Amazon SageMaker

Overview

Amazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can build and train machine learning models, and then directly deploy them into a production-ready hosted environment.

Enable this integration to see all your SageMaker metrics in Datadog.

Setup

Installation

If you haven’t already, set up the Amazon Web Services integration first.

Metric collection

  1. In the AWS integration page, ensure that SageMaker is enabled under the Metric Collection tab.
  2. Install the Datadog - Amazon SageMaker integration.

Log collection

Enable logging

Configure Amazon SageMaker to send logs either to a S3 bucket or to CloudWatch.

Note: If you log to a S3 bucket, make sure that amazon_sagemaker is set as Target prefix.

Send logs to Datadog

  1. If you haven’t already, set up the Datadog log collection AWS Lambda function.

  2. Once the lambda function is installed, manually add a trigger on the S3 bucket or CloudWatch log group that contains your Amazon SageMaker logs in the AWS console:

Data Collected

Metrics

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 percentage of CPU units that are used by the containers on an instance.
Shown as percent
aws.sagemaker.endpoints.disk_utilization
(gauge)
The percentage of disk space used by the containers on an instance uses.
Shown as percent
aws.sagemaker.endpoints.gpumemory_utilization
(gauge)
The percentage of GPU memory used by the containers on an instance.
Shown as percent
aws.sagemaker.endpoints.gpuutilization
(gauge)
The 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 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.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
(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_downloading_time.maximum
(count)
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
(count)
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
(count)
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
(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.minimum
(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_time.maximum
(count)
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
(count)
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
(count)
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
(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_loading_wait_time.maximum
(count)
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
(count)
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
(count)
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
(count)
The average time it takes to launch new compute resources for a serverless endpoint.
Shown as microsecond
aws.sagemaker.model_setup_time.maximum
(count)
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
(count)
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
(count)
The total amount of time takes to launch new compute resources for a serverless endpoint.
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.model_unloading_time.maximum
(count)
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
(count)
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
(count)
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
(count)
The average duration in milliseconds that the pipeline execution ran.
Shown as millisecond
aws.sagemaker.modelbuildingpipeline.execution_duration.maximum
(count)
The maximum duration in milliseconds that the pipeline execution ran.
Shown as millisecond
aws.sagemaker.modelbuildingpipeline.execution_duration.minimum
(count)
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
(count)
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
(count)
The average duration in milliseconds that the step ran.
Shown as millisecond
aws.sagemaker.modelbuildingpipeline.step_duration.maximum
(count)
The maximum duration in milliseconds that the step ran.
Shown as millisecond
aws.sagemaker.modelbuildingpipeline.step_duration.minimum
(count)
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
(count)
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
(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.processingjobs.cpuutilization
(gauge)
The percentage of CPU units that are used by the containers on an instance.
Shown as percent
aws.sagemaker.processingjobs.disk_utilization
(gauge)
The percentage of disk space used by the containers on an instance uses.
Shown as percent
aws.sagemaker.processingjobs.gpumemory_utilization
(gauge)
The percentage of GPU memory used by the containers on an instance.
Shown as percent
aws.sagemaker.processingjobs.gpuutilization
(gauge)
The percentage of GPU units that are used by the containers on an instance.
Shown as percent
aws.sagemaker.processingjobs.memory_utilization
(gauge)
The 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 percentage of CPU units that are used by the containers on an instance.
Shown as percent
aws.sagemaker.trainingjobs.disk_utilization
(gauge)
The percentage of disk space used by the containers on an instance uses.
Shown as percent
aws.sagemaker.trainingjobs.gpumemory_utilization
(gauge)
The percentage of GPU memory used by the containers on an instance.
Shown as percent
aws.sagemaker.trainingjobs.gpuutilization
(gauge)
The percentage of GPU units that are used by the containers on an instance.
Shown as percent
aws.sagemaker.trainingjobs.memory_utilization
(gauge)
The percentage of memory that is used by the containers on an instance.
Shown as percent
aws.sagemaker.transformjobs.cpuutilization
(gauge)
The percentage of CPU units that are used by the containers on an instance.
Shown as percent
aws.sagemaker.transformjobs.disk_utilization
(gauge)
The percentage of disk space used by the containers on an instance uses.
Shown as percent
aws.sagemaker.transformjobs.gpumemory_utilization
(gauge)
The percentage of GPU memory used by the containers on an instance.
Shown as percent
aws.sagemaker.transformjobs.gpuutilization
(gauge)
The percentage of GPU units that are used by the containers on an instance.
Shown as percent
aws.sagemaker.transformjobs.memory_utilization
(gauge)
The 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.

Events

The Amazon SageMaker integration does not include any events.

Service Checks

The Amazon SageMaker integration does not include any service checks.

Troubleshooting

Need help? Contact Datadog support.