Amazon SageMaker

Amazon SageMaker

Crawler Crawler

概要

Amazon SageMaker は、フルマネージド型の機械学習サービスです。Amazon SageMaker を使用して、データサイエンティストや開発者は、機械学習モデルを構築およびトレーニングした後に、実稼働準備ができたホスト環境にモデルを直接デプロイすることができます。

このインテグレーションを有効にすると、Datadog にすべての SageMaker メトリクスを表示できます。

セットアップ

インストール

Amazon Web Services インテグレーションをまだセットアップしていない場合は、最初にセットアップします。

メトリクスの収集

  1. AWS インテグレーションタイルのメトリクス収集で、SageMaker をオンにします。
  2. Datadog - Amazon SageMaker インテグレーションをインストールします。

ログの収集

ログの有効化

Amazon SageMaker から S3 バケットまたは CloudWatch のいずれかにログを送信するよう構成します。

: S3 バケットにログを送る場合は、Target prefixamazon_sagemaker に設定されているかを確認してください。

ログを Datadog に送信する方法

  1. Datadog ログコレクション AWS Lambda 関数 をまだ設定していない場合は、設定を行ってください。

  2. lambda 関数がインストールされたら、AWS コンソールから、Amazon SageMaker ログを含む S3 バケットまたは CloudWatch のロググループに手動でトリガーを追加します。

収集データ

メトリクス

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

イベント

Amazon SageMaker インテグレーションには、イベントは含まれません。

サービスのチェック

Amazon SageMaker インテグレーションには、サービスのチェック機能は含まれません。

トラブルシューティング

ご不明な点は、Datadog のサポートチームまでお問合せください。