aws.sagemaker.cpu_utilization (count) | The percentage of CPU units that are used by the containers on an instance. Shown as percent |
aws.sagemaker.dataset_objects_auto_annotated (count) | The number of dataset objects auto-annotated in a labeling job. Shown as object |
aws.sagemaker.dataset_objects_human_annotated (count) | The number of dataset objects annotated by a human in a labeling job. Shown as object |
aws.sagemaker.dataset_objects_labeling_failed (count) | The number of dataset objects that failed labeling in a labeling job. Shown as object |
aws.sagemaker.disk_utilization (count) | The percentage of disk space used by the containers on an instance uses. Shown as percent |
aws.sagemaker.gpu_memory_utilization (count) | The percentage of GPU memory used by the containers on an instance. Shown as percent |
aws.sagemaker.gpu_utilization (count) | The percentage of GPU units that are 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.invocations (count) | The sum 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.sample_count (count) | The sample count of the number of InvokeEndpoint requests sent to a model endpoint. Shown as request |
aws.sagemaker.jobs_failed (count) | The sum of the number of labeling jobs that failed. Shown as job |
aws.sagemaker.jobs_failed.sample_count (count) | The sample count of the number of labeling jobs that failed. Shown as job |
aws.sagemaker.jobs_stopped (count) | The sum of the number of labeling jobs that were stopped. Shown as job |
aws.sagemaker.jobs_stopped.sample_count (count) | The sample count of the number of labeling jobs that were stopped. Shown as job |
aws.sagemaker.jobs_succeeded (count) | The sum of the number of labeling jobs that succeeded. Shown as job |
aws.sagemaker.jobs_succeeded.sample_count (count) | The sample count number of labeling jobs that succeeded. Shown as job |
aws.sagemaker.memory_utilization (count) | The percentage of memory that is used by the containers on an instance. Shown as percent |
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_downloading_time (count) | The interval of time that it takes to download the model from Amazon Simple Storage Service (Amazon S3). Shown as microsecond |
aws.sagemaker.model_latency (count) | The average interval of time taken by a model to respond as viewed from Amazon SageMaker. Shown as microsecond |
aws.sagemaker.model_latency.maximum (count) | The maximum interval of time taken by a model to respond as viewed from Amazon SageMaker. Shown as microsecond |
aws.sagemaker.model_latency.mininmum (count) | 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 (count) | 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 (count) | 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_wait_time (count) | 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_unloading_time (count) | The interval of time that it takes to unload the model through the container's UnloadModel API call. Shown as microsecond |
aws.sagemaker.overhead_latency (count) | 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 (count) | 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 (count) | 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 (count) | 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.total_dataset_objects_labeled (count) | The maximum number of dataset objects labeled successfully in a labeling job. Shown as object |