--- title: Getting Started with Datadog description: Datadog, the leading service for cloud-scale monitoring. breadcrumbs: Docs > Infrastructure > Datadog Resource Catalog --- # aws_sagemaker_optimizationjob{% #aws_sagemaker_optimizationjob %} ## `account_id`{% #account_id %} **Type**: `STRING` ## `creation_time`{% #creation_time %} **Type**: `TIMESTAMP`**Provider name**: `CreationTime`**Description**: The time when you created the optimization job. ## `deployment_instance_type`{% #deployment_instance_type %} **Type**: `STRING`**Provider name**: `DeploymentInstanceType`**Description**: The type of instance that hosts the optimized model that you create with the optimization job. ## `failure_reason`{% #failure_reason %} **Type**: `STRING`**Provider name**: `FailureReason`**Description**: If the optimization job status is `FAILED`, the reason for the failure. ## `last_modified_time`{% #last_modified_time %} **Type**: `TIMESTAMP`**Provider name**: `LastModifiedTime`**Description**: The time when the optimization job was last updated. ## `model_source`{% #model_source %} **Type**: `STRUCT`**Provider name**: `ModelSource`**Description**: The location of the source model to optimize with an optimization job. - `s3`**Type**: `STRUCT`**Provider name**: `S3`**Description**: The Amazon S3 location of a source model to optimize with an optimization job. - `model_access_config`**Type**: `STRUCT`**Provider name**: `ModelAccessConfig`**Description**: The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA). - `accept_eula`**Type**: `BOOLEAN`**Provider name**: `AcceptEula`**Description**: Specifies agreement to the model end-user license agreement (EULA). The `AcceptEula` value must be explicitly defined as `True` in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. - `s3_uri`**Type**: `STRING`**Provider name**: `S3Uri`**Description**: An Amazon S3 URI that locates a source model to optimize with an optimization job. ## `optimization_configs`{% #optimization_configs %} **Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `OptimizationConfigs`**Description**: Settings for each of the optimization techniques that the job applies. - `model_compilation_config`**Type**: `STRUCT`**Provider name**: `ModelCompilationConfig`**Description**: Settings for the model compilation technique that's applied by a model optimization job. - `image`**Type**: `STRING`**Provider name**: `Image`**Description**: The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization. - `override_environment`**Type**: `MAP_STRING_STRING`**Provider name**: `OverrideEnvironment`**Description**: Environment variables that override the default ones in the model container. - `model_quantization_config`**Type**: `STRUCT`**Provider name**: `ModelQuantizationConfig`**Description**: Settings for the model quantization technique that's applied by a model optimization job. - `image`**Type**: `STRING`**Provider name**: `Image`**Description**: The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization. - `override_environment`**Type**: `MAP_STRING_STRING`**Provider name**: `OverrideEnvironment`**Description**: Environment variables that override the default ones in the model container. - `model_sharding_config`**Type**: `STRUCT`**Provider name**: `ModelShardingConfig`**Description**: Settings for the model sharding technique that's applied by a model optimization job. - `image`**Type**: `STRING`**Provider name**: `Image`**Description**: The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization. - `override_environment`**Type**: `MAP_STRING_STRING`**Provider name**: `OverrideEnvironment`**Description**: Environment variables that override the default ones in the model container. ## `optimization_end_time`{% #optimization_end_time %} **Type**: `TIMESTAMP`**Provider name**: `OptimizationEndTime`**Description**: The time when the optimization job finished processing. ## `optimization_environment`{% #optimization_environment %} **Type**: `MAP_STRING_STRING`**Provider name**: `OptimizationEnvironment`**Description**: The environment variables to set in the model container. ## `optimization_job_arn`{% #optimization_job_arn %} **Type**: `STRING`**Provider name**: `OptimizationJobArn`**Description**: The Amazon Resource Name (ARN) of the optimization job. ## `optimization_job_name`{% #optimization_job_name %} **Type**: `STRING`**Provider name**: `OptimizationJobName`**Description**: The name that you assigned to the optimization job. ## `optimization_job_status`{% #optimization_job_status %} **Type**: `STRING`**Provider name**: `OptimizationJobStatus`**Description**: The current status of the optimization job. ## `optimization_output`{% #optimization_output %} **Type**: `STRUCT`**Provider name**: `OptimizationOutput`**Description**: Output values produced by an optimization job. - `recommended_inference_image`**Type**: `STRING`**Provider name**: `RecommendedInferenceImage`**Description**: The image that SageMaker recommends that you use to host the optimized model that you created with an optimization job. ## `optimization_start_time`{% #optimization_start_time %} **Type**: `TIMESTAMP`**Provider name**: `OptimizationStartTime`**Description**: The time when the optimization job started. ## `output_config`{% #output_config %} **Type**: `STRUCT`**Provider name**: `OutputConfig`**Description**: Details for where to store the optimized model that you create with the optimization job. - `kms_key_id`**Type**: `STRING`**Provider name**: `KmsKeyId`**Description**: The Amazon Resource Name (ARN) of a key in Amazon Web Services KMS. SageMaker uses they key to encrypt the artifacts of the optimized model when SageMaker uploads the model to Amazon S3. - `s3_output_location`**Type**: `STRING`**Provider name**: `S3OutputLocation`**Description**: The Amazon S3 URI for where to store the optimized model that you create with an optimization job. ## `role_arn`{% #role_arn %} **Type**: `STRING`**Provider name**: `RoleArn`**Description**: The ARN of the IAM role that you assigned to the optimization job. ## `stopping_condition`{% #stopping_condition %} **Type**: `STRUCT`**Provider name**: `StoppingCondition` - `max_pending_time_in_seconds`**Type**: `INT32`**Provider name**: `MaxPendingTimeInSeconds`**Description**: The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.When working with training jobs that use capacity from [training plans](https://docs.aws.amazon.com/sagemaker/latest/dg/reserve-capacity-with-training-plans.html), not all `Pending` job states count against the `MaxPendingTimeInSeconds` limit. The following scenarios do not increment the `MaxPendingTimeInSeconds` counter: - The plan is in a `Scheduled` state: Jobs queued (in `Pending` status) before a plan's start date (waiting for scheduled start time) - Between capacity reservations: Jobs temporarily back to `Pending` status between two capacity reservation periods `MaxPendingTimeInSeconds` only increments when jobs are actively waiting for capacity in an `Active` plan. - `max_runtime_in_seconds`**Type**: `INT32`**Provider name**: `MaxRuntimeInSeconds`**Description**: The maximum length of time, in seconds, that a training or compilation job can run before it is stopped. For compilation jobs, if the job does not complete during this time, a `TimeOut` error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model. For all other jobs, if the job does not complete during this time, SageMaker ends the job. When `RetryStrategy` is specified in the job request, `MaxRuntimeInSeconds` specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days. The maximum time that a `TrainingJob` can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days. - `max_wait_time_in_seconds`**Type**: `INT32`**Provider name**: `MaxWaitTimeInSeconds`**Description**: The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than `MaxRuntimeInSeconds`. If the job does not complete during this time, SageMaker ends the job. When `RetryStrategy` is specified in the job request, `MaxWaitTimeInSeconds` specifies the maximum time for all of the attempts in total, not each individual attempt. ## `tags`{% #tags %} **Type**: `UNORDERED_LIST_STRING` ## `vpc_config`{% #vpc_config %} **Type**: `STRUCT`**Provider name**: `VpcConfig`**Description**: A VPC in Amazon VPC that your optimized model has access to. - `security_group_ids`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `SecurityGroupIds`**Description**: The VPC security group IDs, in the form `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 optimized model.