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title: Getting Started with Datadog
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# aws_sagemaker_inferencerecommendationjob{% #aws_sagemaker_inferencerecommendationjob %}

## `account_id`{% #account_id %}

**Type**: `STRING`

## `completion_time`{% #completion_time %}

**Type**: `TIMESTAMP`**Provider name**: `CompletionTime`**Description**: A timestamp that shows when the job completed.

## `creation_time`{% #creation_time %}

**Type**: `TIMESTAMP`**Provider name**: `CreationTime`**Description**: A timestamp that shows when the job was created.

## `endpoint_performances`{% #endpoint_performances %}

**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `EndpointPerformances`**Description**: The performance results from running an Inference Recommender job on an existing endpoint.

- `endpoint_info`**Type**: `STRUCT`**Provider name**: `EndpointInfo`
  - `endpoint_name`**Type**: `STRING`**Provider name**: `EndpointName`**Description**: The name of a customer's endpoint.
- `metrics`**Type**: `STRUCT`**Provider name**: `Metrics`**Description**: The metrics for an existing endpoint.
  - `max_invocations`**Type**: `INT32`**Provider name**: `MaxInvocations`**Description**: The expected maximum number of requests per minute for the instance.
  - `model_latency`**Type**: `INT32`**Provider name**: `ModelLatency`**Description**: The expected model latency at maximum invocations per minute for the instance.

## `failure_reason`{% #failure_reason %}

**Type**: `STRING`**Provider name**: `FailureReason`**Description**: If the job fails, provides information why the job failed.

## `inference_recommendations`{% #inference_recommendations %}

**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `InferenceRecommendations`**Description**: The recommendations made by Inference Recommender.

- `endpoint_configuration`**Type**: `STRUCT`**Provider name**: `EndpointConfiguration`**Description**: Defines the endpoint configuration parameters.
  - `endpoint_name`**Type**: `STRING`**Provider name**: `EndpointName`**Description**: The name of the endpoint made during a recommendation job.
  - `initial_instance_count`**Type**: `INT32`**Provider name**: `InitialInstanceCount`**Description**: The number of instances recommended to launch initially.
  - `instance_type`**Type**: `STRING`**Provider name**: `InstanceType`**Description**: The instance type recommended by Amazon SageMaker Inference Recommender.
  - `serverless_config`**Type**: `STRUCT`**Provider name**: `ServerlessConfig`
    - `max_concurrency`**Type**: `INT32`**Provider name**: `MaxConcurrency`**Description**: The maximum number of concurrent invocations your serverless endpoint can process.
    - `memory_size_in_mb`**Type**: `INT32`**Provider name**: `MemorySizeInMB`**Description**: The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
    - `provisioned_concurrency`**Type**: `INT32`**Provider name**: `ProvisionedConcurrency`**Description**: The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to `MaxConcurrency`.This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see [CreateInferenceRecommendationsJobs](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateInferenceRecommendationsJob.html).
  - `variant_name`**Type**: `STRING`**Provider name**: `VariantName`**Description**: The name of the production variant (deployed model) made during a recommendation job.
- `invocation_end_time`**Type**: `TIMESTAMP`**Provider name**: `InvocationEndTime`**Description**: A timestamp that shows when the benchmark completed.
- `invocation_start_time`**Type**: `TIMESTAMP`**Provider name**: `InvocationStartTime`**Description**: A timestamp that shows when the benchmark started.
- `metrics`**Type**: `STRUCT`**Provider name**: `Metrics`**Description**: The metrics used to decide what recommendation to make.
  - `cost_per_hour`**Type**: `FLOAT`**Provider name**: `CostPerHour`**Description**: Defines the cost per hour for the instance.
  - `cost_per_inference`**Type**: `FLOAT`**Provider name**: `CostPerInference`**Description**: Defines the cost per inference for the instance .
  - `cpu_utilization`**Type**: `FLOAT`**Provider name**: `CpuUtilization`**Description**: The expected CPU utilization at maximum invocations per minute for the instance. `NaN` indicates that the value is not available.
  - `max_invocations`**Type**: `INT32`**Provider name**: `MaxInvocations`**Description**: The expected maximum number of requests per minute for the instance.
  - `memory_utilization`**Type**: `FLOAT`**Provider name**: `MemoryUtilization`**Description**: The expected memory utilization at maximum invocations per minute for the instance. `NaN` indicates that the value is not available.
  - `model_latency`**Type**: `INT32`**Provider name**: `ModelLatency`**Description**: The expected model latency at maximum invocation per minute for the instance.
  - `model_setup_time`**Type**: `INT32`**Provider name**: `ModelSetupTime`**Description**: The time it takes to launch new compute resources for a serverless endpoint. The time can vary depending on the model size, how long it takes to download the model, and the start-up time of the container. `NaN` indicates that the value is not available.
- `model_configuration`**Type**: `STRUCT`**Provider name**: `ModelConfiguration`**Description**: Defines the model configuration.
  - `compilation_job_name`**Type**: `STRING`**Provider name**: `CompilationJobName`**Description**: The name of the compilation job used to create the recommended model artifacts.
  - `environment_parameters`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `EnvironmentParameters`**Description**: Defines the environment parameters that includes key, value types, and values.
    - `key`**Type**: `STRING`**Provider name**: `Key`**Description**: The environment key suggested by the Amazon SageMaker Inference Recommender.
    - `value`**Type**: `STRING`**Provider name**: `Value`**Description**: The value suggested by the Amazon SageMaker Inference Recommender.
    - `value_type`**Type**: `STRING`**Provider name**: `ValueType`**Description**: The value type suggested by the Amazon SageMaker Inference Recommender.
  - `inference_specification_name`**Type**: `STRING`**Provider name**: `InferenceSpecificationName`**Description**: The inference specification name in the model package version.
- `recommendation_id`**Type**: `STRING`**Provider name**: `RecommendationId`**Description**: The recommendation ID which uniquely identifies each recommendation.

## `input_config`{% #input_config %}

**Type**: `STRUCT`**Provider name**: `InputConfig`**Description**: Returns information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations you provided when you initiated the job.

- `container_config`**Type**: `STRUCT`**Provider name**: `ContainerConfig`**Description**: Specifies mandatory fields for running an Inference Recommender job. The fields specified in `ContainerConfig` override the corresponding fields in the model package.
  - `data_input_config`**Type**: `STRING`**Provider name**: `DataInputConfig`**Description**: Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. This field is used for optimizing your model using SageMaker Neo. For more information, see [DataInputConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_InputConfig.html#sagemaker-Type-InputConfig-DataInputConfig).
  - `domain`**Type**: `STRING`**Provider name**: `Domain`**Description**: The machine learning domain of the model and its components. Valid Values: `COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING`
  - `framework`**Type**: `STRING`**Provider name**: `Framework`**Description**: The machine learning framework of the container image. Valid Values: `TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN`
  - `framework_version`**Type**: `STRING`**Provider name**: `FrameworkVersion`**Description**: The framework version of the container image.
  - `nearest_model_name`**Type**: `STRING`**Provider name**: `NearestModelName`**Description**: The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model. Valid Values: `efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet`
  - `payload_config`**Type**: `STRUCT`**Provider name**: `PayloadConfig`**Description**: Specifies the `SamplePayloadUrl` and all other sample payload-related fields.
    - `sample_payload_url`**Type**: `STRING`**Provider name**: `SamplePayloadUrl`**Description**: The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
    - `supported_content_types`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `SupportedContentTypes`**Description**: The supported MIME types for the input data.
  - `supported_endpoint_type`**Type**: `STRING`**Provider name**: `SupportedEndpointType`**Description**: The endpoint type to receive recommendations for. By default this is null, and the results of the inference recommendation job return a combined list of both real-time and serverless benchmarks. By specifying a value for this field, you can receive a longer list of benchmarks for the desired endpoint type.
  - `supported_instance_types`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `SupportedInstanceTypes`**Description**: A list of the instance types that are used to generate inferences in real-time.
  - `supported_response_mime_types`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `SupportedResponseMIMETypes`**Description**: The supported MIME types for the output data.
  - `task`**Type**: `STRING`**Provider name**: `Task`**Description**: The machine learning task that the model accomplishes. Valid Values: `IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER`
- `endpoint_configurations`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `EndpointConfigurations`**Description**: Specifies the endpoint configuration to use for a job.
  - `environment_parameter_ranges`**Type**: `STRUCT`**Provider name**: `EnvironmentParameterRanges`**Description**: The parameter you want to benchmark against.
    - `categorical_parameter_ranges`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `CategoricalParameterRanges`**Description**: Specified a list of parameters for each category.
      - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The Name of the environment variable.
      - `value`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `Value`**Description**: The list of values you can pass.
  - `inference_specification_name`**Type**: `STRING`**Provider name**: `InferenceSpecificationName`**Description**: The inference specification name in the model package version.
  - `instance_type`**Type**: `STRING`**Provider name**: `InstanceType`**Description**: The instance types to use for the load test.
  - `serverless_config`**Type**: `STRUCT`**Provider name**: `ServerlessConfig`
    - `max_concurrency`**Type**: `INT32`**Provider name**: `MaxConcurrency`**Description**: The maximum number of concurrent invocations your serverless endpoint can process.
    - `memory_size_in_mb`**Type**: `INT32`**Provider name**: `MemorySizeInMB`**Description**: The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
    - `provisioned_concurrency`**Type**: `INT32`**Provider name**: `ProvisionedConcurrency`**Description**: The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to `MaxConcurrency`.This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see [CreateInferenceRecommendationsJobs](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateInferenceRecommendationsJob.html).
- `endpoints`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `Endpoints`**Description**: Existing customer endpoints on which to run an Inference Recommender job.
  - `endpoint_name`**Type**: `STRING`**Provider name**: `EndpointName`**Description**: The name of a customer's endpoint.
- `job_duration_in_seconds`**Type**: `INT32`**Provider name**: `JobDurationInSeconds`**Description**: Specifies the maximum duration of the job, in seconds. The maximum value is 18,000 seconds.
- `model_name`**Type**: `STRING`**Provider name**: `ModelName`**Description**: The name of the created model.
- `model_package_version_arn`**Type**: `STRING`**Provider name**: `ModelPackageVersionArn`**Description**: The Amazon Resource Name (ARN) of a versioned model package.
- `resource_limit`**Type**: `STRUCT`**Provider name**: `ResourceLimit`**Description**: Defines the resource limit of the job.
  - `max_number_of_tests`**Type**: `INT32`**Provider name**: `MaxNumberOfTests`**Description**: Defines the maximum number of load tests.
  - `max_parallel_of_tests`**Type**: `INT32`**Provider name**: `MaxParallelOfTests`**Description**: Defines the maximum number of parallel load tests.
- `traffic_pattern`**Type**: `STRUCT`**Provider name**: `TrafficPattern`**Description**: Specifies the traffic pattern of the job.
  - `phases`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `Phases`**Description**: Defines the phases traffic specification.
    - `duration_in_seconds`**Type**: `INT32`**Provider name**: `DurationInSeconds`**Description**: Specifies how long a traffic phase should be. For custom load tests, the value should be between 120 and 3600. This value should not exceed `JobDurationInSeconds`.
    - `initial_number_of_users`**Type**: `INT32`**Provider name**: `InitialNumberOfUsers`**Description**: Specifies how many concurrent users to start with. The value should be between 1 and 3.
    - `spawn_rate`**Type**: `INT32`**Provider name**: `SpawnRate`**Description**: Specified how many new users to spawn in a minute.
  - `stairs`**Type**: `STRUCT`**Provider name**: `Stairs`**Description**: Defines the stairs traffic pattern.
    - `duration_in_seconds`**Type**: `INT32`**Provider name**: `DurationInSeconds`**Description**: Defines how long each traffic step should be.
    - `number_of_steps`**Type**: `INT32`**Provider name**: `NumberOfSteps`**Description**: Specifies how many steps to perform during traffic.
    - `users_per_step`**Type**: `INT32`**Provider name**: `UsersPerStep`**Description**: Specifies how many new users to spawn in each step.
  - `traffic_type`**Type**: `STRING`**Provider name**: `TrafficType`**Description**: Defines the traffic patterns. Choose either `PHASES` or `STAIRS`.
- `volume_kms_key_id`**Type**: `STRING`**Provider name**: `VolumeKmsKeyId`**Description**: The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. This key will be passed to SageMaker Hosting for endpoint creation. The SageMaker execution role must have `kms:CreateGrant` permission in order to encrypt data on the storage volume of the endpoints created for inference recommendation. The inference recommendation job will fail asynchronously during endpoint configuration creation if the role passed does not have `kms:CreateGrant` permission. The `KmsKeyId` can be any of the following formats:
  - // KMS Key ID `"1234abcd-12ab-34cd-56ef-1234567890ab"`
  - // Amazon Resource Name (ARN) of a KMS Key `"arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"`
  - // KMS Key Alias `"alias/ExampleAlias"`
  - // Amazon Resource Name (ARN) of a KMS Key Alias `"arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"`
For more information about key identifiers, see [Key identifiers (KeyID)](https://docs.aws.amazon.com/kms/latest/developerguide/concepts.html#key-id-key-id) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
- `vpc_config`**Type**: `STRUCT`**Provider name**: `VpcConfig`**Description**: Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.
  - `security_group_ids`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `SecurityGroupIds`**Description**: The VPC security group IDs. IDs have the form of `sg-xxxxxxxx`. Specify the security groups for the VPC that is specified in the `Subnets` field.
  - `subnets`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `Subnets`**Description**: The ID of the subnets in the VPC to which you want to connect your model.

## `job_arn`{% #job_arn %}

**Type**: `STRING`**Provider name**: `JobArn`**Description**: The Amazon Resource Name (ARN) of the job.

## `job_description`{% #job_description %}

**Type**: `STRING`**Provider name**: `JobDescription`**Description**: The job description that you provided when you initiated the job.

## `job_name`{% #job_name %}

**Type**: `STRING`**Provider name**: `JobName`**Description**: The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.

## `job_type`{% #job_type %}

**Type**: `STRING`**Provider name**: `JobType`**Description**: The job type that you provided when you initiated the job.

## `last_modified_time`{% #last_modified_time %}

**Type**: `TIMESTAMP`**Provider name**: `LastModifiedTime`**Description**: A timestamp that shows when the job was last modified.

## `role_arn`{% #role_arn %}

**Type**: `STRING`**Provider name**: `RoleArn`**Description**: The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role you provided when you initiated the job.

## `status`{% #status %}

**Type**: `STRING`**Provider name**: `Status`**Description**: The status of the job.

## `stopping_conditions`{% #stopping_conditions %}

**Type**: `STRUCT`**Provider name**: `StoppingConditions`**Description**: The stopping conditions that you provided when you initiated the job.

- `flat_invocations`**Type**: `STRING`**Provider name**: `FlatInvocations`**Description**: Stops a load test when the number of invocations (TPS) peaks and flattens, which means that the instance has reached capacity. The default value is `Stop`. If you want the load test to continue after invocations have flattened, set the value to `Continue`.
- `max_invocations`**Type**: `INT32`**Provider name**: `MaxInvocations`**Description**: The maximum number of requests per minute expected for the endpoint.
- `model_latency_thresholds`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `ModelLatencyThresholds`**Description**: The interval of time taken by a model to respond as viewed from SageMaker. The interval includes the local communication time taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container.
  - `percentile`**Type**: `STRING`**Provider name**: `Percentile`**Description**: The model latency percentile threshold. Acceptable values are `P95` and `P99`. For custom load tests, specify the value as `P95`.
  - `value_in_milliseconds`**Type**: `INT32`**Provider name**: `ValueInMilliseconds`**Description**: The model latency percentile value in milliseconds.

## `tags`{% #tags %}

**Type**: `UNORDERED_LIST_STRING`