Amazon SageMaker Service

2020/03/26 - Amazon SageMaker Service - 6 updated api methods

Changes  Update sagemaker client to latest version

CreateCompilationJob (updated) Link ¶
Changes (request)
{'OutputConfig': {'TargetDevice': {'sitara_am57x'}}}

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 StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

See also: AWS API Documentation

Request Syntax

client.create_compilation_job(
    CompilationJobName='string',
    RoleArn='string',
    InputConfig={
        'S3Uri': 'string',
        'DataInputConfig': 'string',
        'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'
    },
    OutputConfig={
        'S3OutputLocation': 'string',
        'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_inf1'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv22'
    },
    StoppingCondition={
        'MaxRuntimeInSeconds': 123,
        'MaxWaitTimeInSeconds': 123
    }
)
type CompilationJobName

string

param CompilationJobName

[REQUIRED]

A name for the model compilation job. The name must be unique within the AWS Region and within your AWS account.

type RoleArn

string

param 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:

  • Read input data from an S3 bucket

  • Write model artifacts to an S3 bucket

  • Write logs to Amazon CloudWatch Logs

  • Publish metrics to Amazon CloudWatch

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 iam:PassRole permission. For more information, see Amazon SageMaker Roles.

type InputConfig

dict

param 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.

  • S3Uri (string) -- [REQUIRED]

    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).

  • DataInputConfig (string) -- [REQUIRED]

    Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$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 : 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.

  • Framework (string) -- [REQUIRED]

    Identifies the framework in which the model was trained. For example: TENSORFLOW.

type OutputConfig

dict

param OutputConfig

[REQUIRED]

Provides information about the output location for the compiled model and the target device the model runs on.

  • S3OutputLocation (string) -- [REQUIRED]

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

  • TargetDevice (string) -- [REQUIRED]

    Identifies the device that you want to run your model on after it has been compiled. For example: ml_c5.

type StoppingCondition

dict

param 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.

  • MaxRuntimeInSeconds (integer) --

    The maximum length of time, in seconds, that the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.

  • MaxWaitTimeInSeconds (integer) --

    The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds .

rtype

dict

returns

Response Syntax

{
    'CompilationJobArn': 'string'
}

Response Structure

  • (dict) --

    • CompilationJobArn (string) --

      If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:

      • CompilationJobArn : The Amazon Resource Name (ARN) of the compiled job.

CreateFlowDefinition (updated) Link ¶
Changes (request)
{'HumanLoopRequestSource': {'AwsManagedHumanLoopRequestSource': 'AWS/Rekognition/DetectModerationLabels/Image/V3 '
                                                                '| '
                                                                'AWS/Textract/AnalyzeDocument/Forms/V1'}}

Creates a flow definition.

See also: AWS API Documentation

Request Syntax

client.create_flow_definition(
    FlowDefinitionName='string',
    HumanLoopRequestSource={
        'AwsManagedHumanLoopRequestSource': 'AWS/Rekognition/DetectModerationLabels/Image/V3'|'AWS/Textract/AnalyzeDocument/Forms/V1'
    },
    HumanLoopActivationConfig={
        'HumanLoopActivationConditionsConfig': {
            'HumanLoopActivationConditions': 'string'
        }
    },
    HumanLoopConfig={
        'WorkteamArn': 'string',
        'HumanTaskUiArn': 'string',
        'TaskTitle': 'string',
        'TaskDescription': 'string',
        'TaskCount': 123,
        'TaskAvailabilityLifetimeInSeconds': 123,
        'TaskTimeLimitInSeconds': 123,
        'TaskKeywords': [
            'string',
        ],
        'PublicWorkforceTaskPrice': {
            'AmountInUsd': {
                'Dollars': 123,
                'Cents': 123,
                'TenthFractionsOfACent': 123
            }
        }
    },
    OutputConfig={
        'S3OutputPath': 'string',
        'KmsKeyId': 'string'
    },
    RoleArn='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type FlowDefinitionName

string

param FlowDefinitionName

[REQUIRED]

The name of your flow definition.

type HumanLoopRequestSource

dict

param HumanLoopRequestSource

Container for configuring the source of human task requests.

  • AwsManagedHumanLoopRequestSource (string) -- [REQUIRED]

    Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:

type HumanLoopActivationConfig

dict

param HumanLoopActivationConfig

An object containing information about the events that trigger a human workflow.

  • HumanLoopActivationConditionsConfig (dict) -- [REQUIRED]

    Container structure for defining under what conditions SageMaker creates a human loop.

    • HumanLoopActivationConditions (string) -- [REQUIRED]

      JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide .

type HumanLoopConfig

dict

param HumanLoopConfig

[REQUIRED]

An object containing information about the tasks the human reviewers will perform.

  • WorkteamArn (string) -- [REQUIRED]

    Amazon Resource Name (ARN) of a team of workers.

  • HumanTaskUiArn (string) -- [REQUIRED]

    The Amazon Resource Name (ARN) of the human task user interface.

  • TaskTitle (string) -- [REQUIRED]

    A title for the human worker task.

  • TaskDescription (string) -- [REQUIRED]

    A description for the human worker task.

  • TaskCount (integer) -- [REQUIRED]

    The number of distinct workers who will perform the same task on each object. For example, if TaskCount is set to 3 for an image classification labeling job, three workers will classify each input image. Increasing TaskCount can improve label accuracy.

  • TaskAvailabilityLifetimeInSeconds (integer) --

    The length of time that a task remains available for labeling by human workers.

  • TaskTimeLimitInSeconds (integer) --

    The amount of time that a worker has to complete a task.

  • TaskKeywords (list) --

    Keywords used to describe the task so that workers can discover the task.

    • (string) --

  • PublicWorkforceTaskPrice (dict) --

    Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.

    Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer.

    • 0.036

    • 0.048

    • 0.060

    • 0.072

    • 0.120

    • 0.240

    • 0.360

    • 0.480

    • 0.600

    • 0.720

    • 0.840

    • 0.960

    • 1.080

    • 1.200

    Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.

    • 0.012

    • 0.024

    • 0.036

    • 0.048

    • 0.060

    • 0.072

    • 0.120

    • 0.240

    • 0.360

    • 0.480

    • 0.600

    • 0.720

    • 0.840

    • 0.960

    • 1.080

    • 1.200

    Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.

    • 0.840

    • 0.960

    • 1.080

    • 1.200

    Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.

    • 2.400

    • 2.280

    • 2.160

    • 2.040

    • 1.920

    • 1.800

    • 1.680

    • 1.560

    • 1.440

    • 1.320

    • 1.200

    • 1.080

    • 0.960

    • 0.840

    • 0.720

    • 0.600

    • 0.480

    • 0.360

    • 0.240

    • 0.120

    • 0.072

    • 0.060

    • 0.048

    • 0.036

    • 0.024

    • 0.012

    Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars.

    • 1.200

    • 1.080

    • 0.960

    • 0.840

    • 0.720

    • 0.600

    • 0.480

    • 0.360

    • 0.240

    • 0.120

    • 0.072

    • 0.060

    • 0.048

    • 0.036

    • 0.024

    • 0.012

    Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars.

    • 1.200

    • 1.080

    • 0.960

    • 0.840

    • 0.720

    • 0.600

    • 0.480

    • 0.360

    • 0.240

    • 0.120

    • 0.072

    • 0.060

    • 0.048

    • 0.036

    • 0.024

    • 0.012

    • AmountInUsd (dict) --

      Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.

      • Dollars (integer) --

        The whole number of dollars in the amount.

      • Cents (integer) --

        The fractional portion, in cents, of the amount.

      • TenthFractionsOfACent (integer) --

        Fractions of a cent, in tenths.

type OutputConfig

dict

param OutputConfig

[REQUIRED]

An object containing information about where the human review results will be uploaded.

  • S3OutputPath (string) -- [REQUIRED]

    The Amazon S3 path where the object containing human output will be made available.

  • KmsKeyId (string) --

    The Amazon Key Management Service (KMS) key ID for server-side encryption.

type RoleArn

string

param RoleArn

[REQUIRED]

The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298 .

type Tags

list

param Tags

An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.

  • (dict) --

    Describes a tag.

    • Key (string) -- [REQUIRED]

      The tag key.

    • Value (string) -- [REQUIRED]

      The tag value.

rtype

dict

returns

Response Syntax

{
    'FlowDefinitionArn': 'string'
}

Response Structure

  • (dict) --

    • FlowDefinitionArn (string) --

      The Amazon Resource Name (ARN) of the flow definition you create.

DescribeCompilationJob (updated) Link ¶
Changes (response)
{'OutputConfig': {'TargetDevice': {'sitara_am57x'}}}

Returns information about a model compilation job.

To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

See also: AWS API Documentation

Request Syntax

client.describe_compilation_job(
    CompilationJobName='string'
)
type CompilationJobName

string

param CompilationJobName

[REQUIRED]

The name of the model compilation job that you want information about.

rtype

dict

returns

Response Syntax

{
    'CompilationJobName': 'string',
    'CompilationJobArn': 'string',
    'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
    'CompilationStartTime': datetime(2015, 1, 1),
    'CompilationEndTime': datetime(2015, 1, 1),
    'StoppingCondition': {
        'MaxRuntimeInSeconds': 123,
        'MaxWaitTimeInSeconds': 123
    },
    'CreationTime': datetime(2015, 1, 1),
    'LastModifiedTime': datetime(2015, 1, 1),
    'FailureReason': 'string',
    'ModelArtifacts': {
        'S3ModelArtifacts': 'string'
    },
    'RoleArn': 'string',
    'InputConfig': {
        'S3Uri': 'string',
        'DataInputConfig': 'string',
        'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'
    },
    'OutputConfig': {
        'S3OutputLocation': 'string',
        'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_inf1'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv22'
    }
}

Response Structure

  • (dict) --

    • CompilationJobName (string) --

      The name of the model compilation job.

    • CompilationJobArn (string) --

      The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.

    • CompilationJobStatus (string) --

      The status of the model compilation job.

    • CompilationStartTime (datetime) --

      The time when the model compilation job started the CompilationJob instances.

      You are billed for the time between this timestamp and the timestamp in the DescribeCompilationJobResponse$CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That's because it takes time to download the compilation job, which depends on the size of the compilation job container.

    • CompilationEndTime (datetime) --

      The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job's model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker detected that the job failed.

    • StoppingCondition (dict) --

      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.

      • MaxRuntimeInSeconds (integer) --

        The maximum length of time, in seconds, that the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.

      • MaxWaitTimeInSeconds (integer) --

        The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds .

    • CreationTime (datetime) --

      The time that the model compilation job was created.

    • LastModifiedTime (datetime) --

      The time that the status of the model compilation job was last modified.

    • FailureReason (string) --

      If a model compilation job failed, the reason it failed.

    • ModelArtifacts (dict) --

      Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.

      • S3ModelArtifacts (string) --

        The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .

    • RoleArn (string) --

      The Amazon Resource Name (ARN) of the model compilation job.

    • InputConfig (dict) --

      Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

      • S3Uri (string) --

        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).

      • DataInputConfig (string) --

        Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$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 : 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.

      • Framework (string) --

        Identifies the framework in which the model was trained. For example: TENSORFLOW.

    • OutputConfig (dict) --

      Information about the output location for the compiled model and the target device that the model runs on.

      • S3OutputLocation (string) --

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

      • TargetDevice (string) --

        Identifies the device that you want to run your model on after it has been compiled. For example: ml_c5.

DescribeFlowDefinition (updated) Link ¶
Changes (response)
{'HumanLoopRequestSource': {'AwsManagedHumanLoopRequestSource': 'AWS/Rekognition/DetectModerationLabels/Image/V3 '
                                                                '| '
                                                                'AWS/Textract/AnalyzeDocument/Forms/V1'}}

Returns information about the specified flow definition.

See also: AWS API Documentation

Request Syntax

client.describe_flow_definition(
    FlowDefinitionName='string'
)
type FlowDefinitionName

string

param FlowDefinitionName

[REQUIRED]

The name of the flow definition.

rtype

dict

returns

Response Syntax

{
    'FlowDefinitionArn': 'string',
    'FlowDefinitionName': 'string',
    'FlowDefinitionStatus': 'Initializing'|'Active'|'Failed'|'Deleting',
    'CreationTime': datetime(2015, 1, 1),
    'HumanLoopRequestSource': {
        'AwsManagedHumanLoopRequestSource': 'AWS/Rekognition/DetectModerationLabels/Image/V3'|'AWS/Textract/AnalyzeDocument/Forms/V1'
    },
    'HumanLoopActivationConfig': {
        'HumanLoopActivationConditionsConfig': {
            'HumanLoopActivationConditions': 'string'
        }
    },
    'HumanLoopConfig': {
        'WorkteamArn': 'string',
        'HumanTaskUiArn': 'string',
        'TaskTitle': 'string',
        'TaskDescription': 'string',
        'TaskCount': 123,
        'TaskAvailabilityLifetimeInSeconds': 123,
        'TaskTimeLimitInSeconds': 123,
        'TaskKeywords': [
            'string',
        ],
        'PublicWorkforceTaskPrice': {
            'AmountInUsd': {
                'Dollars': 123,
                'Cents': 123,
                'TenthFractionsOfACent': 123
            }
        }
    },
    'OutputConfig': {
        'S3OutputPath': 'string',
        'KmsKeyId': 'string'
    },
    'RoleArn': 'string',
    'FailureReason': 'string'
}

Response Structure

  • (dict) --

    • FlowDefinitionArn (string) --

      The Amazon Resource Name (ARN) of the flow defintion.

    • FlowDefinitionName (string) --

      The Amazon Resource Name (ARN) of the flow definition.

    • FlowDefinitionStatus (string) --

      The status of the flow definition. Valid values are listed below.

    • CreationTime (datetime) --

      The timestamp when the flow definition was created.

    • HumanLoopRequestSource (dict) --

      Container for configuring the source of human task requests.

      • AwsManagedHumanLoopRequestSource (string) --

        Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:

    • HumanLoopActivationConfig (dict) --

      An object containing information about what triggers a human review workflow.

      • HumanLoopActivationConditionsConfig (dict) --

        Container structure for defining under what conditions SageMaker creates a human loop.

        • HumanLoopActivationConditions (string) --

          JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide .

    • HumanLoopConfig (dict) --

      An object containing information about who works on the task, the workforce task price, and other task details.

      • WorkteamArn (string) --

        Amazon Resource Name (ARN) of a team of workers.

      • HumanTaskUiArn (string) --

        The Amazon Resource Name (ARN) of the human task user interface.

      • TaskTitle (string) --

        A title for the human worker task.

      • TaskDescription (string) --

        A description for the human worker task.

      • TaskCount (integer) --

        The number of distinct workers who will perform the same task on each object. For example, if TaskCount is set to 3 for an image classification labeling job, three workers will classify each input image. Increasing TaskCount can improve label accuracy.

      • TaskAvailabilityLifetimeInSeconds (integer) --

        The length of time that a task remains available for labeling by human workers.

      • TaskTimeLimitInSeconds (integer) --

        The amount of time that a worker has to complete a task.

      • TaskKeywords (list) --

        Keywords used to describe the task so that workers can discover the task.

        • (string) --

      • PublicWorkforceTaskPrice (dict) --

        Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.

        Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer.

        • 0.036

        • 0.048

        • 0.060

        • 0.072

        • 0.120

        • 0.240

        • 0.360

        • 0.480

        • 0.600

        • 0.720

        • 0.840

        • 0.960

        • 1.080

        • 1.200

        Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.

        • 0.012

        • 0.024

        • 0.036

        • 0.048

        • 0.060

        • 0.072

        • 0.120

        • 0.240

        • 0.360

        • 0.480

        • 0.600

        • 0.720

        • 0.840

        • 0.960

        • 1.080

        • 1.200

        Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.

        • 0.840

        • 0.960

        • 1.080

        • 1.200

        Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.

        • 2.400

        • 2.280

        • 2.160

        • 2.040

        • 1.920

        • 1.800

        • 1.680

        • 1.560

        • 1.440

        • 1.320

        • 1.200

        • 1.080

        • 0.960

        • 0.840

        • 0.720

        • 0.600

        • 0.480

        • 0.360

        • 0.240

        • 0.120

        • 0.072

        • 0.060

        • 0.048

        • 0.036

        • 0.024

        • 0.012

        Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars.

        • 1.200

        • 1.080

        • 0.960

        • 0.840

        • 0.720

        • 0.600

        • 0.480

        • 0.360

        • 0.240

        • 0.120

        • 0.072

        • 0.060

        • 0.048

        • 0.036

        • 0.024

        • 0.012

        Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars.

        • 1.200

        • 1.080

        • 0.960

        • 0.840

        • 0.720

        • 0.600

        • 0.480

        • 0.360

        • 0.240

        • 0.120

        • 0.072

        • 0.060

        • 0.048

        • 0.036

        • 0.024

        • 0.012

        • AmountInUsd (dict) --

          Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.

          • Dollars (integer) --

            The whole number of dollars in the amount.

          • Cents (integer) --

            The fractional portion, in cents, of the amount.

          • TenthFractionsOfACent (integer) --

            Fractions of a cent, in tenths.

    • OutputConfig (dict) --

      An object containing information about the output file.

      • S3OutputPath (string) --

        The Amazon S3 path where the object containing human output will be made available.

      • KmsKeyId (string) --

        The Amazon Key Management Service (KMS) key ID for server-side encryption.

    • RoleArn (string) --

      The Amazon Resource Name (ARN) of the AWS Identity and Access Management (IAM) execution role for the flow definition.

    • FailureReason (string) --

ListCompilationJobs (updated) Link ¶
Changes (response)
{'CompilationJobSummaries': {'CompilationTargetDevice': {'sitara_am57x'}}}

Lists model compilation jobs that satisfy various filters.

To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.

See also: AWS API Documentation

Request Syntax

client.list_compilation_jobs(
    NextToken='string',
    MaxResults=123,
    CreationTimeAfter=datetime(2015, 1, 1),
    CreationTimeBefore=datetime(2015, 1, 1),
    LastModifiedTimeAfter=datetime(2015, 1, 1),
    LastModifiedTimeBefore=datetime(2015, 1, 1),
    NameContains='string',
    StatusEquals='INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
    SortBy='Name'|'CreationTime'|'Status',
    SortOrder='Ascending'|'Descending'
)
type NextToken

string

param NextToken

If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken . To retrieve the next set of model compilation jobs, use the token in the next request.

type MaxResults

integer

param MaxResults

The maximum number of model compilation jobs to return in the response.

type CreationTimeAfter

datetime

param CreationTimeAfter

A filter that returns the model compilation jobs that were created after a specified time.

type CreationTimeBefore

datetime

param CreationTimeBefore

A filter that returns the model compilation jobs that were created before a specified time.

type LastModifiedTimeAfter

datetime

param LastModifiedTimeAfter

A filter that returns the model compilation jobs that were modified after a specified time.

type LastModifiedTimeBefore

datetime

param LastModifiedTimeBefore

A filter that returns the model compilation jobs that were modified before a specified time.

type NameContains

string

param NameContains

A filter that returns the model compilation jobs whose name contains a specified string.

type StatusEquals

string

param StatusEquals

A filter that retrieves model compilation jobs with a specific DescribeCompilationJobResponse$CompilationJobStatus status.

type SortBy

string

param SortBy

The field by which to sort results. The default is CreationTime .

type SortOrder

string

param SortOrder

The sort order for results. The default is Ascending .

rtype

dict

returns

Response Syntax

{
    'CompilationJobSummaries': [
        {
            'CompilationJobName': 'string',
            'CompilationJobArn': 'string',
            'CreationTime': datetime(2015, 1, 1),
            'CompilationStartTime': datetime(2015, 1, 1),
            'CompilationEndTime': datetime(2015, 1, 1),
            'CompilationTargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_inf1'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv22',
            'LastModifiedTime': datetime(2015, 1, 1),
            'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED'
        },
    ],
    'NextToken': 'string'
}

Response Structure

  • (dict) --

    • CompilationJobSummaries (list) --

      An array of CompilationJobSummary objects, each describing a model compilation job.

      • (dict) --

        A summary of a model compilation job.

        • CompilationJobName (string) --

          The name of the model compilation job that you want a summary for.

        • CompilationJobArn (string) --

          The Amazon Resource Name (ARN) of the model compilation job.

        • CreationTime (datetime) --

          The time when the model compilation job was created.

        • CompilationStartTime (datetime) --

          The time when the model compilation job started.

        • CompilationEndTime (datetime) --

          The time when the model compilation job completed.

        • CompilationTargetDevice (string) --

          The type of device that the model will run on after compilation has completed.

        • LastModifiedTime (datetime) --

          The time when the model compilation job was last modified.

        • CompilationJobStatus (string) --

          The status of the model compilation job.

    • NextToken (string) --

      If the response is truncated, Amazon SageMaker returns this NextToken . To retrieve the next set of model compilation jobs, use this token in the next request.