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# Loki components {{< youtube id="\_hv4i84Z68s" >}} Loki is a modular system that contains many components that can either be run together (in "single binary" mode with target `all`), in logical groups (in "simple scalable deployment" mode with targets `read`, `write`, `backend`), or individually (in "microservice" mod... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/components.md | main | loki | [
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level and enables us to scale the distributors up or down and have the per-distributor limit adjust accordingly. For instance, say we have 10 distributors and tenant A has a 10MB rate limit. Each distributor will allow up to 1MB/s before limiting. Now, say another large tenant joins the cluster and we need to spin up 1... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/components.md | main | loki | [
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achieve consistent hashing; all [ingesters](#ingester) register themselves into the hash ring with a set of tokens they own. Each token is a random unsigned 32-bit number. Along with a set of tokens, ingesters register their state into the hash ring. The state `JOINING`, and `ACTIVE` may all receive write requests, whi... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/components.md | main | loki | [
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1. A flush occurs. Whenever a chunk is compressed and marked as read-only, a writable chunk takes its place. If an ingester process crashes or exits abruptly, all the data that has not yet been flushed will be lost. Loki is usually configured to replicate multiple replicas (usually 3) of each log to mitigate this risk.... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/components.md | main | loki | [
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This process is used to avoid flushing all chunks when shutting down, which is a slow process. ## Query frontend The \*\*query frontend\*\* is an \*\*optional service\*\* providing the querier's API endpoints and can be used to accelerate the read path. When the query frontend is in place, incoming query requests shoul... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/components.md | main | loki | [
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volume query results similar to the [metric query](#metric-queries) results. This cache is only applicable when using single store TSDB. ## Query scheduler The \*\*query scheduler\*\* is an \*\*optional service\*\* providing more [advanced queuing functionality](https://grafana.com/docs/loki//operations/query-fairness/... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/components.md | main | loki | [
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can be managed via the ruler API or directly by uploading the files to object storage. Alternatively, the ruler can also delegate rule evaluation to the query frontend. This mode is called remote rule evaluation and is used to gain the advantages of query splitting, query sharding, and caching from the query frontend. ... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/components.md | main | loki | [
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# Loki deployment modes Loki is a distributed system consisting of many microservices. It also has a unique build model where all of those microservices exist within the same binary. You can configure the behavior of the single binary with the `-target` command-line flag to specify which microservices will run on start... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/deployment-modes.md | main | loki | [
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stateless and can be run as a Kubernetes Deployment that can be scaled automatically (Note that in the official helm chart it is currently deployed as a stateful set). It contains the following components: \* Query Frontend \* Querier - `-target=backend` - The backend target is stateful, and is controlled by a Kubernet... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/deployment-modes.md | main | loki | [
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# Get started with Grafana Loki {{< youtube id="1uk8LtQqsZQ" >}} Loki is a horizontally scalable, highly available, multi-tenant log aggregation system inspired by Prometheus. It's designed to be very cost-effective and easy to operate. It doesn't index the contents of the logs, but rather a set of labels for each log ... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/_index.md | main | loki | [
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# Modify default OpenTelemetry labels When Grafana Loki first started supporting OpenTelemetry, we selected a set of [default resource attributes to promote to labels](https://grafana.com/docs/loki//get-started/labels/#default-labels-for-opentelemetry). We included some labels such as k8s.pod.name to be consistent with... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/labels/modify-default-labels.md | main | loki | [
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Helm chart to demote `k8s.pod.name` and `service.instance.id` from index labels to structured metadata. ```yaml # Enable pod log collection for the cluster. Will collect logs from all pods in both the meta and loki namespace. podLogs: enabled: true collector: alloy-singleton #List of attributes to use as index labels i... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/labels/modify-default-labels.md | main | loki | [
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# Cardinality The cardinality of a data attribute is the number of distinct values that the attribute can have. For example, a boolean column in a database, which can only have a value of either `true` or `false` has a cardinality of 2. High cardinality refers to a column or row in a database that can have many possibl... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/labels/cardinality.md | main | loki | [
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label. [Query acceleration with Blooms](https://grafana.com/docs/loki//operations/bloom-filters/) also utilizes structured metadata. {{< /admonition >}} | https://github.com/grafana/loki/blob/main/docs/sources/get-started/labels/cardinality.md | main | loki | [
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# What is structured metadata {{< admonition type="warning" >}} Structured metadata was added to chunk format V4 which is used if the schema version is greater or equal to `13`. See [Schema Config](https://grafana.com/docs/loki//configure/storage/#schema-config) for more details about schema versions. {{< /admonition >... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/labels/structured-metadata.md | main | loki | [
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[max\_structured\_metadata\_entries\_count: | default = 128] ``` {{< /admonition >}} ## Querying structured metadata Structured metadata is extracted automatically for each returned log line and added to the labels returned for the query. You can use labels of structured metadata to filter log line using a [label filte... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/labels/structured-metadata.md | main | loki | [
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# Label best practices Grafana Loki is under active development, and we are constantly working to improve performance. But here are some of the most current best practices for labels that will give you the best experience with Loki. ## Static labels are good Use labels for things like regions, clusters, servers, applic... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/labels/bp-labels.md | main | loki | [
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dynamic labels with Loki: - Ensure the labels have low cardinality, ideally limited to tens of values. - Use labels with long-lived values, such as the initial segment of an HTTP path: `/load`, `/save`, `/update`. - Do not extract ephemeral values like a trace ID or an order ID into a label; the values should be static... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/labels/bp-labels.md | main | loki | [
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# Understand labels Labels are a crucial part of Loki. They allow Loki to organize and group together log messages into log streams. Each log stream must have at least one label to be stored and queried in Loki. In this topic we'll learn about labels and why your choice of labels is important when shipping logs to Loki... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/labels/_index.md | main | loki | [
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select resource attributes as labels. Although the default config selects more than 15 Resource Attributes, some are mutually exclusive. {{< /admonition >}} {{< admonition type="tip" >}} For Grafana Cloud Logs, see the [current OpenTelemetry guidance](https://grafana.com/docs/grafana-cloud/send-data/otlp/otlp-format-co... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/labels/_index.md | main | loki | [
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or virtual machines, the hostname should be stored in [structured metadata](https://grafana.com/docs/loki//get-started/labels/structured-metadata/). If your logs had the example labels above, then you might query them in LogQL like this: `{namespace="mynamespace", cluster="cluster123" filename="/var/log/myapp.log"}` Un... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/labels/_index.md | main | loki | [
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`{environment="production", app="fast\_app"}` Now the "fast\_app" and "slow\_app" will ship logs to different streams, allowing each to maintain their order of ingestion. ## Loki labels examples The way that labels are added to logs is configured in the client that you use to send logs to Loki. The specific configurati... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/labels/_index.md | main | loki | [
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-0500] "GET /1986.js HTTP/1.1" 200 932 "-" "Mozilla/5.0 (Windows; U; Windows NT 5.1; de; rv:1.9.1.7) Gecko/20091221 Firefox/3.5.7 GTB6" {job="apache",env="dev",action="POST",status\_code="200"} 11.11.11.12 - frank [25/Jan/2000:14:00:02 -0500] "POST /1986.js HTTP/1.1" 200 932 "-" "Mozilla/5.0 (Windows; U; Windows NT 5.1... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/labels/_index.md | main | loki | [
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# Quickstart to run Loki locally If you want to experiment with Loki, you can run Loki locally using the Docker Compose file that ships with Loki. It runs Loki in the [simple scalable deployment](https://grafana.com/docs/loki//get-started/deployment-modes/#simple-scalable) mode and includes a sample application to gene... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/quick-start/quick-start.md | main | loki | [
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Grafana Alloy is running. - You can access the Grafana Alloy UI at [http://localhost:12345](http://localhost:12345). 6. (Optional) You can check all the containers are running by running the following command: ```bash docker ps -a ``` ## View your logs in Grafana After you have collected logs, you will want to view the... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/quick-start/quick-start.md | main | loki | [
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logfmt parser\*\*, by clicking \*\*Use this query\*\*. 1. On the Explore tab, click \*\*Label browser\*\*, in the dialog select a container and click \*\*Show logs\*\*. For a thorough introduction to LogQL, refer to the [LogQL reference](https://grafana.com/docs/loki//query/). ## Sample queries (code view) Here are som... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/quick-start/quick-start.md | main | loki | [
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# Loki Tutorial This quickstart guide will walk you through deploying Loki in single binary mode (also known as [monolithic mode](https://grafana.com/docs/loki//get-started/deployment-modes/#monolithic-mode)) using Docker Compose. Grafana Loki is only one component of the Grafana observability stack for logs. In this t... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/quick-start/tutorial.md | main | loki | [
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with Grafana Logs Drilldown](https://grafana.com/docs/grafana/latest/explore/simplified-exploration/logs/get-started/). ## Collect logs from a sample application Currently, the Loki stack is collecting logs about itself. To provide a more realistic example, you can deploy a sample application that generates logs. The s... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/quick-start/tutorial.md | main | loki | [
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1. From the menu in the dashboard header, select the Loki data source (2). This displays the Loki query editor. In the query editor you use the Loki query language, [LogQL](https://grafana.com/docs/loki//query/), to query your logs. To learn more about the query editor, refer to the [query editor documentation](https:/... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/quick-start/tutorial.md | main | loki | [
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of `ERROR` and a `line` attribute of `58`. LogQL also supports metrics queries. Metrics are useful for abstracting the raw log data aggregating attributes into numeric values. This allows you to utilize more visualization options in Grafana as well as generate alerts on your logs. For example, you can use a metric quer... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/quick-start/tutorial.md | main | loki | [
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are connected: \* \*\*discovery.docker\*\*: This component queries the metadata of the Docker environment via the Docker socket and discovers new containers, as well as providing metadata about the containers. \* \*\*discovery.relabel\*\*: This component converts a metadata (`\_\_meta\_docker\_container\_name`) label i... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/quick-start/tutorial.md | main | loki | [
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documentation. ### Grafana Loki Data source The final piece of the puzzle is the Grafana Loki data source. This is used by Grafana to connect to Loki and query the logs. Grafana has multiple ways to define a data source: \* \*\*Direct\*\*: This is where you define the data source in the Grafana UI. \* \*\*Provisioning\... | https://github.com/grafana/loki/blob/main/docs/sources/get-started/quick-start/tutorial.md | main | loki | [
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# Contributor Covenant Code of Conduct ## Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex char... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/CODE_OF_CONDUCT.md | main | ckad-exercises | [
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 # State Persistence (8%) kubernetes.io > Documentation > Tasks > Configure Pods and Containers > [Configure a Pod to Use a Volume for Storage](https://kubernetes.io/docs/tasks/configure-pod-container/configure-volume-storage/) kubernetes.io > Doc... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/g.state.md | main | ckad-exercises | [
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```bash kubectl create -f pvc.yaml ``` Show the PersistentVolumeClaims and PersistentVolumes: ```bash kubectl get pvc # will show as 'Bound' kubectl get pv # will show as 'Bound' as well ``` ### Create a busybox pod with command 'sleep 3600', save it on pod.yaml. Mount the PersistentVolumeClaim to '/etc/foo'. Connect t... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/g.state.md | main | ckad-exercises | [
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 # Pod design (20%) [Labels And Annotations](#labels-and-annotations) [Deployments](#deployments) [Jobs](#jobs) [Cron Jobs](#cron-jobs) ## Labels and Annotations kubernetes.io > Documentation > Concepts > Overview > Working with Kubernetes O... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/c.pod_design.md | main | ckad-exercises | [
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`nodeName` forces the Pod to be bound to a specific node (bypassing the scheduler). For more details, see the official docs: [https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#nodename](https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#nodename) ```yaml apiVersion: v1 kind:... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/c.pod_design.md | main | ckad-exercises | [
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confirm that the replicas are OK show ```bash kubectl rollout history deploy nginx kubectl get deploy nginx kubectl get rs # check that a new replica set has been created kubectl get po ``` ### Undo the latest rollout and verify that new pods have the old image (nginx:1.18.0) show ```bash kubectl rollout undo deploy ng... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/c.pod_design.md | main | ckad-exercises | [
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from within a Pod: ``` # run a wget to the Service my-app-svc kubectl run -it --rm --restart=Never busybox --image=gcr.io/google-containers/busybox --command -- wget -qO- my-app-svc version-1 ``` Deploy 1 replica of v2: ``` apiVersion: apps/v1 kind: Deployment metadata: name: my-app-v2 labels: app: my-app spec: replica... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/c.pod_design.md | main | ckad-exercises | [
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``` ```bash kubectl create -f job.yaml ``` Verify that it has been completed: ```bash kubectl get job busybox -w # will take two and a half minutes kubectl delete jobs busybox ``` ### Create the same job, but make it run 5 parallel times show ```bash vi job.yaml ``` Add job.spec.parallelism=5 ```YAML apiVersion: batch/... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/c.pod_design.md | main | ckad-exercises | [
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- -c - date; echo Hello from the Kubernetes cluster image: busybox name: time-limited-job resources: {} restartPolicy: Never schedule: '\* \* \* \* \*' status: {} ``` ### Create a cron job with image busybox that runs every minute and writes 'date; echo Hello from the Kubernetes cluster' to standard output. The cron jo... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/c.pod_design.md | main | ckad-exercises | [
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 # Services and Networking (13%) ### Create a pod with image nginx called nginx and expose its port 80 show ```bash kubectl run nginx --image=nginx --restart=Never --port=80 --expose # observe that a pod as well as a service are created ``` ###... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/f.services.md | main | ckad-exercises | [
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--port=6262 --target-port=8080 kubectl get service foo # you will see ClusterIP as well as port 6262 kubectl get endpoints foo # you will see the IPs of the three replica pods, listening on port 8080 ``` ### Create a temp busybox pod and connect via wget to foo service. Verify that each time there's a different hostnam... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/f.services.md | main | ckad-exercises | [
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# Define, build and modify container images - Note: The topic is part of the new CKAD syllabus. Here are a few examples of using \*\*podman\*\* to manage the life cycle of container images. The use of \*\*docker\*\* had been the industry standard for many years, but now large companies like [Red Hat](https://www.redhat... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/j.podman.md | main | ckad-exercises | [
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skipped: already exists Copying blob 8e47efbf2b7e skipped: already exists Copying blob 42e0f5a91e40 skipped: already exists Copying config ef4b14a72d done Writing manifest to image destination Storing signatures ef4b14a72d02ae0577eb0632d084c057777725c279e12ccf5b0c6e4ff5fd598b ``` ### Create a container without running/... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/j.podman.md | main | ckad-exercises | [
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 # Configuration (18%) [ConfigMaps](#configmaps) [SecurityContext](#securitycontext) [Resource Requests and Limits](#resource-requests-and-limits) [Limit Ranges](#limit-ranges) [Resource Quotas](#resource-quotas) [Secrets](#secrets) [Servi... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/d.configuration.md | main | ckad-exercises | [
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- name: myvolume # the name that you specified in pod.spec.volumes.name mountPath: /etc/lala # the path inside your container dnsPolicy: ClusterFirst restartPolicy: Never status: {} ``` ```bash kubectl create -f pod.yaml kubectl exec -it nginx -- /bin/sh cd /etc/lala ls # will show var8 var9 cat var8 # will show val8 `... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/d.configuration.md | main | ckad-exercises | [
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2Gi ``` ```bash kubectl apply -f rq-one.yaml ``` or ```bash kubectl create quota my-rq --namespace=one --hard=requests.cpu=1,requests.memory=1Gi,limits.cpu=2,limits.memory=2Gi ``` ### Attempt to create a pod with resource requests `cpu=2`, `memory=3Gi` and limits `cpu=3`, `memory=4Gi` in namespace `one` show ```bash vi... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/d.configuration.md | main | ckad-exercises | [
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pod in env variable called 'USERNAME' show ```bash kubectl delete po nginx kubectl run nginx --image=nginx --restart=Never -o yaml --dry-run=client > pod.yaml vi pod.yaml ``` ```YAML apiVersion: v1 kind: Pod metadata: creationTimestamp: null labels: run: nginx name: nginx spec: containers: - image: nginx imagePullPolic... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/d.configuration.md | main | ckad-exercises | [
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create -f sa.yaml ``` ### Create an nginx pod that uses 'myuser' as a service account show ```bash kubectl run nginx --image=nginx --restart=Never -o yaml --dry-run=client > pod.yaml vi pod.yaml ``` ```YAML apiVersion: v1 kind: Pod metadata: creationTimestamp: null labels: run: nginx name: nginx spec: serviceAccountNam... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/d.configuration.md | main | ckad-exercises | [
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 # Observability (18%) ## Liveness, readiness and startup probes kubernetes.io > Documentation > Tasks > Configure Pods and Containers > [Configure Liveness, Readiness and Startup Probes](https://kubernetes.io/docs/tasks/configure-pod-cont... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/e.observability.md | main | ckad-exercises | [
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... | 0.061296 |
### Create a busybox pod that runs 'ls /notexist'. Determine if there's an error (of course there is), see it. In the end, delete the pod show ```bash kubectl run busybox --restart=Never --image=busybox -- /bin/sh -c 'ls /notexist' # show that there's an error kubectl logs busybox kubectl describe po busybox kubectl de... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/e.observability.md | main | ckad-exercises | [
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[](LICENSE) [](http://makeapullrequest.com) [ # Core Concepts (13%) kubernetes.io > Documentation > Reference > kubectl CLI > [kubectl Cheat Sheet](https://kubernetes.io/docs/reference/kubectl/cheatsheet/) kubernetes.io > Documentation > Tasks > Monitoring, Logging, and Debugging > ... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/a.core_concepts.md | main | ckad-exercises | [
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recreated' kubectl get po nginx -w # watch it ``` \*Note\*: some time after changing the image, you should see that the value in the `RESTARTS` column has been increased by 1, because the container has been restarted, as stated in the events shown at the bottom of the `kubectl describe pod` command: ``` Events: Type Re... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/a.core_concepts.md | main | ckad-exercises | [
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# Extend the Kubernetes API with CRD (CustomResourceDefinition) - Note: CRD is part of the new CKAD syllabus. Here are a few examples of installing custom resource into the Kubernetes API by creating a CRD. ## CRD in K8s ### Create a CustomResourceDefinition manifest file for an Operator with the following specificatio... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/i.crd.md | main | ckad-exercises | [
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 # Multi-container Pods (10%) ### Create a Pod with two containers, both with image busybox and command "echo hello; sleep 3600". Connect to the second container and run 'ls' show The easiest way to do it is create a pod with a single c... | https://github.com/dgkanatsios/CKAD-exercises/blob/main/b.multi_container_pods.md | main | ckad-exercises | [
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//!!NODE\_ROOT [."topic"] [[contribute,contribute.title]] = Contribute to this guide :info\_doctype: chapter :info\_titleabbrev: Contribute Anyone can contribute to the best practices guide. The EKS Best Practices Guide is written in the AsciiDoc format on GitHub. == Summary for existing contributors \* Open the https:... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/contribute.adoc | mainline | aws-eks-best-practices | [
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Edit a single page You can rapidly update individual pages by using GitHub. Each page contains an "📝 Edit this page on GitHub" link at the bottom. . Navigate to the page in this guide you want to edit . Click the "Edit this page on GitHub" link at the bottom . Click the edit pencil icon on the top right of the GitHub ... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/contribute.adoc | mainline | aws-eks-best-practices | [
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"diagram of VPC with three availability zones" is appropriate alt-text. . Update the following example with the alt-text and image filename. Insert at the desired location. + ==== +image::[]+ ==== + For example, + ==== +image::eks-data-plane-connectivity.jpeg[Network diagram]+ ==== == Check style with Vale . https://va... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/contribute.adoc | mainline | aws-eks-best-practices | [
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//!!NODE\_ROOT [[top]] = Amazon EKS :doctype: book :toc: left :icons: font :experimental: :idprefix: :idseparator: - :info\_doctype: book :info\_title: Amazon EKS :info\_subtitle: Best Practices Guide :info\_abstract: Learn best practices for operating EKS clusters. :info\_corpauthor: AWS :info\_publisher: AWS :info\_c... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/book.adoc | mainline | aws-eks-best-practices | [
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//!!NODE\_ROOT [."topic"] [[introduction,introduction.title]] = Amazon EKS Best Practices Guide :doctype: book :sectnums: :toc: left :icons: font :experimental: :idprefix: :idseparator: - :sourcedir: . :info\_doctype: chapter :info\_title: Amazon EKS Best Practices Guide :info\_titleabbrev: Introduction :info\_abstract... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/index.adoc | mainline | aws-eks-best-practices | [
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//!!NODE\_ROOT [."topic"] [[aiml-cpu-inference,aiml-cpu-inference.title]] = CPU Inference and Orchestration on EKS :info\_doctype: section :imagesdir: images/ :info\_title: CPU Inference and Orchestration :info\_abstract: Best practices for running AI inference and orchestration workloads on CPU instances with Amazon E... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_cpu_inference.adoc | mainline | aws-eks-best-practices | [
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AI pipelines, every GPU inference call is surrounded by CPU work: tool execution, context assembly, vector search, embedding lookups, guardrails, response validation, memory management, and orchestration logic. As agents grow more complex (more tools, longer chains, multi-step reasoning), these CPU workloads grow super... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_cpu_inference.adoc | mainline | aws-eks-best-practices | [
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outputs. For any interactive or latency-sensitive use case, 70B+ models belong on GPU or Trainium. === Quick Benchmark Workflow Before committing to an instance family, we recommend running a structured benchmark comparing your candidate CPU families (arm64 and x86) against GPU on a single comparable metric: \*cost-per... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_cpu_inference.adoc | mainline | aws-eks-best-practices | [
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with Karpenter managing node lifecycle and KEDA scaling on observed load. This pattern extends naturally into RAG architectures: embedding generation, document chunking, and retrieval from OpenSearch all run cost-effectively on CPU nodes, feeding results to a GPU-hosted LLM only for the final generation step. The CPU f... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_cpu_inference.adoc | mainline | aws-eks-best-practices | [
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it still helps for longer inputs. TIP: Q4\_K\_M quality degradation varies by model and task. Always evaluate on your own dataset before deploying to production. === Bin-packing for dense serving For classical ML and embedding models (typically <500MB each), the goal is \*maximum pod density per node at stable tail lat... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_cpu_inference.adoc | mainline | aws-eks-best-practices | [
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Triton, TorchServe) expose Prometheus-compatible metrics at a `/metrics` endpoint. The metric names vary by server, but the concepts are the same. \*Key metrics to instrument:\* [cols="2,3,2", options="header"] |=== |Metric Category |Description |Alerting Threshold |Requests processing / in-flight |Number of requests c... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_cpu_inference.adoc | mainline | aws-eks-best-practices | [
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dataset on your quantized SLM and compare. . \*Evaluate\* — Define your quality threshold before testing, for example, "SLM accuracy within 5 percentage points of baseline." The right threshold depends on the task: a classifier reviewed by humans can tolerate more errors than a system making automatic decisions. === Ho... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_cpu_inference.adoc | mainline | aws-eks-best-practices | [
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//!!NODE\_ROOT [."topic"] [[aiml-networking,aiml-networking.title]] = AI/ML on EKS - Networking :info\_doctype: section :imagesdir: images/ :info\_title: Networking :info\_abstract: Networking :info\_titleabbrev: Networking :authors: ["Leah Tucker"] :date: 2025-05-30 TIP: https://aws-experience.com/emea/smb/events/seri... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_networking.adoc | mainline | aws-eks-best-practices | [
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//!!NODE\_ROOT [."topic"] [[aiml-performance,aiml-performance.title]] = AI/ML on EKS - Performance :info\_doctype: section :imagesdir: images/ :info\_title: Application Scaling and Performance :info\_abstract: Application Scaling and Performance :info\_titleabbrev: Performance :authors: ["Leah Tucker"] :date: 2025-05-3... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_performance.adoc | mainline | aws-eks-best-practices | [
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extra infrastructure, compatible with all HuggingFace models, simple Kubernetes deployment. \* \*\*Cons\*\*: Restricts to single request/simple batching, slow token generation (no optimized kernels), memory inefficient, lacks scaling/monitoring, and involves long startup times. \* \*\*Recommendation\*\*: Use for initia... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_performance.adoc | mainline | aws-eks-best-practices | [
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to understand the difference between baseline and burst bandwidth (see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-instance-network-bandwidth.html[Amazon EC2 instance network bandwidth]). To minimize time-to-ready for your workloads, select instance sizes with sufficient baseline network bandwidth relative ... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_performance.adoc | mainline | aws-eks-best-practices | [
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https://containerd.io/[containerd] where images are stored after being pulled from a registry. Pre-pulling ensures images are available locally, avoiding download delays during pod startup. This approach is particularly useful when images change often (e.g., frequent updates), when EBS snapshots are not preconfigured, ... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_performance.adoc | mainline | aws-eks-best-practices | [
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//!!NODE\_ROOT [."topic"] [[aiml-security,aiml-security.title]] = AI/ML on EKS - Security :info\_doctype: section :imagesdir: images/ :info\_title: Security :info\_abstract: Security :info\_titleabbrev: Security :authors: ["Leah Tucker"] :date: 2025-07-01 TIP: https://aws-experience.com/emea/smb/events/series/get-hands... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_security.adoc | mainline | aws-eks-best-practices | [
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//!!NODE\_ROOT [."topic"] [[aiml-storage,aiml-storage.title]] = AI/ML on EKS - Storage :info\_doctype: section :imagesdir: images/ :info\_title: Storage :info\_abstract: Storage :info\_titleabbrev: Storage :authors: ["Leah Tucker"] :date: 2025-05-30 TIP: https://aws-experience.com/emea/smb/events/series/get-hands-on-wi... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_storage.adoc | mainline | aws-eks-best-practices | [
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serving, and you require native Amazon S3 data repository integration, we recommend https://docs.aws.amazon.com/fsx/latest/LustreGuide/what-is.html[Amazon FSx for Lustre]. FSx for Lustre provides a fully managed high performance parallel filesystem that is designed for compute-intensive workloads like high-performance ... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_storage.adoc | mainline | aws-eks-best-practices | [
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larger than the `ImportedFileChunkSize` will be stored on multiple OSTs with a stripe count based on the `ImportedFileChunkSize` defined value (default 1GiB). If you have large files, we recommend tuning this parameter to a higher value. \*\* \*\*Placement\*\*: Deploy an FSx for Lustre file system in the same Availabil... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_storage.adoc | mainline | aws-eks-best-practices | [
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\* \*\*API request charges\*\*: When performing a high number of file operations with the Mountpoint for S3, API request charges can become a portion of storage costs. To mitigate this, if strong consistency is not required, always enable metadata caching and set the `metadata-ttl` period to reduce the number of API op... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_storage.adoc | mainline | aws-eks-best-practices | [
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\* \*\*Storage Class:\*\* FSx for OpenZFS supports both the https://docs.aws.amazon.com/fsx/latest/OpenZFSGuide/performance-ssd.html[SSD storage] class as well as the https://docs.aws.amazon.com/fsx/latest/OpenZFSGuide/performance-intelligent-tiering.html[Intelligent-Tiering] storage class. For AI/ML workloads it is re... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_storage.adoc | mainline | aws-eks-best-practices | [
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Claim (PVC) that is backed by an EFS file system is created as an https://docs.aws.amazon.com/efs/latest/ug/efs-access-points.html[EFS Access-point to the EFS file system]. EFS allows multiple nodes and pods to access the same model files, eliminating the need to sync data to each node's filesystem. https://docs.aws.am... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_storage.adoc | mainline | aws-eks-best-practices | [
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using the Mountpoint S3 CSI Driver with S3 Express One Zone is caching. The first instance reads data from S3 Standard (general purpose), caching it in lower-latency Express One Zone. Subsequent reads by other clients access the cached data faster, which is ideal for multi-node scenarios where multiple EKS nodes read t... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_storage.adoc | mainline | aws-eks-best-practices | [
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//!!NODE\_ROOT [."topic"] [[aiml-compute,aiml-compute.title]] = AI/ML on EKS - Compute :info\_doctype: section :imagesdir: images/ :info\_title: Compute and Autoscaling :info\_abstract: Compute and Autoscaling :info\_titleabbrev: Compute :authors: ["Leah Tucker"] :date: 2025-05-30 TIP: https://aws-experience.com/emea/s... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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available. It’s important to understand the unique characteristics of your AI/ML workloads before diversifying arbitrarily. Review the available instance types using the https://aws.amazon.com/ec2/instance-explorer/[EC2 Instance Type Explorer] tool to generate a list of instance types that match your specific compute r... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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from checkpoints if interrupted, making them ideal for Spot's variable availability. \* \*\*Fine-tuning models\*\*: Use Spot instances for fine-tuning with robust checkpointing mechanisms to resume from the last saved state, minimizing the impact of instance interruptions. \* \*\*Batch inference\*\*: Use Spot instances... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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unused EC2 instance capacity for a specific instance type in a specific Availability Zone (AZ). When Karpenter tries to provision a new node, it selects an instance type that matches the NodePool's requirements. If no compatible instance type has Spot capacity in any AZ, then provisioning fails. To avoid this issue, al... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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longer than two minutes, the old node will be interrupted prior to those pods being rescheduled, which could impact workload availability. Consider the two-minute Spot interruption notice when designing your applications, implement checkpointing in long-running applications (e.g., saving progress to persistent storage ... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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work completed within a specific time frame to understand impact of diversification. \*\*Scenario: Diversification for real time inference workloads\*\* For a real-time online inference workload on Spot Instances, you can configure a Karpenter NodePool to diversify across compatible GPU instance families and generation... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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overhead. \* \*\*For Spot instances or hardware failures\*\*: In environments prone to interruptions, such as EC2 Spot (2-minute notice) or hardware failures (e.g., GPU memory errors), checkpointing enables quick resumption, making Spot viable for cost savings in fault-tolerant workloads. \* \*\*Distributed training at... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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and Trainium instances, ODCRs can be used for a wider range of instance types, including G instances, making them suitable for workloads that require different GPU capabilities, such as inference or graphics. When using Amazon EC2 Spot Instances, being able to diverse across different instance types, sizes, and availab... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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`karpenter.k8s.aws/instance-gpu-name: "a100"`) \* \*\*Time-Slicing\*\*: Schedules workloads to share a GPU's compute resources over time, allowing concurrent execution without physical partitioning. This is ideal for workloads with variable compute demands, but may lack memory isolation. \* \*\*Multi-Instance GPU (MIG)... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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and Trainium-based instances. It is tailored for AI/ML workloads, using labels (e.g., node-health-status) to manage node health. When a node is deemed unhealthy, HyperPod triggers automatic replacement of the faulty hardware, such as GPUs. It detects networking-related failures for EFA through its basic health checks b... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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memory. We recommend using Kubernetes `PriorityClass` to assign priorities and `PodDisruptionBudget` to control eviction behavior. [,yaml] ---- apiVersion: scheduling.k8s.io/v1 kind: PriorityClass metadata: name: low-priority value: 100 --- spec: priorityClassName: low-priority ---- See the https://kubernetes.io/docs/c... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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DRA transforms this model by introducing structured, declarative allocation that provides the Kubernetes scheduler with complete visibility into hardware characteristics and workload requirements. This enables intelligent placement decisions and efficient resource sharing. === Advantages of using DRA instead of NVIDIA ... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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about Kubernetes DRA and NVIDIA DRA drivers, see the following resources on GitHub: \* Kubernetes https://github.com/kubernetes/dynamic-resource-allocation[dynamic-resource-allocation] \* https://github.com/kubernetes/enhancements/tree/master/keps/sig-node/3063-dynamic-resource-allocation[Kubernetes enhancement proposa... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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A10G GPU and creates two `resourceslices` on that node. \* The `gpu.nvidia.com` slice registers the physical A10G GPU device with its specifications (memory, compute capability, and more). \* Since A10G doesn't support MIG partitioning, the `compute-domain.nvidia.com` slice creates a single compute domain representing ... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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usage. For an example, see <>. \* \*Multi-Process service (MPS)\* enables concurrent execution of multiple CUDA processes on a single GPU with better isolation than time-slicing. For an example, see <>. \* \*Multi-Instance GPU (MIG)\* provides hardware-level partitioning, creating isolated GPU instances with dedicated ... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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GPU and reserved it for this Pod \* \*Pod status\*: Starts running immediately Second Pod (`training-pod-2`) \* \*State\*: `pending` \* \*Meaning\*: Waiting for DRA to configure time-slicing on the same GPU \* \*Pod status\*: Waiting to be scheduled \* The state will go from `pending` to `allocated,reserved` to `runnin... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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driver allocates one `ResourceClaim` to the Pod and automatically configures MPS to allow both the inference and training containers to execute concurrently. Each container gets its own isolated GPU memory space and compute resources, with the MPS daemon coordinating access to the underlying hardware. You can verify th... | https://github.com/aws/aws-eks-best-practices/blob/mainline/latest/bpg/aiml/aiml_compute.adoc | mainline | aws-eks-best-practices | [
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... | 0.078114 |
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