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.
public void setKmsKeyId(String kmsKeyId)
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume 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
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
kmsKeyId - The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage
volume 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
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
public String getKmsKeyId()
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume 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
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
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
public OutputConfig withKmsKeyId(String kmsKeyId)
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume 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
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
kmsKeyId - The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage
volume 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
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
public String toString()
toString in class ObjectObject.toString()public OutputConfig clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojoProtocolMarshaller.marshall in interface StructuredPojoprotocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.