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Add HPA support to ChatQnA #327

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67 changes: 67 additions & 0 deletions helm-charts/README.md
Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,10 @@ 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)
Expand Down Expand Up @@ -62,8 +66,71 @@ 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! |
| 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.
Expand Down
11 changes: 6 additions & 5 deletions helm-charts/chatqna/README.md
Original file line number Diff line number Diff line change
Expand Up @@ -83,8 +83,9 @@ Access `http://localhost:5174` to play with the ChatQnA workload through UI.

## Values

| Key | Type | Default | Description |
| ---------------- | ------ | ----------------------------- | ------------------------------------------------------------------------ |
| image.repository | string | `"opea/chatqna"` | |
| service.port | string | `"8888"` | |
| tgi.LLM_MODEL_ID | string | `"Intel/neural-chat-7b-v3-3"` | Models id from https://huggingface.co/, or predownloaded model directory |
| Key | Type | Default | Description |
| -------------------------------------- | ------ | ----------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| image.repository | string | `"opea/chatqna"` | |
| service.port | string | `"8888"` | |
| tgi.LLM_MODEL_ID | string | `"Intel/neural-chat-7b-v3-3"` | Models id from https://huggingface.co/, or predownloaded model directory |
| global.horizontalPodAutoscaler.enabled | bop; | false | HPA autoscaling for the TGI and TEI service deployments based on metrics they provide. See HPA section in ../README.md before enabling! |
53 changes: 53 additions & 0 deletions helm-charts/chatqna/templates/customMetrics.yaml
Original file line number Diff line number Diff line change
@@ -0,0 +1,53 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

{{- if .Values.global.horizontalPodAutoscaler.enabled }}
apiVersion: v1
data:
config.yaml: |
rules:
- seriesQuery: '{__name__="tgi_request_inference_duration_sum",service="{{ include "tgi.fullname" .Subcharts.tgi }}"}'
# Average request latency from TGI histograms, over 1 min
# (0.001 divider add is to make sure there's always a valid value)
metricsQuery: 'rate(tgi_request_inference_duration_sum{service="{{ include "tgi.fullname" .Subcharts.tgi }}",<<.LabelMatchers>>}[1m]) / (0.001+rate(tgi_request_inference_duration_count{service="{{ include "tgi.fullname" .Subcharts.tgi }}",<<.LabelMatchers>>}[1m]))'
name:
matches: ^tgi_request_inference_duration_sum
as: "tgi_request_latency"
resources:
# HPA needs both namespace + suitable object resource for its query paths:
# /apis/custom.metrics.k8s.io/v1beta1/namespaces/default/service/*/tgi_request_latency
# (pod is not suitable object type for matching as each instance has different name)
overrides:
namespace:
resource: namespace
service:
resource: service
- seriesQuery: '{__name__="te_request_inference_duration_sum",service="{{ include "teirerank.fullname" .Subcharts.teirerank }}"}'
# Average request latency from TEI histograms, over 1 min
metricsQuery: 'rate(te_request_inference_duration_sum{service="{{ include "teirerank.fullname" .Subcharts.teirerank }}",<<.LabelMatchers>>}[1m]) / (0.001+rate(te_request_inference_duration_count{service="{{ include "teirerank.fullname" .Subcharts.teirerank }}",<<.LabelMatchers>>}[1m]))'
name:
matches: ^te_request_inference_duration_sum
as: "reranking_request_latency"
resources:
overrides:
namespace:
resource: namespace
service:
resource: service
- seriesQuery: '{__name__="te_request_inference_duration_sum",service="{{ include "tei.fullname" .Subcharts.tei }}"}'
# Average request latency from TEI histograms, over 1 min
metricsQuery: 'rate(te_request_inference_duration_sum{service="{{ include "tei.fullname" .Subcharts.tei }}",<<.LabelMatchers>>}[1m]) / (0.001+rate(te_request_inference_duration_count{service="{{ include "tei.fullname" .Subcharts.tei }}",<<.LabelMatchers>>}[1m]))'
name:
matches: ^te_request_inference_duration_sum
as: "embedding_request_latency"
resources:
overrides:
namespace:
resource: namespace
service:
resource: service
kind: ConfigMap
metadata:
name: adapter-config
namespace: monitoring
{{- end }}
8 changes: 8 additions & 0 deletions helm-charts/chatqna/values.yaml
Original file line number Diff line number Diff line change
Expand Up @@ -48,3 +48,11 @@ global:
modelUseHostPath: ""
# modelUseHostPath: /mnt/opea-models
# modelUsePVC: model-volume

# Enabling HorizontalPodAutoscaler (HPA) will:
# - Overwrite existing PrometheusAdapter "adapter-config" configMap with ChatQnA specific custom metric queries
# for embedding, reranking, tgi services
# Upstream default configMap:
# - https://github.com/kubernetes-sigs/prometheus-adapter/blob/master/deploy/manifests/config-map.yaml
horizontalPodAutoscaler:
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This will need "HorizontalPodAutoscaler (HPA) support" section in chatqna README too. It can be same as in common components, with first clause updated e.g. to:

`horizontalPodAutoscaler` option enables HPA scaling for the TGI and TEI inferencing deployments:

Verification section can be omitted I think, it's enough to have it in TGI & TEI READMEs.

Options table entry description could be e.g:

HPA autoscaling for the TGI and TEI service deployments based on metrics they provide. See #pre-conditions and #gotchas before enabling!  (If one doesn't want one of them to be scaled, given service `maxReplicas` can be set to `1`)

enabled: false
13 changes: 7 additions & 6 deletions helm-charts/common/tei/README.md
Original file line number Diff line number Diff line change
Expand Up @@ -35,9 +35,10 @@ curl http://localhost:2081/embed -X POST -d '{"inputs":"What is Deep Learning?"}

## Values

| Key | Type | Default | Description |
| ----------------------- | ------ | ------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| EMBEDDING_MODEL_ID | string | `"BAAI/bge-base-en-v1.5"` | Models id from https://huggingface.co/, or predownloaded model directory |
| global.modelUseHostPath | string | `"/mnt/opea-models"` | Cached models directory, tei will not download if the model is cached here. The host path "modelUseHostPath" will be mounted to container as /data directory. Set this to null/empty will force it to download model. |
| image.repository | string | `"ghcr.io/huggingface/text-embeddings-inference"` | |
| image.tag | string | `"cpu-1.5"` | |
| Key | Type | Default | Description |
| ------------------------------- | ------ | ------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| EMBEDDING_MODEL_ID | string | `"BAAI/bge-base-en-v1.5"` | Models id from https://huggingface.co/, or predownloaded model directory |
| global.modelUseHostPath | string | `"/mnt/opea-models"` | Cached models directory, tei will not download if the model is cached here. The host path "modelUseHostPath" will be mounted to container as /data directory. Set this to null/empty will force it to download model. |
| image.repository | string | `"ghcr.io/huggingface/text-embeddings-inference"` | |
| image.tag | string | `"cpu-1.5"` | |
| horizontalPodAutoscaler.enabled | bool | false | Enable HPA autoscaling for the service deployment based on metrics it provides. See HPA section in ../../README.md before enabling! |
7 changes: 7 additions & 0 deletions helm-charts/common/tei/templates/deployment.yaml
Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,10 @@ metadata:
labels:
{{- include "tei.labels" . | nindent 4 }}
spec:
# use explicit replica counts only of HorizontalPodAutoscaler is disabled
{{- if not .Values.global.horizontalPodAutoscaler.enabled }}
replicas: {{ .Values.replicaCount }}
{{- end }}
selector:
matchLabels:
{{- include "tei.selectorLabels" . | nindent 6 }}
Expand Down Expand Up @@ -102,3 +105,7 @@ spec:
tolerations:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- if .Values.global.horizontalPodAutoscaler.enabled }}
# extra time to finish processing buffered requests before HPA forcibly terminates pod
terminationGracePeriodSeconds: 60
{{- end }}
51 changes: 51 additions & 0 deletions helm-charts/common/tei/templates/horizontalPodAutoscaler.yaml
Original file line number Diff line number Diff line change
@@ -0,0 +1,51 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

{{- if .Values.global.horizontalPodAutoscaler.enabled }}
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: {{ include "tei.fullname" . }}
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: {{ include "tei.fullname" . }}
minReplicas: 1
maxReplicas: {{ .Values.horizontalPodAutoscaler.maxReplicas }}
metrics:
- type: Object
object:
metric:
# tei-embedding time metrics are in seconds
name: embedding_request_latency
describedObject:
apiVersion: v1
# get metric for named object of given type (in same namespace)
kind: Service
name: {{ include "tei.fullname" . }}
target:
# embedding_request_latency is average for all TEI pods. To avoid replica fluctuations when
# TEI startup + request processing takes longer than HPA evaluation period, this uses
# "Value" (replicas = metric.value / target.value), instead of "averageValue" type:
# https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#algorithm-details
type: Value
value: 4
behavior:
scaleDown:
stabilizationWindowSeconds: 180
policies:
- type: Percent
value: 25
periodSeconds: 15
scaleUp:
selectPolicy: Max
stabilizationWindowSeconds: 0
policies:
- type: Percent
value: 50
periodSeconds: 15
- type: Pods
value: 2
periodSeconds: 15
{{- end }}
17 changes: 17 additions & 0 deletions helm-charts/common/tei/templates/servicemonitor.yaml
Original file line number Diff line number Diff line change
@@ -0,0 +1,17 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

{{- if .Values.global.horizontalPodAutoscaler.enabled }}
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: {{ include "tei.fullname" . }}
spec:
selector:
matchLabels:
{{- include "tei.selectorLabels" . | nindent 6 }}
endpoints:
- interval: 4s
port: tei
scheme: http
{{- end }}
9 changes: 9 additions & 0 deletions helm-charts/common/tei/values.yaml
Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,9 @@

replicaCount: 1

horizontalPodAutoscaler:
maxReplicas: 2

port: 2081
shmSize: 1Gi
EMBEDDING_MODEL_ID: "BAAI/bge-base-en-v1.5"
Expand Down Expand Up @@ -92,3 +95,9 @@ global:
# By default, both var are set to empty, the model will be downloaded and saved to a tmp volume.
modelUseHostPath: ""
modelUsePVC: ""
# Enabling HPA will:
# - Ignore above replica count, as it will be controlled by HPA
# - Add example HPA scaling rules with thresholds suitable for Xeon deployments
# - Require custom metrics ConfigMap available in the main application chart
horizontalPodAutoscaler:
enabled: false
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