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Version: 4.4

Monitoring Dashboard

This guide describes the Grafana monitoring dashboards provided in the existing monitoring stack of Katonic MLOps Platform.

Overview​

Setting up monitoring for your Katonic Kubernetes cluster allows you to track your resource usage and analyze and debug application errors.

You can see the dashboard image from our Katonic Kubernetes cluster below:

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Katonic Monitoring Platform provides the following dashboards:

Kubernetes Compute Resources Node Dashboard​

Kubernetes Compute Resources Node dashboard will give you the data of the existing nodes present in your cluster with existing data source which contains the following:

  • Node CPU Usage

  • Node CPU Quota

  • Node Memory Usage

  • Node Memory Quota

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Kubernetes Compute Resources Pod Dashboard​

Kubernetes Compute Resources Pod dashboard will give you the data of the existing pods present in your cluster with existing data source and the namespace which contains the following:

  • Pod CPU Usage

  • CPU Throttling

  • Pod CPU Quota

  • Pod Memory Usage and Quota

  • Received and Transmitted Bandwidth of Pods

  • Rates of Packet received and transmitted of Pods

  • Rates of Packet Dropped received and transmitted of Pods

  • Storage I/O Distribution of Pods

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Kubernetes Networking Namespace (Workload) Dashboard​

Kubernetes Networking Namespace (Workload) dashboard will give you the data of the existing namespaces present in your cluster with existing data source, type of resource and resolution which contains the following:

  • Current Bandwidth of Namespaces

  • Average Bandwidth of Namespaces

  • Bandwidth History

  • Packets

  • Errors (Rates of received packets dropped and Rates of transmitted packets dropped)

  • Memory and CPU Usage of each namespaces

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Kubernetes Networking Workload Dashboard​

Kubernetes Networking Workload dashboard will give you the data of the networking workload present in your cluster with existing data source, namespaces , workload and type of resources which contains the following:

  • Current bandwidth of the workload

  • Average bandwidth of the workload

  • Bandwidth History

  • Network Packets transmitted and received

  • Errors (Rates of received packets dropped and Rates of transmitted packets dropped)

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Kubernetes Persistent Volumes Dashboard​

Kubernetes Persistent Volumes dashboard will give you the data of the persistent volumes present in your cluster with existing data source, namespaces and persistentvolumeClaims which contains the following:

  • Volume space usage by each namespace

  • Volume inodes usage

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Kubernetes Cluster Monitoring Dashboard​

Kubernetes Cluster Monitoring dashboard will give you the entire data of the cluster monitoring by existing nodes which contains the following:

  • Pod CPU Usage

  • Total Usage (Memory, CPU, Filesystem)

  • All Processor’s CPU Usage

  • Container Memory Usage

  • Network I/O Pressure

  • CPU Usage and Quota

  • Memory Usage and Quota

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Node-Exporter Dashboard​

Kubernetes Node-Exporter dashboard will give you the entire data of node exporters by the instance types present in the given cluster which contains the following:

  • CPU Usage and Load Average

  • Memory Usage

  • Disk I/O and Disk Usage

  • Network received and transmitted

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Other Models Dashboard​

Other Models dashboard will give you the statistics of your model deployed into your existing kubernetes cluster with the deployments present. It contains the following data:

  • Memory Usage

  • CPU Usage

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Classification Dashboard​

As documents are classified, you can view statistics about the classification process, such as how much time has passed since the process started, how much container memory and container CPU usage is done so far in the classification process.

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Regression Dashboard​

Regression dashboard will provide the statistics of a regression model which provides a function that describes the relationship between one or more independent variables and a response, dependent, or target variable

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NLP Dashboard​

NLP dashboard will provide the statistics of a NLP model which provides a function that describes the relationship between one or more text contents, like how much similar they are to each other, are they conveying the same message.These insights enable proactive maintenance, improvement, and optimization of the model, facilitating timely corrective actions and enhancing its overall performance in production environments.

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