Amazon SageMaker

개요

Amazon SageMaker는 완전관리형 머신러닝 서비스입니다. Amazon SageMaker를 사용해 데이터 과학자와 개발자는 머신러닝 모델을 구축하고 트레이닝한 다음 직접 프로덕션 레디 호스팅 환경에 배포할 수 있습니다.

이 통합을 활성화하여 Datadog에서 모든 SageMaker 메트릭을 확인하세요.

설정

설치

이미 하지 않은 경우 먼저 Amazon Web Services 통합을 설정하세요.

메트릭 수집

AWS 통합 페이지에서 Metric Collection 탭에 SageMaker가 활성화되어 있는지 확인하세요.
Datadog - Amazon SageMaker 통합을 설치하세요.

로그 수집

로깅 활성화

Amazon SageMaker를 설정하여 S3 버킷 또는 클라우드와치(CloudWatch) 중 하나로 로그를 전송하세요.

참고: S3 버킷에 로그인한 경우 amazon_sagemaker가 _대상 접두어_로 설정되어 있는지 확인하세요.

Datadog에 로그 전송

이미 하지 않은 경우 Datadog 로그 수집 AWS 람다 함수를 설정하세요.
람다 함수가 설치되면 AWS 콘솔에서 Amazon SageMaker 로그를 포함하는 S3 버킷 또는 클라우드와치(CloudWatch) 로그 그룹에 대해 수동으로 트리거를 추가합니다.
- S3 버킷에서 직접 트리거 추가
- 클라우드와치(CloudWatch) 로그 그룹에 수동 트리거 추가

수집한 데이터

메트릭

aws.sagemaker.consumed_read_requests_units (count)	The average number of consumed read units over the specified time period.
aws.sagemaker.consumed_read_requests_units.maximum (count)	The maximum number of consumed read units over the specified time period.
aws.sagemaker.consumed_read_requests_units.minimum (count)	The minimum number of consumed read units over the specified time period.
aws.sagemaker.consumed_read_requests_units.p90 (count)	The 90th percentile of consumed read units over the specified time period.
aws.sagemaker.consumed_read_requests_units.p95 (count)	The 95th percentile of consumed read units over the specified time period.
aws.sagemaker.consumed_read_requests_units.p99 (count)	The 99th percentile of consumed read units over the specified time period.
aws.sagemaker.consumed_read_requests_units.sample_count (count)	The sample count of consumed read units over the specified time period.
aws.sagemaker.consumed_read_requests_units.sum (count)	The sum of consumed read units over the specified time period.
aws.sagemaker.consumed_write_requests_units (count)	The average number of consumed write units over the specified time period.
aws.sagemaker.consumed_write_requests_units.maximum (count)	The maximum number of consumed write units over the specified time period.
aws.sagemaker.consumed_write_requests_units.minimum (count)	The minimum number of consumed write units over the specified time period.
aws.sagemaker.consumed_write_requests_units.p90 (count)	The 90th percentile of consumed write units over the specified time period.
aws.sagemaker.consumed_write_requests_units.p95 (count)	The 95th percentile of consumed write units over the specified time period.
aws.sagemaker.consumed_write_requests_units.p99 (count)	The 99th percentile of consumed write units over the specified time period.
aws.sagemaker.consumed_write_requests_units.sample_count (count)	The sample count of consumed write units over the specified time period.
aws.sagemaker.consumed_write_requests_units.sum (count)	The sum of consumed write units over the specified time period.
aws.sagemaker.endpoints.cpuutilization (gauge)	The average percentage of CPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.endpoints.cpuutilization.maximum (gauge)	The maximum percentage of CPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.endpoints.cpuutilization.minimum (gauge)	The minimum percentage of CPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.endpoints.disk_utilization (gauge)	The average percentage of disk space used by the containers on an instance uses. Shown as percent
aws.sagemaker.endpoints.disk_utilization.maximum (gauge)	The maximum percentage of disk space used by the containers on an instance uses. Shown as percent
aws.sagemaker.endpoints.disk_utilization.minimum (gauge)	The minimum percentage of disk space used by the containers on an instance uses. Shown as percent
aws.sagemaker.endpoints.gpu_memory_utilization (gauge)	The average percentage of GPU memory used by the containers on an instance. Shown as percent
aws.sagemaker.endpoints.gpu_memory_utilization.maximum (gauge)	The maximum percentage of GPU memory used by the containers on an instance. Shown as percent
aws.sagemaker.endpoints.gpu_memory_utilization.minimum (gauge)	The minimum percentage of GPU memory used by the containers on an instance. Shown as percent
aws.sagemaker.endpoints.gpu_utilization (gauge)	The average percentage of GPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.endpoints.gpu_utilization.maximum (gauge)	The maximum percentage of GPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.endpoints.gpu_utilization.minimum (gauge)	The minimum percentage of GPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.endpoints.loaded_model_count (count)	The number of models loaded in the containers of the multi-model endpoint. This metric is emitted per instance.
aws.sagemaker.endpoints.loaded_model_count.maximum (count)	The maximum number of models loaded in the containers of the multi-model endpoint. This metric is emitted per instance.
aws.sagemaker.endpoints.loaded_model_count.minimum (count)	The minimum number of models loaded in the containers of the multi-model endpoint. This metric is emitted per instance.
aws.sagemaker.endpoints.loaded_model_count.sample_count (count)	The sample count of models loaded in the containers of the multi-model endpoint. This metric is emitted per instance.
aws.sagemaker.endpoints.loaded_model_count.sum (count)	The sum of models loaded in the containers of the multi-model endpoint. This metric is emitted per instance.
aws.sagemaker.endpoints.memory_utilization (gauge)	The average percentage of memory that is used by the containers on an instance. Shown as percent
aws.sagemaker.endpoints.memory_utilization.maximum (gauge)	The maximum percentage of memory that is used by the containers on an instance. Shown as percent
aws.sagemaker.endpoints.memory_utilization.minimum (gauge)	The minimum percentage of memory that is used by the containers on an instance. Shown as percent
aws.sagemaker.invocation_4xx_errors (count)	The average number of InvokeEndpoint requests where the model returned a 4xx HTTP response code. Shown as request
aws.sagemaker.invocation_4xx_errors.sum (count)	The sum of the number of InvokeEndpoint requests where the model returned a 4xx HTTP response code. Shown as request
aws.sagemaker.invocation_5xx_errors (count)	The average number of InvokeEndpoint requests where the model returned a 5xx HTTP response code. Shown as request
aws.sagemaker.invocation_5xx_errors.sum (count)	The sum of the number of InvokeEndpoint requests where the model returned a 5xx HTTP response code. Shown as request
aws.sagemaker.invocation_model_errors (count)	The number of model invocation requests which did not result in 2XX HTTP response. This includes 4XX/5XX status codes, low-level socket errors, malformed HTTP responses, and request timeouts.
aws.sagemaker.invocations (count)	The number of InvokeEndpoint requests sent to a model endpoint. Shown as request
aws.sagemaker.invocations.maximum (count)	The maximum of the number of InvokeEndpoint requests sent to a model endpoint. Shown as request
aws.sagemaker.invocations.minimum (count)	The minimum of the number of InvokeEndpoint requests sent to a model endpoint. Shown as request
aws.sagemaker.invocations.sample_count (count)	The sample count of the number of InvokeEndpoint requests sent to a model endpoint. Shown as request
aws.sagemaker.invocations_per_instance (count)	The number of invocations sent to a model normalized by InstanceCount in each ProductionVariant.
aws.sagemaker.invocations_per_instance.sum (count)	The sum of invocations sent to a model normalized by InstanceCount in each ProductionVariant.
aws.sagemaker.jobs_failed (count)	The average number of occurrences a single labeling job failed. Shown as job
aws.sagemaker.jobs_failed.sample_count (count)	The sample count of occurrences a single labeling job failed. Shown as job
aws.sagemaker.jobs_failed.sum (count)	The sum of occurrences a single labeling job failed. Shown as job
aws.sagemaker.jobs_stopped (count)	The average number of occurrences a single labeling job was stopped. Shown as job
aws.sagemaker.jobs_stopped.sample_count (count)	The sample count of occurrences a single labeling job was stopped. Shown as job
aws.sagemaker.jobs_stopped.sum (count)	The sum of occurrences a single labeling job was stopped. Shown as job
aws.sagemaker.labelingjobs.dataset_objects_auto_annotated (count)	The average number of dataset objects auto-annotated in a labeling job.
aws.sagemaker.labelingjobs.dataset_objects_auto_annotated.max (count)	The maximum number of dataset objects auto-annotated in a labeling job.
aws.sagemaker.labelingjobs.dataset_objects_human_annotated (count)	The average number of dataset objects annotated by a human in a labeling job.
aws.sagemaker.labelingjobs.dataset_objects_human_annotated.max (count)	The maximum number of dataset objects annotated by a human in a labeling job.
aws.sagemaker.labelingjobs.dataset_objects_labeling_failed (count)	The number of dataset objects that failed labeling in a labeling job.
aws.sagemaker.labelingjobs.dataset_objects_labeling_failed.max (count)	The number of dataset objects that failed labeling in a labeling job.
aws.sagemaker.labelingjobs.jobs_succeeded (count)	The average number of occurrences a single labeling job succeeded. Shown as job
aws.sagemaker.labelingjobs.jobs_succeeded.sample_count (count)	The sample count of occurrences a single labeling job succeeded. Shown as job
aws.sagemaker.labelingjobs.jobs_succeeded.sum (count)	The sum of occurrences a single labeling job succeeded. Shown as job
aws.sagemaker.labelingjobs.total_dataset_objects_labeled (count)	The average number of dataset objects labeled successfully in a labeling job.
aws.sagemaker.labelingjobs.total_dataset_objects_labeled.maximum (count)	The maximum number of dataset objects labeled successfully in a labeling job.
aws.sagemaker.model_cache_hit (count)	The number of InvokeEndpoint requests sent to the multi-model endpoint for which the model was already loaded. Shown as request
aws.sagemaker.model_cache_hit.maximum (count)	The maximum number of InvokeEndpoint requests sent to the multi-model endpoint for which the model was already loaded. Shown as request
aws.sagemaker.model_cache_hit.minimum (count)	The minimum number of InvokeEndpoint requests sent to the multi-model endpoint for which the model was already loaded. Shown as request
aws.sagemaker.model_cache_hit.sample_count (count)	The sample count of InvokeEndpoint requests sent to the multi-model endpoint for which the model was already loaded. Shown as request
aws.sagemaker.model_cache_hit.sum (count)	The sum of InvokeEndpoint requests sent to the multi-model endpoint for which the model was already loaded. Shown as request
aws.sagemaker.model_downloading_time (gauge)	The interval of time that it takes to download the model from Amazon Simple Storage Service (Amazon S3). Shown as microsecond
aws.sagemaker.model_downloading_time.maximum (gauge)	The maximum interval of time that it takes to download the model from Amazon Simple Storage Service (Amazon S3). Shown as microsecond
aws.sagemaker.model_downloading_time.minimum (gauge)	The minimum interval of time that it takes to download the model from Amazon Simple Storage Service (Amazon S3). Shown as microsecond
aws.sagemaker.model_downloading_time.sample_count (count)	The sample count interval of time that it takes to download the model from Amazon Simple Storage Service (Amazon S3). Shown as microsecond
aws.sagemaker.model_downloading_time.sum (gauge)	The sum interval of time that it takes to download the model from Amazon Simple Storage Service (Amazon S3). Shown as microsecond
aws.sagemaker.model_latency (gauge)	The average interval of time taken by a model to respond as viewed from Amazon SageMaker. Shown as microsecond
aws.sagemaker.model_latency.maximum (gauge)	The maximum interval of time taken by a model to respond as viewed from Amazon SageMaker. Shown as microsecond
aws.sagemaker.model_latency.minimum (gauge)	The minimum interval of time taken by a model to respond as viewed from Amazon SageMaker. Shown as microsecond
aws.sagemaker.model_latency.sample_count (count)	The sample count interval of time taken by a model to respond as viewed from Amazon SageMaker. Shown as microsecond
aws.sagemaker.model_latency.sum (gauge)	The sum of the interval of time taken by a model to respond as viewed from Amazon SageMaker. Shown as microsecond
aws.sagemaker.model_loading_time (gauge)	The interval of time that it takes to load the model through the container's LoadModel API call. Shown as microsecond
aws.sagemaker.model_loading_time.maximum (gauge)	The maximum interval of time that it takes to load the model through the container's LoadModel API call. Shown as microsecond
aws.sagemaker.model_loading_time.minimum (gauge)	The minimum interval of time that it takes to load the model through the container's LoadModel API call. Shown as microsecond
aws.sagemaker.model_loading_time.sample_count (count)	The sample count interval of time that it takes to load the model through the container's LoadModel API call. Shown as microsecond
aws.sagemaker.model_loading_time.sum (gauge)	The sum interval of time that it takes to load the model through the container's LoadModel API call. Shown as microsecond
aws.sagemaker.model_loading_wait_time (gauge)	The interval of time that an invocation request has waited for the target model to be downloaded, or loaded, or both in order to perform inference. Shown as microsecond
aws.sagemaker.model_loading_wait_time.maximum (gauge)	The maximum interval of time that an invocation request has waited for the target model to be downloaded, or loaded, or both in order to perform inference. Shown as microsecond
aws.sagemaker.model_loading_wait_time.minimum (gauge)	The minimum interval of time that an invocation request has waited for the target model to be downloaded, or loaded, or both in order to perform inference. Shown as microsecond
aws.sagemaker.model_loading_wait_time.sample_count (count)	The sample count interval of time that an invocation request has waited for the target model to be downloaded, or loaded, or both in order to perform inference. Shown as microsecond
aws.sagemaker.model_loading_wait_time.sum (gauge)	The sum interval of time that an invocation request has waited for the target model to be downloaded, or loaded, or both in order to perform inference. Shown as microsecond
aws.sagemaker.model_setup_time (gauge)	The average time it takes to launch new compute resources for a serverless endpoint. Shown as microsecond
aws.sagemaker.model_setup_time.maximum (gauge)	The maximum interval of time it takes to launch new compute resources for a serverless endpoint. Shown as microsecond
aws.sagemaker.model_setup_time.minimum (gauge)	The minimum interval of time it takes to launch new compute resources for a serverless endpoint. Shown as microsecond
aws.sagemaker.model_setup_time.sample_count (count)	The sample_count of the amount of time it takes to launch new compute resources for a serverless endpoint. Shown as microsecond
aws.sagemaker.model_setup_time.sum (gauge)	The total amount of time takes to launch new compute resources for a serverless endpoint. Shown as microsecond
aws.sagemaker.model_unloading_time (gauge)	The interval of time that it takes to unload the model through the container's UnloadModel API call. Shown as microsecond
aws.sagemaker.model_unloading_time.maximum (gauge)	The maximum interval of time that it takes to unload the model through the container's UnloadModel API call. Shown as microsecond
aws.sagemaker.model_unloading_time.minimum (gauge)	The minimum interval of time that it takes to unload the model through the container's UnloadModel API call. Shown as microsecond
aws.sagemaker.model_unloading_time.sample_count (count)	The sample count interval of time that it takes to unload the model through the container's UnloadModel API call. Shown as microsecond
aws.sagemaker.model_unloading_time.sum (gauge)	The sum interval of time that it takes to unload the model through the container's UnloadModel API call. Shown as microsecond
aws.sagemaker.modelbuildingpipeline.execution_duration (gauge)	The average duration in milliseconds that the pipeline execution ran. Shown as millisecond
aws.sagemaker.modelbuildingpipeline.execution_duration.maximum (gauge)	The maximum duration in milliseconds that the pipeline execution ran. Shown as millisecond
aws.sagemaker.modelbuildingpipeline.execution_duration.minimum (gauge)	The minimum duration in milliseconds that the pipeline execution ran. Shown as millisecond
aws.sagemaker.modelbuildingpipeline.execution_duration.sample_count (count)	The sample count duration in milliseconds that the pipeline execution ran. Shown as millisecond
aws.sagemaker.modelbuildingpipeline.execution_duration.sum (gauge)	The sum duration in milliseconds that the pipeline execution ran. Shown as millisecond
aws.sagemaker.modelbuildingpipeline.execution_failed (count)	The average number of steps that failed.
aws.sagemaker.modelbuildingpipeline.execution_failed.sum (count)	The sum of steps that failed.
aws.sagemaker.modelbuildingpipeline.execution_started (count)	The average number of pipeline executions that started.
aws.sagemaker.modelbuildingpipeline.execution_started.sum (count)	The sum of pipeline executions that started.
aws.sagemaker.modelbuildingpipeline.execution_stopped (count)	The average number of pipeline executions that stopped.
aws.sagemaker.modelbuildingpipeline.execution_stopped.sum (count)	The sum of pipeline executions that stopped.
aws.sagemaker.modelbuildingpipeline.execution_succeeded (count)	The average number of pipeline executions that succeeded.
aws.sagemaker.modelbuildingpipeline.execution_succeeded.sum (count)	The sum of pipeline executions that succeeded.
aws.sagemaker.modelbuildingpipeline.step_duration (gauge)	The average duration in milliseconds that the step ran. Shown as millisecond
aws.sagemaker.modelbuildingpipeline.step_duration.maximum (gauge)	The maximum duration in milliseconds that the step ran. Shown as millisecond
aws.sagemaker.modelbuildingpipeline.step_duration.minimum (gauge)	The minimum duration in milliseconds that the step ran. Shown as millisecond
aws.sagemaker.modelbuildingpipeline.step_duration.sample_count (count)	The sample count duration in milliseconds that the step ran. Shown as millisecond
aws.sagemaker.modelbuildingpipeline.step_duration.sum (gauge)	The sum duration in milliseconds that the step ran. Shown as millisecond
aws.sagemaker.modelbuildingpipeline.step_failed (count)	The average number of steps that failed.
aws.sagemaker.modelbuildingpipeline.step_failed.sum (count)	The sum of steps that failed.
aws.sagemaker.modelbuildingpipeline.step_started (count)	The average number of steps that started.
aws.sagemaker.modelbuildingpipeline.step_started.sum (count)	The sum of steps that started.
aws.sagemaker.modelbuildingpipeline.step_stopped (count)	The average number of steps that stopped.
aws.sagemaker.modelbuildingpipeline.step_stopped.sum (count)	The sum of steps that stopped.
aws.sagemaker.modelbuildingpipeline.step_succeeded (count)	The average number of steps that succeeded.
aws.sagemaker.modelbuildingpipeline.step_succeeded.sum (count)	The sum of steps that succeeded.
aws.sagemaker.overhead_latency (gauge)	The average interval of time added to the time taken to respond to a client request by Amazon SageMaker overheads. Shown as microsecond
aws.sagemaker.overhead_latency.maximum (gauge)	The maximum interval of time added to the time taken to respond to a client request by Amazon SageMaker overheads. Shown as microsecond
aws.sagemaker.overhead_latency.minimum (gauge)	The minimum interval of time added to the time taken to respond to a client request by Amazon SageMaker overheads. Shown as microsecond
aws.sagemaker.overhead_latency.sample_count (count)	The sample count of the interval of time added to the time taken to respond to a client request by Amazon SageMaker overheads. Shown as microsecond
aws.sagemaker.overhead_latency.sum (gauge)	The sum of the interval of time added to the time taken to respond to a client request by Amazon SageMaker overheads. Shown as microsecond
aws.sagemaker.processingjobs.cpuutilization (gauge)	The average percentage of CPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.processingjobs.cpuutilization.maximum (gauge)	The maximum percentage of CPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.processingjobs.cpuutilization.minimum (gauge)	The minimum percentage of CPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.processingjobs.disk_utilization (gauge)	The average percentage of disk space used by the containers on an instance uses. Shown as percent
aws.sagemaker.processingjobs.disk_utilization.maximum (gauge)	The maximum percentage of disk space used by the containers on an instance uses. Shown as percent
aws.sagemaker.processingjobs.disk_utilization.minimum (gauge)	The minimum percentage of disk space used by the containers on an instance uses. Shown as percent
aws.sagemaker.processingjobs.gpu_memory_utilization (gauge)	The average percentage of GPU memory used by the containers on an instance. Shown as percent
aws.sagemaker.processingjobs.gpu_memory_utilization.maximum (gauge)	The maximum percentage of GPU memory used by the containers on an instance. Shown as percent
aws.sagemaker.processingjobs.gpu_memory_utilization.minimum (gauge)	The minimum percentage of GPU memory used by the containers on an instance. Shown as percent
aws.sagemaker.processingjobs.gpu_utilization (gauge)	The average percentage of GPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.processingjobs.gpu_utilization.maximum (gauge)	The maximum percentage of GPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.processingjobs.gpu_utilization.minimum (gauge)	The minimum percentage of GPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.processingjobs.memory_utilization (gauge)	The average percentage of memory that is used by the containers on an instance. Shown as percent
aws.sagemaker.processingjobs.memory_utilization.maximum (gauge)	The maximum percentage of memory that is used by the containers on an instance. Shown as percent
aws.sagemaker.processingjobs.memory_utilization.minimum (gauge)	The minimum percentage of memory that is used by the containers on an instance. Shown as percent
aws.sagemaker.tasks_returned (count)	The average number of occurrences a single task was returned.
aws.sagemaker.tasks_returned.sample_count (count)	The sample count of occurrences a single task was returned.
aws.sagemaker.tasks_returned.sum (count)	The sum of occurrences a single task was returned.
aws.sagemaker.trainingjobs.cpuutilization (gauge)	The average percentage of CPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.trainingjobs.cpuutilization.maximum (gauge)	The maximum percentage of CPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.trainingjobs.cpuutilization.minimum (gauge)	The minimum percentage of CPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.trainingjobs.disk_utilization (gauge)	The average percentage of disk space used by the containers on an instance uses. Shown as percent
aws.sagemaker.trainingjobs.disk_utilization.maximum (gauge)	The maximum percentage of disk space used by the containers on an instance uses. Shown as percent
aws.sagemaker.trainingjobs.disk_utilization.minimum (gauge)	The minimum percentage of disk space used by the containers on an instance uses. Shown as percent
aws.sagemaker.trainingjobs.gpu_memory_utilization (gauge)	The average percentage of GPU memory used by the containers on an instance. Shown as percent
aws.sagemaker.trainingjobs.gpu_memory_utilization.maximum (gauge)	The maximum percentage of GPU memory used by the containers on an instance. Shown as percent
aws.sagemaker.trainingjobs.gpu_memory_utilization.minimum (gauge)	The minimum percentage of GPU memory used by the containers on an instance. Shown as percent
aws.sagemaker.trainingjobs.gpu_utilization (gauge)	The average percentage of GPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.trainingjobs.gpu_utilization.maximum (gauge)	The maximum percentage of GPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.trainingjobs.gpu_utilization.minimum (gauge)	The minimum percentage of GPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.trainingjobs.memory_utilization (gauge)	The average percentage of memory that is used by the containers on an instance. Shown as percent
aws.sagemaker.trainingjobs.memory_utilization.maximum (gauge)	The maximum percentage of memory that is used by the containers on an instance. Shown as percent
aws.sagemaker.trainingjobs.memory_utilization.minimum (gauge)	The minimum percentage of memory that is used by the containers on an instance. Shown as percent
aws.sagemaker.transformjobs.cpuutilization (gauge)	The average percentage of CPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.transformjobs.cpuutilization.maximum (gauge)	The maximum percentage of CPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.transformjobs.cpuutilization.minimum (gauge)	The minimum percentage of CPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.transformjobs.disk_utilization (gauge)	The average percentage of disk space used by the containers on an instance uses. Shown as percent
aws.sagemaker.transformjobs.disk_utilization.maximum (gauge)	The maximum percentage of disk space used by the containers on an instance uses. Shown as percent
aws.sagemaker.transformjobs.disk_utilization.minimum (gauge)	The minimum percentage of disk space used by the containers on an instance uses. Shown as percent
aws.sagemaker.transformjobs.gpu_memory_utilization (gauge)	The average percentage of GPU memory used by the containers on an instance. Shown as percent
aws.sagemaker.transformjobs.gpu_memory_utilization.maximum (gauge)	The maximum percentage of GPU memory used by the containers on an instance. Shown as percent
aws.sagemaker.transformjobs.gpu_memory_utilization.minimum (gauge)	The minimum percentage of GPU memory used by the containers on an instance. Shown as percent
aws.sagemaker.transformjobs.gpu_utilization (gauge)	The average percentage of GPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.transformjobs.gpu_utilization.maximum (gauge)	The maximum percentage of GPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.transformjobs.gpu_utilization.minimum (gauge)	The minimum percentage of GPU units that are used by the containers on an instance. Shown as percent
aws.sagemaker.transformjobs.memory_utilization (gauge)	The average percentage of memory that is used by the containers on an instance. Shown as percent
aws.sagemaker.transformjobs.memory_utilization.maximum (gauge)	The maximum percentage of memory that is used by the containers on an instance. Shown as percent
aws.sagemaker.transformjobs.memory_utilization.minimum (gauge)	The minimum percentage of memory that is used by the containers on an instance. Shown as percent
aws.sagemaker.workteam.active_workers (count)	The average number of single active workers on a private work team that submitted, released, or declined a task.
aws.sagemaker.workteam.active_workers.sample_count (count)	The sample count of single active workers on a private work team that submitted, released, or declined a task.
aws.sagemaker.workteam.active_workers.sum (count)	The sum of single active workers on a private work team that submitted, released, or declined a task.
aws.sagemaker.workteam.tasks_accepted (count)	The average number of occurrences a single task was accepted by a worker.
aws.sagemaker.workteam.tasks_accepted.sample_count (count)	The sample count of occurrences a single task was accepted by a worker.
aws.sagemaker.workteam.tasks_accepted.sum (count)	The sum of occurrences a single task was accepted by a worker.
aws.sagemaker.workteam.tasks_declined (count)	The average number of occurrences a single task was declined by a worker.
aws.sagemaker.workteam.tasks_declined.sample_count (count)	The sample count of occurrences a single task was declined by a worker.
aws.sagemaker.workteam.tasks_declined.sum (count)	The sum of occurrences a single task was declined by a worker.
aws.sagemaker.workteam.tasks_submitted (count)	The average number of occurrences a single task was submitted/completed by a private worker.
aws.sagemaker.workteam.tasks_submitted.sample_count (count)	The average number of occurrences a single task was submitted/completed by a private worker.
aws.sagemaker.workteam.tasks_submitted.sum (count)	The average number of occurrences a single task was submitted/completed by a private worker.
aws.sagemaker.workteam.time_spent (count)	The average time spent on a task completed by a private worker.
aws.sagemaker.workteam.time_spent.sample_count (count)	The average time spent on a task completed by a private worker.
aws.sagemaker.workteam.time_spent.sum (count)	The average time spent on a task completed by a private worker.

이벤트

Amazon SageMaker 통합에는 이벤트가 포함되어 있지 않습니다.

서비스 점검

Amazon SageMaker 통합에는 서비스 점검이 포함되어 있지 않습니다.

즉시 사용 가능한 모니터링

Datadog는 SageMaker 엔드포인트 및 작업에 대해 즉시 사용 가능한 대시보드를 제공합니다.

SageMaker 엔드포인트

SageMaker 엔드포인트 대시보드를 사용하여 즉시 추가 설정 없이 SageMaker 엔드포인트의 상태 및 성능 모니터링을 시작할 수 있습니다. 어느 엔드포인트에 오류, 예상보다 높은 지연 또는 트래픽 급증이 있는지 확인하세요. 인스턴스 유형과 CPU, GPU, 메모리 및 디스크 활용률 메트릭을 사용해 확장 정책 선택 항목 을 검토하고 교정하세요.