HPA improvements

helm-charts/HPA.md

@@ -0,0 +1,196 @@
+# HorizontalPodAutoscaler (HPA) support
+
+## Table of Contents
+
+- [Introduction](#introduction)
+- [Pre-conditions](#pre-conditions)
+  - [Resource requests](#resource-requests)
+  - [Prometheus](#prometheus)
+- [Gotchas](#gotchas)
+- [Enable HPA](#enable-hpa)
+  - [Install](#install)
+  - [Post-install](#post-install)
+- [Verify](#verify)
+
+## Introduction
+
+`horizontalPodAutoscaler` option enables HPA scaling for the TGI and TEI inferencing deployments:
+https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/
+
+Autoscaling is based on custom application metrics provided through [Prometheus](https://prometheus.io/).
+
+## Pre-conditions
+
+Read [post-install](#post-install) steps before installation!
+
+### Resource requests
+
+HPA controlled CPU pods SHOULD have appropriate resource requests or affinity rules (enabled in their
+subcharts and tested to work) so that k8s scheduler does not schedule too many of them on the same
+node(s). Otherwise they never reach ready state.
+
+If you use different models than the default ones, update TGI and TEI resource requests to match
+model requirements.
+
+Too large requests would not be a problem as long as pods still fit to available nodes. However,
+unless rules have been added to pods preventing them from being scheduled on same nodes, too
+small requests would be an issue:
+
+- Multiple inferencing instances interfere / slow down each other, especially if there are no
+  [NRI policies](https://github.com/opea-project/GenAIEval/tree/main/doc/platform-optimization)
+  that provide further isolation
+- Containers can become non-functional when their actual resource usage crosses the specified limits
+
+### Prometheus
+
+If cluster does not run [Prometheus operator](https://github.com/prometheus-operator/kube-prometheus)
+yet, it SHOULD be be installed before enabling HPA, e.g. by using a Helm chart for it:
+https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack
+
+Prometheus-adapter is also needed, to provide k8s custom metrics based on collected TGI / TEI metrics:
+https://github.com/prometheus-community/helm-charts/tree/main/charts/prometheus-adapter
+
+To install (older versions) of them:
+
+```console
+$ helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
+$ helm repo update
+$ prom_ns=monitoring  # namespace for Prometheus/-adapter
+$ kubectl create ns $prom_ns
+$ helm install prometheus-stack prometheus-community/kube-prometheus-stack --version 55.5.2 -n $prom_ns
+$ kubectl get services -n $prom_ns
+$ helm install  prometheus-adapter prometheus-community/prometheus-adapter --version 4.10.0 -n $prom_ns \
+  --set prometheus.url=http://prometheus-stack-kube-prom-prometheus.$prom_ns.svc \
+  --set prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues=false
+```
+
+NOTE: the service name given above in `prometheus.url` must match the listed Prometheus service name,
+otherwise adapter cannot access it!
+
+(Alternative for setting the above `prometheusSpec` variable to `false` is making sure that
+`prometheusRelease` value in top-level chart matches the release name given to the Prometheus
+install i.e. when it differs from `prometheus-stack` used above. That is used to annotate
+created serviceMonitors with a label Prometheus requires when above option is `true`.)
+
+## Gotchas
+
+Why HPA is opt-in:
+
+- Installing custom metrics for HPA requires manual post-install steps, as
+  Prometheus-operator and -adapter are missing support needed to automate that
+- Top level chart name needs to conform to Prometheus metric naming conventions,
+  as it is also used as a metric name prefix (with dashes converted to underscores)
+- By default Prometheus adds [k8s RBAC rules](https://github.com/prometheus-operator/kube-prometheus/blob/main/manifests/prometheus-roleBindingSpecificNamespaces.yaml)
+  for accessing metrics from `default`, `kube-system` and `monitoring` namespaces. If Helm is
+  asked to install OPEA services to some other namespace, those rules need to be updated accordingly
+- Unless pod resource requests, affinity rules, scheduling topology constraints and/or cluster NRI
+  policies are used to better isolate service inferencing pods from each other, instances
+  scaled up on same node may never get to ready state
+- Current HPA rules are just examples, for efficient scaling they need to be fine-tuned for given setup
+  performance (underlying HW, used models and data types, OPEA version etc)
+- Debugging missing custom metric issues is hard as logs rarely include anything helpful
+
+## Enable HPA
+
+### Install
+
+ChatQnA includes pre-configured values files for scaling the services.
+
+To enable HPA, add `-f chatqna/hpa-values.yaml` option to your `helm install` command line.
+
+If **CPU** versions of TGI (and TEI) services are being scaled, resource requests and probe timings
+suitable for CPU usage need to be used. Add `-f chatqna/cpu-values.yaml` option to your `helm install`
+line. If you need to change model specified there, update the resource requests accordingly.
+
+### Post-install
+
+Above step created custom metrics config for Prometheus-adapter suitable for HPA use.
+
+Take backup of existing custom metrics config before replacing it:
+
+```console
+$ prom_ns=monitoring  # Prometheus/-adapter namespace
+$ name=$(kubectl -n $prom_ns get cm --selector app.kubernetes.io/name=prometheus-adapter -o name | cut -d/ -f2)
+$ kubectl -n $prom_ns get cm/$name -o yaml > adapter-config.yaml.bak
+```
+
+Save generated config with values matching current adapter config:
+
+```console
+$ chart=chatqna  # OPEA chart release name
+$ kubectl get cm/$chart-custom-metrics -o yaml | sed \
+  -e "s/name:.*custom-metrics$/name: $name/" \
+  -e "s/namespace: default$/namespace: $prom_ns/" \
+  > adapter-config.yaml
+```
+
+NOTE: if there are existing custom metric rules you need to retain, add them from saved
+`adapter-config.yaml.bak` to `adapter-config.yaml` file now!
+
+Overwrite current Prometheus-adapter configMap with generated one:
+
+```console
+$ kubectl delete -n $prom_ns cm/$name
+$ kubectl apply -f adapter-config.yaml
+```
+
+And restart it, so that it will use the new config:
+
+```console
+$ selector=app.kubernetes.io/name=prometheus-adapter
+$ kubectl -n $prom_ns delete $(kubectl -n $prom_ns get pod --selector $selector -o name)
+```
+
+## Verify
+
+To verify that horizontalPodAutoscaler options work, it's better to check that both metrics
+from the inferencing services, and HPA rules using custom metrics generated from them, do work.
+
+(Names of the object names depend on whether Prometheus was installed from manifests, or Helm,
+and the release name given for its Helm install.)
+
+Check installed Prometheus service names:
+
+```console
+$ prom_ns=monitoring  # Prometheus/-adapter namespace
+$ kubectl -n $prom_ns get svc
+```
+
+Use service name matching your Prometheus installation:
+
+```console
+$ prom_svc=prometheus-stack-kube-prom-prometheus  # Metrics service
+```
+
+Verify Prometheus found metric endpoints for chart services, i.e. last number on `curl` output is non-zero:
+
+```console
+$ chart=chatqna # OPEA chart release name
+$ prom_url=http://$(kubectl -n $prom_ns get -o jsonpath="{.spec.clusterIP}:{.spec.ports[0].port}" svc/$prom_svc)
+$ curl --no-progress-meter $prom_url/metrics | grep scrape_pool_targets.*$chart
+```
+
+**NOTE**: TGI and TEI inferencing services provide metrics endpoint only after they've processed
+their first request, and reranking service will be used only after context data has been uploaded!
+
+Check that both Prometheus metrics required from TGI are available:
+
+```console
+$ for m in sum count; do
+  curl --no-progress-meter $prom_url/api/v1/query? \
+  --data-urlencode query=tgi_request_inference_duration_$m'{service="'$chart'-tgi"}' | jq;
+done | grep __name__
+```
+
+PrometheusAdapter lists corresponding TGI and/or TEI custom metrics (prefixed with chart name):
+
+```console
+$ kubectl get --raw /apis/custom.metrics.k8s.io/v1beta1 | jq .resources[].name
+```
+
+And HPA rules have TARGET values for HPA controlled service deployments (instead of `<unknown>`):
+
+```console
+$ ns=default  # OPEA namespace
+$ kubectl -n $ns get hpa
+```

helm-charts/README.md

@@ -9,10 +9,6 @@ This directory contains helm charts for [GenAIComps](https://github.com/opea-pro
   - [Components](#components)
 - [How to deploy with helm charts](#deploy-with-helm-charts)
 - [Helm Charts Options](#helm-charts-options)
-- [HorizontalPodAutoscaler (HPA) support](#horizontalpodautoscaler-hpa-support)
-  - [Pre-conditions](#pre-conditions)
-  - [Gotchas](#gotchas)
-  - [Verify HPA metrics](#verify-hpa-metrics)
 - [Using Persistent Volume](#using-persistent-volume)
 - [Using Private Docker Hub](#using-private-docker-hub)
 - [Helm Charts repository](#helm-chart-repository)
@@ -66,71 +62,9 @@ There are global options(which should be shared across all components of a workl
 | global     | http_proxy https_proxy no_proxy | Proxy settings. If you are running the workloads behind the proxy, you'll have to add your proxy settings here.                                                                                                                                                                |
 | global     | modelUsePVC                     | The PersistentVolumeClaim you want to use as huggingface hub cache. Default "" means not using PVC. Only one of modelUsePVC/modelUseHostPath can be set.                                                                                                                       |
 | global     | modelUseHostPath                | If you don't have Persistent Volume in your k8s cluster and want to use local directory as huggingface hub cache, set modelUseHostPath to your local directory name. Note that this can't share across nodes. Default "". Only one of modelUsePVC/modelUseHostPath can be set. |
-| global     | horizontalPodAutoscaler.enabled | Enable HPA autoscaling for TGI and TEI service deployments based on metrics they provide. See #pre-conditions and #gotchas before enabling!                                                                                                                                    |
+| chatqna    | horizontalPodAutoscaler.enabled | Enable HPA autoscaling for TGI and TEI service deployments based on metrics they provide. See [Pre-conditions](HPA.md#pre-conditions) and [Gotchas](HPA.md#gotchas) before enabling!                                                                                           |
 | tgi        | LLM_MODEL_ID                    | The model id you want to use for tgi server. Default "Intel/neural-chat-7b-v3-3".                                                                                                                                                                                              |
 
-## HorizontalPodAutoscaler (HPA) support
-
-`horizontalPodAutoscaler` option enables HPA scaling for the TGI and TEI inferencing deployments:
-https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/
-
-Autoscaling is based on custom application metrics provided through [Prometheus](https://prometheus.io/).
-
-### Pre-conditions
-
-If cluster does not run [Prometheus operator](https://github.com/prometheus-operator/kube-prometheus)
-yet, it SHOULD be be installed before enabling HPA, e.g. by using:
-https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack
-
-Enabling HPA in top-level Helm chart (e.g. `chatqna`), overwrites cluster's current _PrometheusAdapter_
-configuration with relevant custom metric queries. If that has queries you wish to retain, _or_ HPA is
-otherwise enabled only in TGI or TEI subchart(s), you need add relevat queries to _PrometheusAdapter_
-configuration _manually_ (e.g. from `chatqna` custom metrics Helm template).
-
-### Gotchas
-
-Why HPA is opt-in:
-
-- Enabling (top level) chart `horizontalPodAutoscaler` option will _overwrite_ cluster's current
-  `PrometheusAdapter` configuration with its own custom metrics configuration.
-  Take copy of the existing one before install, if that matters:
-  `kubectl -n monitoring get cm/adapter-config -o yaml > adapter-config.yaml`
-- `PrometheusAdapter` needs to be restarted after install, for it to read the new configuration:
-  `ns=monitoring; kubectl -n $ns delete $(kubectl -n $ns get pod --selector app.kubernetes.io/name=prometheus-adapter -o name)`
-- By default Prometheus adds [k8s RBAC rules](https://github.com/prometheus-operator/kube-prometheus/blob/main/manifests/prometheus-roleBindingSpecificNamespaces.yaml)
-  for accessing metrics from `default`, `kube-system` and `monitoring` namespaces. If Helm is
-  asked to install OPEA services to some other namespace, those rules need to be updated accordingly
-- Current HPA rules are examples for Xeon, for efficient scaling they need to be fine-tuned for given setup
-  performance (underlying HW, used models and data types, OPEA version etc)
-
-### Verify HPA metrics
-
-To verify that metrics required by horizontalPodAutoscaler option work, check following...
-
-Prometheus has found the metric endpoints, i.e. last number on `curl` output is non-zero:
-
-```console
-chart=chatqna; # OPEA services prefix
-ns=monitoring; # Prometheus namespace
-prom_url=http://$(kubectl -n $ns get -o jsonpath="{.spec.clusterIP}:{.spec.ports[0].port}" svc/prometheus-k8s);
-curl --no-progress-meter $prom_url/metrics | grep scrape_pool_targets.*$chart
-```
-
-**NOTE**: TGI and TEI inferencing services provide metrics endpoint only after they've processed their first request!
-
-PrometheusAdapter lists TGI and/or TGI custom metrics (`te_*` / `tgi_*`):
-
-```console
-kubectl get --raw /apis/custom.metrics.k8s.io/v1beta1 | jq .resources[].name
-```
-
-HPA rules list valid (not `<unknown>`) TARGET values for service deployments:
-
-```console
-ns=default;  # OPEA namespace
-kubectl -n $ns get hpa
-```
-
 ## Using Persistent Volume
 
 It's common to use Persistent Volume(PV) for model caches(huggingface hub cache) in a production k8s cluster. We support to pass the PersistentVolumeClaim(PVC) to containers, but it's the user's responsibility to create the PVC depending on your k8s cluster's capability.  

helm-charts/chatqna/cpu-values.yaml

@@ -0,0 +1,109 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# Override CPU resource request and probe timing values in specific subcharts
+#
+# RESOURCES
+#
+# Resource request matching actual resource usage (with enough slack)
+# is important when service is scaled up, so that right amount of pods
+# get scheduled to right nodes.
+#
+# Because resource usage depends on the used devices, model, data type
+# and SW versions, and this top-level chart has overrides for them,
+# resource requests need to be specified here too.
+#
+# To test service without resource request, use "resources: {}".
+#
+# PROBES
+#
+# Inferencing pods startup / warmup takes *much* longer on CPUs than
+# with acceleration devices, and their responses are also slower,
+# especially when node is running several instances of these services.
+#
+# Kubernetes restarting pod before its startup finishes, or not
+# sending it queries because it's not in ready state due to slow
+# readiness responses, does really NOT help in getting faster responses.
+#
+# => probe timings need to be increased when running on CPU.
+
+tgi:
+  # TODO: add Helm value also for TGI data type option:
+  # https://github.com/opea-project/GenAIExamples/issues/330
+  LLM_MODEL_ID: Intel/neural-chat-7b-v3-3
+
+  # Potentially suitable values for scaling CPU TGI 2.2 with Intel/neural-chat-7b-v3-3 @ 32-bit:
+  resources:
+    limits:
+      cpu: 8
+      memory: 70Gi
+    requests:
+      cpu: 6
+      memory: 65Gi
+
+  livenessProbe:
+    initialDelaySeconds: 8
+    periodSeconds: 8
+    failureThreshold: 24
+    timeoutSeconds: 4
+  readinessProbe:
+    initialDelaySeconds: 16
+    periodSeconds: 8
+    timeoutSeconds: 4
+  startupProbe:
+    initialDelaySeconds: 10
+    periodSeconds: 5
+    failureThreshold: 180
+    timeoutSeconds: 2
+
+teirerank:
+  RERANK_MODEL_ID: "BAAI/bge-reranker-base"
+
+  # Potentially suitable values for scaling CPU TEI v1.5 with BAAI/bge-reranker-base model:
+  resources:
+    limits:
+      cpu: 4
+      memory: 30Gi
+    requests:
+      cpu: 2
+      memory: 25Gi
+
+  livenessProbe:
+    initialDelaySeconds: 8
+    periodSeconds: 8
+    failureThreshold: 24
+    timeoutSeconds: 4
+  readinessProbe:
+    initialDelaySeconds: 8
+    periodSeconds: 8
+    timeoutSeconds: 4
+  startupProbe:
+    initialDelaySeconds: 5
+    periodSeconds: 5
+    failureThreshold: 120
+
+tei:
+  EMBEDDING_MODEL_ID: "BAAI/bge-base-en-v1.5"
+
+  # Potentially suitable values for scaling CPU TEI 1.5 with BAAI/bge-base-en-v1.5 model:
+  resources:
+    limits:
+      cpu: 4
+      memory: 4Gi
+    requests:
+      cpu: 2
+      memory: 3Gi
+
+  livenessProbe:
+    initialDelaySeconds: 5
+    periodSeconds: 5
+    failureThreshold: 24
+    timeoutSeconds: 2
+  readinessProbe:
+    initialDelaySeconds: 5
+    periodSeconds: 5
+    timeoutSeconds: 2
+  startupProbe:
+    initialDelaySeconds: 5
+    periodSeconds: 5
+    failureThreshold: 120