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Présentation
Google Cloud Machine Learning est un service géré qui vous permet de créer facilement des modèles d’apprentissage automatique applicables à n’importe quel type de données, quel que soit leur volume.
Recueillez des métriques de Google Machine Learning pour :
- Visualiser les performances de vos services d’apprentissage automatique
- Corréler les performances de vos services d’apprentissage automatique avec vos applications
Configuration
Installation
Si vous ne l’avez pas déjà fait, configurez d’abord l’intégration Google Cloud Platform. Aucune autre procédure d’installation n’est requise.
Collecte de logs
Les logs Google Cloud Machine Learning sont recueillis avec Google Cloud Logging et envoyés à un Cloud Pub/Sub via un forwarder Push HTTP. Si vous ne l’avez pas déjà fait, configurez un Cloud Pub/Sub à l’aide d’un forwarder Push HTTP.
Une fois cette opération effectuée, exportez vos logs Google Cloud Machine Learning depuis Google Cloud Logging vers le Pub/Sub :
- Accédez à la page Google Cloud Logging et filtrez les logs Google Cloud Machine Learning.
- Cliquez sur Create Export et nommez le récepteur.
- Choisissez Cloud Pub/Sub comme destination et sélectionnez le Pub/Sub créé à cette fin. Remarque : le Pub/Sub peut se situer dans un autre projet.
- Cliquez sur Create et attendez que le message de confirmation s’affiche.
Données collectées
Métriques
| |
|---|
gcp.ml.prediction.error_count
(count) | Cumulative count of prediction errors. |
gcp.ml.prediction.latencies.avg
(count) | The average latency of a certain type.
Shown as microsecond |
gcp.ml.prediction.latencies.samplecount
(count) | Latency of a certain type. |
gcp.ml.prediction.latencies.sumsqdev
(count) | Latency of a certain type. |
gcp.ml.prediction.online.accelerator.duty_cycle
(gauge) | Average fraction of time over the past sample period during which the accelerator(s) were actively processing.
Shown as percent |
gcp.ml.prediction.online.accelerator.memory.bytes_used
(gauge) | Amount of accelerator memory allocated by the model replica.
Shown as byte |
gcp.ml.prediction.online.cpu.utilization
(gauge) | Fraction of CPU allocated by the model replica and currently in use. May exceed 100% if the machine type has multiple CPUs.
Shown as percent |
gcp.ml.prediction.online.memory.bytes_used
(gauge) | Amount of memory allocated by the model replica and currently in use.
Shown as byte |
gcp.ml.prediction.online.network.bytes_received
(count) | Number of bytes received over the network by the model replica.
Shown as byte |
gcp.ml.prediction.online.network.bytes_sent
(count) | Number of bytes sent over the network by the model replica.
Shown as byte |
gcp.ml.prediction.online.replicas
(gauge) | Number of active model replicas. |
gcp.ml.prediction.online.target_replicas
(gauge) | Aspired number of active model replicas. |
gcp.ml.prediction.prediction_count
(count) | Cumulative count of predictions. |
gcp.ml.prediction.response_count
(count) | Cumulative count of different response codes. |
gcp.ml.training.accelerator.memory.utilization
(gauge) | Fraction of allocated accelerator memory that is currently in use. Values are numbers between 0.0 and 1.0, charts display the values as a percentage between 0% and 100%.
Shown as percent |
gcp.ml.training.accelerator.utilization
(gauge) | Fraction of allocated accelerator that is currently in use. Values are numbers between 0.0 and 1.0, charts display the values as a percentage between 0% and 100%.
Shown as percent |
gcp.ml.training.cpu.utilization
(gauge) | Fraction of allocated CPU that is currently in use. Values are numbers between 0.0 and 1.0, charts display the values as a percentage between 0% and 100%.
Shown as percent |
gcp.ml.training.memory.utilization
(gauge) | Fraction of allocated memory that is currently in use. Values are numbers between 0.0 and 1.0, charts display the values as a percentage between 0% and 100%.
Shown as percent |
gcp.ml.training.network.received_bytes_count
(count) | Number of bytes received by the training job over the network.
Shown as byte |
gcp.ml.training.network.sent_bytes_count
(count) | Number of bytes sent by the training job over the network.
Shown as byte |
Événements
L’intégration Google Cloud Machine Learning n’inclut aucun événement.
Checks de service
L’intégration Google Cloud Machine Learning n’inclut aucun check de service.
Dépannage
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