Starts a model compilation job
Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with AWS IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.
You can also provide a Tag to track the model compilation job's
resource use and costs. The response body contains the
CompilationJobArn for the compiled job.
To stop a model compilation job, use
stop_compilation_job. To get
information about a particular model compilation job, use
describe_compilation_job. To get
information about multiple model compilation jobs, use
list_compilation_jobs.
sagemaker_create_compilation_job(CompilationJobName, RoleArn, InputConfig, OutputConfig, StoppingCondition, Tags)
CompilationJobName |
[required] A name for the model compilation job. The name must be unique within the AWS Region and within your AWS account. |
RoleArn |
[required] The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf. During model compilation, Amazon SageMaker needs your permission to:
You grant permissions for all of these tasks to an IAM role. To pass
this role to Amazon SageMaker, the caller of this API must have the
|
InputConfig |
[required] Provides 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. |
OutputConfig |
[required] Provides information about the output location for the compiled model and the target device the model runs on. |
StoppingCondition |
[required] Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs. |
Tags |
An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources. |
A list with the following syntax:
list( CompilationJobArn = "string" )
svc$create_compilation_job(
CompilationJobName = "string",
RoleArn = "string",
InputConfig = list(
S3Uri = "string",
DataInputConfig = "string",
Framework = "TENSORFLOW"|"KERAS"|"MXNET"|"ONNX"|"PYTORCH"|"XGBOOST"|"TFLITE"|"DARKNET"|"SKLEARN"
),
OutputConfig = list(
S3OutputLocation = "string",
TargetDevice = "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"|"jacinto_tda4vm",
TargetPlatform = list(
Os = "ANDROID"|"LINUX",
Arch = "X86_64"|"X86"|"ARM64"|"ARM_EABI"|"ARM_EABIHF",
Accelerator = "INTEL_GRAPHICS"|"MALI"|"NVIDIA"
),
CompilerOptions = "string",
KmsKeyId = "string"
),
StoppingCondition = list(
MaxRuntimeInSeconds = 123,
MaxWaitTimeInSeconds = 123
),
Tags = list(
list(
Key = "string",
Value = "string"
)
)
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