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Class: Aws::SageMaker::Types::OutputConfig

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing OutputConfig as input to an Aws::Client method, you can use a vanilla Hash:

{
  s3_output_location: "S3Uri", # required
  target_device: "lambda", # accepts lambda, ml_m4, ml_m5, ml_c4, ml_c5, ml_p2, ml_p3, ml_g4dn, ml_inf1, jetson_tx1, jetson_tx2, jetson_nano, jetson_xavier, rasp3b, imx8qm, deeplens, rk3399, rk3288, aisage, sbe_c, qcs605, qcs603, sitara_am57x, amba_cv22, x86_win32, x86_win64, coreml
  target_platform: {
    os: "ANDROID", # required, accepts ANDROID, LINUX
    arch: "X86_64", # required, accepts X86_64, X86, ARM64, ARM_EABI, ARM_EABIHF
    accelerator: "INTEL_GRAPHICS", # accepts INTEL_GRAPHICS, MALI, NVIDIA
  },
  compiler_options: "CompilerOptions",
}

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.

Returned by:

See Also:

Instance Attribute Summary collapse

Instance Attribute Details

#compiler_options ⇒ String

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.

    ^

Returns:

  • (String) —

    Specifies additional parameters for compiler options in JSON format.

#s3_output_location ⇒ String

Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

Returns:

  • (String) —

    Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts.

#target_device ⇒ String

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.

Possible values:

  • lambda
  • ml_m4
  • ml_m5
  • ml_c4
  • ml_c5
  • ml_p2
  • ml_p3
  • ml_g4dn
  • ml_inf1
  • jetson_tx1
  • jetson_tx2
  • jetson_nano
  • jetson_xavier
  • rasp3b
  • imx8qm
  • deeplens
  • rk3399
  • rk3288
  • aisage
  • sbe_c
  • qcs605
  • qcs603
  • sitara_am57x
  • amba_cv22
  • x86_win32
  • x86_win64
  • coreml

Returns:

  • (String) —

    Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed.

#target_platform ⇒ Types::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}

Returns:

  • (Types::TargetPlatform) —

    Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators.