mirror of
https://github.com/elastic/kibana.git
synced 2025-04-23 09:19:04 -04:00
# Backport This will backport the following commits from `main` to `8.x`: - [[ML] Update vCPUs ranges for start model deployment (#195617)](https://github.com/elastic/kibana/pull/195617) <!--- Backport version: 9.4.3 --> ### Questions ? Please refer to the [Backport tool documentation](https://github.com/sqren/backport) <!--BACKPORT [{"author":{"name":"Dima Arnautov","email":"dmitrii.arnautov@elastic.co"},"sourceCommit":{"committedDate":"2024-10-14T14:38:26Z","message":"[ML] Update vCPUs ranges for start model deployment (#195617)\n\n## Summary\r\n\r\n#### Different vCPUs ranges and enabling support for static allocations\r\nbased on the serverless project type\r\n\r\n- Each serverless config yml, e.g.\r\n[search.es.yml](84b3b79a15/config/serverless.es.yml (L61))\r\nnow contains parameters required for start model deployment:\r\n\r\n```yml\r\nxpack.ml.nlp:\r\n enabled: true\r\n modelDeployment:\r\n allowStaticAllocations: true\r\n vCPURange:\r\n low:\r\n min: 0\r\n max: 2\r\n static: 2\r\n medium:\r\n min: 1\r\n max: 32\r\n static: 32\r\n high:\r\n min: 1\r\n max: 512\r\n static: 512\r\n```\r\n\r\nNote: _There will be no static allocations option for serverless O11y\r\nand serverless Security._\r\n\r\n#### The minimum values of vCPUs\r\n\r\n- 0 for the Low usage level on both serverless and ESS.\r\n- 1 for the Medium and High usage levels on both serverless and ESS.\r\n \r\n#### The default vCPUs usage levels\r\n- Low in serverless.\r\n- Medium in ESS and on-prem\r\n\r\n### Checklist\r\n\r\n- [x] [Unit or functional\r\ntests](https://www.elastic.co/guide/en/kibana/master/development-tests.html)\r\nwere updated or added to match the most common scenarios","sha":"13897083dc8c465a16ddb3856d4e4904b9629610","branchLabelMapping":{"^v9.0.0$":"main","^v8.16.0$":"8.x","^v(\\d+).(\\d+).\\d+$":"$1.$2"}},"sourcePullRequest":{"labels":["release_note:enhancement",":ml","v9.0.0","Feature:3rd Party Models","Team:ML","ci:project-deploy-elasticsearch","ci:project-deploy-observability","v8.16.0","backport:version"],"title":"[ML] Update vCPUs ranges for start model deployment ","number":195617,"url":"https://github.com/elastic/kibana/pull/195617","mergeCommit":{"message":"[ML] Update vCPUs ranges for start model deployment (#195617)\n\n## Summary\r\n\r\n#### Different vCPUs ranges and enabling support for static allocations\r\nbased on the serverless project type\r\n\r\n- Each serverless config yml, e.g.\r\n[search.es.yml](84b3b79a15/config/serverless.es.yml (L61))\r\nnow contains parameters required for start model deployment:\r\n\r\n```yml\r\nxpack.ml.nlp:\r\n enabled: true\r\n modelDeployment:\r\n allowStaticAllocations: true\r\n vCPURange:\r\n low:\r\n min: 0\r\n max: 2\r\n static: 2\r\n medium:\r\n min: 1\r\n max: 32\r\n static: 32\r\n high:\r\n min: 1\r\n max: 512\r\n static: 512\r\n```\r\n\r\nNote: _There will be no static allocations option for serverless O11y\r\nand serverless Security._\r\n\r\n#### The minimum values of vCPUs\r\n\r\n- 0 for the Low usage level on both serverless and ESS.\r\n- 1 for the Medium and High usage levels on both serverless and ESS.\r\n \r\n#### The default vCPUs usage levels\r\n- Low in serverless.\r\n- Medium in ESS and on-prem\r\n\r\n### Checklist\r\n\r\n- [x] [Unit or functional\r\ntests](https://www.elastic.co/guide/en/kibana/master/development-tests.html)\r\nwere updated or added to match the most common scenarios","sha":"13897083dc8c465a16ddb3856d4e4904b9629610"}},"sourceBranch":"main","suggestedTargetBranches":["8.x"],"targetPullRequestStates":[{"branch":"main","label":"v9.0.0","branchLabelMappingKey":"^v9.0.0$","isSourceBranch":true,"state":"MERGED","url":"https://github.com/elastic/kibana/pull/195617","number":195617,"mergeCommit":{"message":"[ML] Update vCPUs ranges for start model deployment (#195617)\n\n## Summary\r\n\r\n#### Different vCPUs ranges and enabling support for static allocations\r\nbased on the serverless project type\r\n\r\n- Each serverless config yml, e.g.\r\n[search.es.yml](84b3b79a15/config/serverless.es.yml (L61))\r\nnow contains parameters required for start model deployment:\r\n\r\n```yml\r\nxpack.ml.nlp:\r\n enabled: true\r\n modelDeployment:\r\n allowStaticAllocations: true\r\n vCPURange:\r\n low:\r\n min: 0\r\n max: 2\r\n static: 2\r\n medium:\r\n min: 1\r\n max: 32\r\n static: 32\r\n high:\r\n min: 1\r\n max: 512\r\n static: 512\r\n```\r\n\r\nNote: _There will be no static allocations option for serverless O11y\r\nand serverless Security._\r\n\r\n#### The minimum values of vCPUs\r\n\r\n- 0 for the Low usage level on both serverless and ESS.\r\n- 1 for the Medium and High usage levels on both serverless and ESS.\r\n \r\n#### The default vCPUs usage levels\r\n- Low in serverless.\r\n- Medium in ESS and on-prem\r\n\r\n### Checklist\r\n\r\n- [x] [Unit or functional\r\ntests](https://www.elastic.co/guide/en/kibana/master/development-tests.html)\r\nwere updated or added to match the most common scenarios","sha":"13897083dc8c465a16ddb3856d4e4904b9629610"}},{"branch":"8.x","label":"v8.16.0","branchLabelMappingKey":"^v8.16.0$","isSourceBranch":false,"state":"NOT_CREATED"}]}] BACKPORT--> Co-authored-by: Dima Arnautov <dmitrii.arnautov@elastic.co>
This commit is contained in:
Kibana Machine
GitHub
parent
eca46fe4bc
commit
107ff84cbf
18 changed files with 493 additions and 75 deletions
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@ -57,7 +57,23 @@ xpack.painless_lab.enabled: false
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xpack.ml.ad.enabled: false
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xpack.ml.dfa.enabled: false
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xpack.ml.nlp.enabled: true
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xpack.ml.nlp:
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enabled: true
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modelDeployment:
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allowStaticAllocations: true
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vCPURange:
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low:
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min: 0
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max: 2
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static: 2
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medium:
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min: 1
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max: 32
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static: 32
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high:
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min: 1
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max: 512
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static: 512
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xpack.ml.compatibleModuleType: 'search'
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data_visualizer.resultLinks.fileBeat.enabled: false
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@ -189,7 +189,20 @@ telemetry.labels.serverless: observability
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xpack.ml.ad.enabled: true
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xpack.ml.dfa.enabled: false
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xpack.ml.nlp.enabled: true
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xpack.ml.nlp:
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enabled: true
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modelDeployment:
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allowStaticAllocations: false
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vCPURange:
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low:
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min: 0
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max: 2
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medium:
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min: 1
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max: 32
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high:
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min: 1
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max: 128
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xpack.ml.compatibleModuleType: 'observability'
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# Disable the embedded Dev Console
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@ -100,7 +100,20 @@ xpack.fleet.packages:
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xpack.ml.ad.enabled: true
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xpack.ml.dfa.enabled: true
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xpack.ml.nlp.enabled: true
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xpack.ml.nlp:
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enabled: true
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modelDeployment:
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allowStaticAllocations: false
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vCPURange:
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low:
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min: 0
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max: 2
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medium:
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min: 1
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max: 32
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high:
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min: 1
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max: 128
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xpack.ml.compatibleModuleType: 'security'
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# Disable the embedded Dev Console
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@ -300,6 +300,16 @@ export default function ({ getService }: PluginFunctionalProviderContext) {
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'xpack.ml.ad.enabled (boolean)',
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'xpack.ml.dfa.enabled (boolean)',
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'xpack.ml.nlp.enabled (boolean)',
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'xpack.ml.nlp.modelDeployment.allowStaticAllocations (boolean)',
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'xpack.ml.nlp.modelDeployment.vCPURange.high.max (number)',
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'xpack.ml.nlp.modelDeployment.vCPURange.high.min (number)',
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'xpack.ml.nlp.modelDeployment.vCPURange.high.static (number?)',
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'xpack.ml.nlp.modelDeployment.vCPURange.low.max (number)',
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'xpack.ml.nlp.modelDeployment.vCPURange.low.min (number)',
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'xpack.ml.nlp.modelDeployment.vCPURange.low.static (number?)',
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'xpack.ml.nlp.modelDeployment.vCPURange.medium.max (number)',
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'xpack.ml.nlp.modelDeployment.vCPURange.medium.min (number)',
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'xpack.ml.nlp.modelDeployment.vCPURange.medium.static (number?)',
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'xpack.osquery.actionEnabled (boolean?)',
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'xpack.remote_clusters.ui.enabled (boolean?)',
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/**
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@ -20,11 +20,29 @@ export const ML_EXTERNAL_BASE_PATH = '/api/ml';
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export type MlFeatures = Record<'ad' | 'dfa' | 'nlp', boolean>;
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export type CompatibleModule = 'security' | 'observability' | 'search';
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export type ExperimentalFeatures = Record<'ruleFormV2', boolean>;
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export interface ModelDeploymentSettings {
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allowStaticAllocations: boolean;
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vCPURange: Record<
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'low' | 'medium' | 'high',
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{
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min: number;
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max: number;
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static?: number;
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}
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>;
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}
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export interface NLPSettings {
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modelDeployment: ModelDeploymentSettings;
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}
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export interface ConfigSchema {
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ad?: { enabled: boolean };
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dfa?: { enabled: boolean };
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nlp?: { enabled: boolean };
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nlp?: {
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enabled: boolean;
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modelDeployment?: ModelDeploymentSettings;
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};
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compatibleModuleType?: CompatibleModule;
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experimental?: {
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ruleFormV2?: { enabled: boolean };
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@ -51,3 +69,9 @@ export function initExperimentalFeatures(
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experimentalFeatures.ruleFormV2 = config.experimental.ruleFormV2.enabled;
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}
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}
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export function initModelDeploymentSettings(nlpSettings: NLPSettings, config: ConfigSchema) {
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if (config.nlp?.modelDeployment !== undefined) {
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nlpSettings.modelDeployment = config.nlp.modelDeployment;
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}
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}
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@ -19,13 +19,13 @@ import { KibanaRenderContextProvider } from '@kbn/react-kibana-context-render';
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import { StorageContextProvider } from '@kbn/ml-local-storage';
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import useLifecycles from 'react-use/lib/useLifecycles';
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import useObservable from 'react-use/lib/useObservable';
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import type { ExperimentalFeatures, MlFeatures } from '../../common/constants/app';
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import type { ExperimentalFeatures, MlFeatures, NLPSettings } from '../../common/constants/app';
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import { ML_STORAGE_KEYS } from '../../common/types/storage';
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import type { MlSetupDependencies, MlStartDependencies } from '../plugin';
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import { setLicenseCache } from './license';
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import { MlRouter } from './routing';
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import type { PageDependencies } from './routing/router';
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import { EnabledFeaturesContextProvider } from './contexts/ml';
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import { EnabledFeaturesContextProvider, MlServerInfoContextProvider } from './contexts/ml';
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import type { StartServices } from './contexts/kibana';
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import { getMlGlobalServices } from './util/get_services';
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@ -42,6 +42,7 @@ interface AppProps {
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isServerless: boolean;
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mlFeatures: MlFeatures;
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experimentalFeatures: ExperimentalFeatures;
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nlpSettings: NLPSettings;
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}
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const localStorage = new Storage(window.localStorage);
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@ -59,6 +60,7 @@ const App: FC<AppProps> = ({
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isServerless,
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mlFeatures,
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experimentalFeatures,
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nlpSettings,
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}) => {
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const pageDeps: PageDependencies = {
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history: appMountParams.history,
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@ -142,7 +144,9 @@ const App: FC<AppProps> = ({
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showMLNavMenu={chromeStyle === 'classic'}
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experimentalFeatures={experimentalFeatures}
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>
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<MlRouter pageDeps={pageDeps} />
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<MlServerInfoContextProvider nlpSettings={nlpSettings}>
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<MlRouter pageDeps={pageDeps} />
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</MlServerInfoContextProvider>
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</EnabledFeaturesContextProvider>
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</DatePickerContextProvider>
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</StorageContextProvider>
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@ -158,7 +162,8 @@ export const renderApp = (
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appMountParams: AppMountParameters,
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isServerless: boolean,
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mlFeatures: MlFeatures,
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experimentalFeatures: ExperimentalFeatures
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experimentalFeatures: ExperimentalFeatures,
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nlpSettings: NLPSettings
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) => {
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appMountParams.onAppLeave((actions) => actions.default());
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@ -170,6 +175,7 @@ export const renderApp = (
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isServerless={isServerless}
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mlFeatures={mlFeatures}
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experimentalFeatures={experimentalFeatures}
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nlpSettings={nlpSettings}
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/>,
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appMountParams.element
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);
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@ -7,3 +7,4 @@
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export { DataSourceContextProvider, useDataSource } from './data_source_context';
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export { EnabledFeaturesContextProvider, useEnabledFeatures } from './serverless_context';
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export { MlServerInfoContextProvider, useMlServerInfo } from './ml_server_info_context';
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@ -0,0 +1,39 @@
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/*
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* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
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* or more contributor license agreements. Licensed under the Elastic License
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* 2.0; you may not use this file except in compliance with the Elastic License
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* 2.0.
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*/
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import React, { type FC, type PropsWithChildren, createContext, useContext } from 'react';
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import type { NLPSettings } from '../../../../common/constants/app';
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export interface MlServerInfoContextValue {
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// TODO add ML server info
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nlpSettings: NLPSettings;
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}
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export const MlServerInfoContext = createContext<MlServerInfoContextValue | undefined>(undefined);
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export const MlServerInfoContextProvider: FC<PropsWithChildren<MlServerInfoContextValue>> = ({
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children,
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nlpSettings,
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}) => {
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return (
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<MlServerInfoContext.Provider
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value={{
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nlpSettings,
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}}
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>
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{children}
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</MlServerInfoContext.Provider>
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);
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};
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export function useMlServerInfo() {
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const context = useContext(MlServerInfoContext);
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if (context === undefined) {
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throw new Error('useMlServerInfo must be used within a MlServerInfoContextProvider');
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}
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return context;
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}
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@ -44,23 +44,82 @@ describe('DeploymentParamsMapper', () => {
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it('should get correct VCU levels', () => {
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expect(mapper.getVCURange('low')).toEqual({
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min: 8,
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min: 0,
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max: 16,
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static: 16,
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});
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expect(mapper.getVCURange('medium')).toEqual({
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min: 24,
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min: 8,
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max: 256,
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static: 256,
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});
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expect(mapper.getVCURange('high')).toEqual({
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min: 264,
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max: 4000,
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static: 800,
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min: 8,
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max: 4096,
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static: 4096,
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});
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});
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it('should enforce adaptive allocations', () => {
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it('maps UI params to API correctly', () => {
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expect(
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mapper.mapUiToApiDeploymentParams({
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deploymentId: 'test-deployment',
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optimized: 'optimizedForSearch',
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adaptiveResources: false,
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vCPUUsage: 'low',
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})
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).toEqual({
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number_of_allocations: 1,
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deployment_id: 'test-deployment',
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model_id: 'test-model',
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priority: 'normal',
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threads_per_allocation: 2,
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});
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expect(
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mapper.mapUiToApiDeploymentParams({
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deploymentId: 'test-deployment',
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optimized: 'optimizedForIngest',
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adaptiveResources: false,
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vCPUUsage: 'low',
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})
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).toEqual({
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deployment_id: 'test-deployment',
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model_id: 'test-model',
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priority: 'normal',
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threads_per_allocation: 1,
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number_of_allocations: 2,
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});
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});
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it('overrides vCPUs levels and enforces adaptive allocations if static support is not configured', () => {
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mapper = new DeploymentParamsMapper(modelId, mlServerLimits, cloudInfo, false, {
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modelDeployment: {
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allowStaticAllocations: false,
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vCPURange: {
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low: { min: 0, max: 2, static: 2 },
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medium: { min: 1, max: 32, static: 32 },
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high: { min: 1, max: 128, static: 128 },
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},
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},
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});
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expect(mapper.getVCURange('low')).toEqual({
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min: 0,
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max: 16,
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static: 16,
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});
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expect(mapper.getVCURange('medium')).toEqual({
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min: 8,
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max: 256,
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static: 256,
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});
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expect(mapper.getVCURange('high')).toEqual({
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min: 8,
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max: 1024,
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static: 1024,
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});
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expect(
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mapper.mapUiToApiDeploymentParams({
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deploymentId: 'test-deployment',
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@ -72,7 +131,7 @@ describe('DeploymentParamsMapper', () => {
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adaptive_allocations: {
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enabled: true,
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max_number_of_allocations: 1,
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min_number_of_allocations: 1,
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min_number_of_allocations: 0,
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},
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deployment_id: 'test-deployment',
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model_id: 'test-model',
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@ -88,15 +147,15 @@ describe('DeploymentParamsMapper', () => {
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vCPUUsage: 'low',
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})
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).toEqual({
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adaptive_allocations: {
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enabled: true,
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max_number_of_allocations: 2,
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min_number_of_allocations: 1,
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},
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deployment_id: 'test-deployment',
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model_id: 'test-model',
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priority: 'normal',
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threads_per_allocation: 1,
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adaptive_allocations: {
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enabled: true,
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max_number_of_allocations: 2,
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min_number_of_allocations: 0,
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},
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});
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});
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});
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threads_per_allocation: 2,
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adaptive_allocations: {
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enabled: true,
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min_number_of_allocations: 1,
|
||||
min_number_of_allocations: 0,
|
||||
max_number_of_allocations: 1,
|
||||
},
|
||||
});
|
||||
|
|
@ -507,7 +566,7 @@ describe('DeploymentParamsMapper', () => {
|
|||
adaptive_allocations: {
|
||||
enabled: true,
|
||||
max_number_of_allocations: 12499,
|
||||
min_number_of_allocations: 4,
|
||||
min_number_of_allocations: 1,
|
||||
},
|
||||
});
|
||||
|
||||
|
|
@ -525,7 +584,7 @@ describe('DeploymentParamsMapper', () => {
|
|||
threads_per_allocation: 1,
|
||||
adaptive_allocations: {
|
||||
enabled: true,
|
||||
min_number_of_allocations: 1,
|
||||
min_number_of_allocations: 0,
|
||||
max_number_of_allocations: 2,
|
||||
},
|
||||
});
|
||||
|
|
@ -544,7 +603,7 @@ describe('DeploymentParamsMapper', () => {
|
|||
threads_per_allocation: 1,
|
||||
adaptive_allocations: {
|
||||
enabled: true,
|
||||
min_number_of_allocations: 3,
|
||||
min_number_of_allocations: 1,
|
||||
max_number_of_allocations: 32,
|
||||
},
|
||||
});
|
||||
|
|
@ -563,7 +622,7 @@ describe('DeploymentParamsMapper', () => {
|
|||
threads_per_allocation: 1,
|
||||
adaptive_allocations: {
|
||||
enabled: true,
|
||||
min_number_of_allocations: 33,
|
||||
min_number_of_allocations: 1,
|
||||
max_number_of_allocations: 99999,
|
||||
},
|
||||
});
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@
|
|||
*/
|
||||
|
||||
import type { MlStartTrainedModelDeploymentRequest } from '@elastic/elasticsearch/lib/api/typesWithBodyKey';
|
||||
import type { NLPSettings } from '../../../common/constants/app';
|
||||
import type { TrainedModelDeploymentStatsResponse } from '../../../common/types/trained_models';
|
||||
import type { CloudInfo } from '../services/ml_server_info';
|
||||
import type { MlServerLimits } from '../../../common/types/ml_server_info';
|
||||
|
|
@ -17,16 +18,16 @@ export type MlStartTrainedModelDeploymentRequestNew = MlStartTrainedModelDeploym
|
|||
|
||||
const THREADS_MAX_EXPONENT = 5;
|
||||
|
||||
// TODO set to 0 when https://github.com/elastic/elasticsearch/pull/113455 is merged
|
||||
const MIN_SUPPORTED_NUMBER_OF_ALLOCATIONS = 1;
|
||||
|
||||
type VCPUBreakpoints = Record<
|
||||
DeploymentParamsUI['vCPUUsage'],
|
||||
{
|
||||
min: number;
|
||||
max: number;
|
||||
/** Static value is used for the number of vCPUs when the adaptive resources are disabled */
|
||||
static: number;
|
||||
/**
|
||||
* Static value is used for the number of vCPUs when the adaptive resources are disabled.
|
||||
* Not allowed in certain environments.
|
||||
*/
|
||||
static?: number;
|
||||
}
|
||||
>;
|
||||
|
||||
|
|
@ -39,26 +40,28 @@ export class DeploymentParamsMapper {
|
|||
private readonly threadingParamsValues: number[];
|
||||
|
||||
/**
|
||||
* vCPUs level breakpoints for cloud cluster with enabled ML autoscaling
|
||||
* vCPUs level breakpoints for cloud cluster with enabled ML autoscaling.
|
||||
* TODO resolve dynamically when Control Pane exposes the vCPUs range.
|
||||
*/
|
||||
private readonly autoscalingVCPUBreakpoints: VCPUBreakpoints = {
|
||||
low: { min: MIN_SUPPORTED_NUMBER_OF_ALLOCATIONS, max: 2, static: 2 },
|
||||
medium: { min: 3, max: 32, static: 32 },
|
||||
high: { min: 33, max: 99999, static: 100 },
|
||||
low: { min: this.minAllowedNumberOfAllocation, max: 2, static: 2 },
|
||||
medium: { min: 1, max: 32, static: 32 },
|
||||
high: { min: 1, max: 99999, static: 128 },
|
||||
};
|
||||
|
||||
/**
|
||||
* vCPUs level breakpoints for serverless projects
|
||||
* Default vCPUs level breakpoints for serverless projects.
|
||||
* Can be overridden by the project specific settings.
|
||||
*/
|
||||
private readonly serverlessVCPUBreakpoints: VCPUBreakpoints = {
|
||||
low: { min: MIN_SUPPORTED_NUMBER_OF_ALLOCATIONS, max: 2, static: 2 },
|
||||
medium: { min: 3, max: 32, static: 32 },
|
||||
high: { min: 33, max: 500, static: 100 },
|
||||
low: { min: this.minAllowedNumberOfAllocation, max: 2, static: 2 },
|
||||
medium: { min: 1, max: 32, static: 32 },
|
||||
high: { min: 1, max: 512, static: 512 },
|
||||
};
|
||||
|
||||
/**
|
||||
* vCPUs level breakpoints based on the ML server limits.
|
||||
* Either on-prem or cloud with disabled ML autoscaling
|
||||
* Either on-prem or cloud with disabled ML autoscaling.
|
||||
*/
|
||||
private readonly hardwareVCPUBreakpoints: VCPUBreakpoints;
|
||||
|
||||
|
|
@ -67,12 +70,26 @@ export class DeploymentParamsMapper {
|
|||
*/
|
||||
private readonly vCpuBreakpoints: VCPUBreakpoints;
|
||||
|
||||
/**
|
||||
* Gets the min allowed number of allocations.
|
||||
* - 0 for serverless and ESS with enabled autoscaling.
|
||||
* - 1 otherwise
|
||||
* @private
|
||||
*/
|
||||
private get minAllowedNumberOfAllocation(): number {
|
||||
return !this.showNodeInfo || this.cloudInfo.isMlAutoscalingEnabled ? 0 : 1;
|
||||
}
|
||||
|
||||
constructor(
|
||||
private readonly modelId: string,
|
||||
private readonly mlServerLimits: MlServerLimits,
|
||||
private readonly cloudInfo: CloudInfo,
|
||||
private readonly showNodeInfo: boolean
|
||||
private readonly showNodeInfo: boolean,
|
||||
private readonly nlpSettings?: NLPSettings
|
||||
) {
|
||||
/**
|
||||
* Initial value can be different for serverless and ESS with autoscaling.
|
||||
*/
|
||||
const maxSingleMlNodeProcessors = this.mlServerLimits.max_single_ml_node_processors;
|
||||
|
||||
this.threadingParamsValues = new Array(THREADS_MAX_EXPONENT)
|
||||
|
|
@ -83,7 +100,7 @@ export class DeploymentParamsMapper {
|
|||
const mediumValue = this.mlServerLimits!.total_ml_processors! / 2;
|
||||
|
||||
this.hardwareVCPUBreakpoints = {
|
||||
low: { min: MIN_SUPPORTED_NUMBER_OF_ALLOCATIONS, max: 2, static: 2 },
|
||||
low: { min: this.minAllowedNumberOfAllocation, max: 2, static: 2 },
|
||||
medium: { min: Math.min(3, mediumValue), max: mediumValue, static: mediumValue },
|
||||
high: {
|
||||
min: mediumValue + 1,
|
||||
|
|
@ -94,6 +111,10 @@ export class DeploymentParamsMapper {
|
|||
|
||||
if (!this.showNodeInfo) {
|
||||
this.vCpuBreakpoints = this.serverlessVCPUBreakpoints;
|
||||
if (this.nlpSettings?.modelDeployment) {
|
||||
// Apply project specific overrides
|
||||
this.vCpuBreakpoints = this.nlpSettings.modelDeployment.vCPURange;
|
||||
}
|
||||
} else if (this.cloudInfo.isMlAutoscalingEnabled) {
|
||||
this.vCpuBreakpoints = this.autoscalingVCPUBreakpoints;
|
||||
} else {
|
||||
|
|
@ -108,6 +129,11 @@ export class DeploymentParamsMapper {
|
|||
return input.vCPUUsage === 'low' ? 2 : Math.max(...this.threadingParamsValues);
|
||||
}
|
||||
|
||||
/**
|
||||
* Returns allocation values accounting for the number of threads per allocation.
|
||||
* @param params
|
||||
* @private
|
||||
*/
|
||||
private getAllocationsParams(
|
||||
params: DeploymentParamsUI
|
||||
): Pick<MlStartTrainedModelDeploymentRequestNew, 'number_of_allocations'> &
|
||||
|
|
@ -126,7 +152,7 @@ export class DeploymentParamsMapper {
|
|||
min_number_of_allocations:
|
||||
Math.floor(levelValues.min / threadsPerAllocation) ||
|
||||
// in any env, allow scale down to 0 only for "low" vCPU usage
|
||||
(params.vCPUUsage === 'low' ? MIN_SUPPORTED_NUMBER_OF_ALLOCATIONS : 1),
|
||||
(params.vCPUUsage === 'low' ? this.minAllowedNumberOfAllocation : 1),
|
||||
max_number_of_allocations: maxValue,
|
||||
};
|
||||
}
|
||||
|
|
@ -148,7 +174,7 @@ export class DeploymentParamsMapper {
|
|||
public getVCURange(vCPUUsage: DeploymentParamsUI['vCPUUsage']) {
|
||||
// general purpose (c6gd) 1VCU = 1GB RAM / 0.5 vCPU
|
||||
// vector optimized (r6gd) 1VCU = 1GB RAM / 0.125 vCPU
|
||||
const vCPUBreakpoints = this.serverlessVCPUBreakpoints[vCPUUsage];
|
||||
const vCPUBreakpoints = this.vCpuBreakpoints[vCPUUsage];
|
||||
|
||||
return Object.entries(vCPUBreakpoints).reduce((acc, [key, val]) => {
|
||||
// as we can't retrieve Search project configuration, we assume that the vector optimized instance is used
|
||||
|
|
@ -165,8 +191,8 @@ export class DeploymentParamsMapper {
|
|||
input: DeploymentParamsUI
|
||||
): MlStartTrainedModelDeploymentRequestNew {
|
||||
const resultInput: DeploymentParamsUI = Object.create(input);
|
||||
if (!this.showNodeInfo) {
|
||||
// Enforce adaptive resources for serverless
|
||||
if (!this.showNodeInfo && this.nlpSettings?.modelDeployment.allowStaticAllocations === false) {
|
||||
// Enforce adaptive resources for serverless projects with prohibited static allocations
|
||||
resultInput.adaptiveResources = true;
|
||||
}
|
||||
|
||||
|
|
@ -177,7 +203,7 @@ export class DeploymentParamsMapper {
|
|||
deployment_id: resultInput.deploymentId,
|
||||
priority: 'normal',
|
||||
threads_per_allocation: this.getNumberOfThreads(resultInput),
|
||||
...(resultInput.adaptiveResources || !this.showNodeInfo
|
||||
...(resultInput.adaptiveResources
|
||||
? {
|
||||
adaptive_allocations: {
|
||||
enabled: true,
|
||||
|
|
|
|||
|
|
@ -41,6 +41,7 @@ import type { CoreStart, OverlayStart } from '@kbn/core/public';
|
|||
import { css } from '@emotion/react';
|
||||
import { toMountPoint } from '@kbn/react-kibana-mount';
|
||||
import { dictionaryValidator } from '@kbn/ml-validators';
|
||||
import type { NLPSettings } from '../../../common/constants/app';
|
||||
import type { TrainedModelDeploymentStatsResponse } from '../../../common/types/trained_models';
|
||||
import { type CloudInfo, getNewJobLimits } from '../services/ml_server_info';
|
||||
import type { ModelItem } from './models_list';
|
||||
|
|
@ -220,7 +221,7 @@ export const DeploymentSetup: FC<DeploymentSetupProps> = ({
|
|||
const helperText = useMemo<string | undefined>(() => {
|
||||
const vcpuRange = deploymentParamsMapper.getVCPURange(config.vCPUUsage);
|
||||
|
||||
if (cloudInfo.isCloud && cloudInfo.isMlAutoscalingEnabled) {
|
||||
if (cloudInfo.isCloud && cloudInfo.isMlAutoscalingEnabled && showNodeInfo) {
|
||||
// Running in cloud with ML autoscaling enabled
|
||||
if (config.adaptiveResources) {
|
||||
// With adaptive resources
|
||||
|
|
@ -285,7 +286,7 @@ export const DeploymentSetup: FC<DeploymentSetupProps> = ({
|
|||
}
|
||||
}
|
||||
} else if (
|
||||
(cloudInfo.isCloud && !cloudInfo.isMlAutoscalingEnabled) ||
|
||||
(cloudInfo.isCloud && !cloudInfo.isMlAutoscalingEnabled && showNodeInfo) ||
|
||||
(!cloudInfo.isCloud && showNodeInfo)
|
||||
) {
|
||||
// Running in cloud with autoscaling disabled or on-prem
|
||||
|
|
@ -352,7 +353,7 @@ export const DeploymentSetup: FC<DeploymentSetupProps> = ({
|
|||
}
|
||||
}
|
||||
} else if (!showNodeInfo) {
|
||||
// Running a Search project in serverless
|
||||
// Running in serverless
|
||||
const vcuRange = deploymentParamsMapper.getVCURange(config.vCPUUsage);
|
||||
|
||||
if (config.adaptiveResources) {
|
||||
|
|
@ -386,6 +387,29 @@ export const DeploymentSetup: FC<DeploymentSetupProps> = ({
|
|||
}
|
||||
);
|
||||
}
|
||||
} else {
|
||||
// Static allocations are allowed for Search projects
|
||||
switch (config.vCPUUsage) {
|
||||
case 'low':
|
||||
return i18n.translate(
|
||||
'xpack.ml.trainedModels.modelsList.startDeployment.serverless.lowCpuStaticHelp',
|
||||
{
|
||||
defaultMessage:
|
||||
'This level set resources to {staticVCUs, plural, one {VCU} other {# VCUs}}, which may be suitable for development, testing, and demos depending on your parameters. It is not recommended for production use.',
|
||||
values: { staticVCUs: vcuRange.static },
|
||||
}
|
||||
);
|
||||
case 'medium':
|
||||
case 'high':
|
||||
return i18n.translate(
|
||||
'xpack.ml.trainedModels.modelsList.startDeployment.serverless.mediumCpuStaticHelp',
|
||||
{
|
||||
defaultMessage:
|
||||
'Your model will consume {staticVCUs, plural, one {VCU} other {# VCUs}}, even when not in use.',
|
||||
values: { staticVCUs: vcuRange.static },
|
||||
}
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
}, [
|
||||
|
|
@ -570,8 +594,8 @@ export const DeploymentSetup: FC<DeploymentSetupProps> = ({
|
|||
<EuiSpacer size={'s'} />
|
||||
|
||||
<EuiFormHelpText id={'vCpuRangeHelp'}>
|
||||
<EuiCallOut size="s">
|
||||
<p>{helperText}</p>
|
||||
<EuiCallOut size="s" data-test-subj="mlModelsStartDeploymentModalVCPUHelperText">
|
||||
{helperText}
|
||||
</EuiCallOut>
|
||||
</EuiFormHelpText>
|
||||
</EuiPanel>
|
||||
|
|
@ -630,6 +654,7 @@ interface StartDeploymentModalProps {
|
|||
cloudInfo: CloudInfo;
|
||||
deploymentParamsMapper: DeploymentParamsMapper;
|
||||
showNodeInfo: boolean;
|
||||
nlpSettings: NLPSettings;
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -645,6 +670,7 @@ export const StartUpdateDeploymentModal: FC<StartDeploymentModalProps> = ({
|
|||
cloudInfo,
|
||||
deploymentParamsMapper,
|
||||
showNodeInfo,
|
||||
nlpSettings,
|
||||
}) => {
|
||||
const isUpdate = !!initialParams;
|
||||
|
||||
|
|
@ -653,20 +679,22 @@ export const StartUpdateDeploymentModal: FC<StartDeploymentModalProps> = ({
|
|||
deploymentParamsMapper.mapApiToUiDeploymentParams(v)
|
||||
);
|
||||
|
||||
const defaultVCPUUsage: DeploymentParamsUI['vCPUUsage'] = showNodeInfo ? 'medium' : 'low';
|
||||
|
||||
return uiParams?.some((v) => v.optimized === 'optimizedForIngest')
|
||||
? {
|
||||
deploymentId: `${model.model_id}_search`,
|
||||
optimized: 'optimizedForSearch',
|
||||
vCPUUsage: 'medium',
|
||||
vCPUUsage: defaultVCPUUsage,
|
||||
adaptiveResources: true,
|
||||
}
|
||||
: {
|
||||
deploymentId: `${model.model_id}_ingest`,
|
||||
optimized: 'optimizedForIngest',
|
||||
vCPUUsage: 'medium',
|
||||
vCPUUsage: defaultVCPUUsage,
|
||||
adaptiveResources: true,
|
||||
};
|
||||
}, [deploymentParamsMapper, model.model_id, model.stats?.deployment_stats]);
|
||||
}, [deploymentParamsMapper, model.model_id, model.stats?.deployment_stats, showNodeInfo]);
|
||||
|
||||
const [config, setConfig] = useState<DeploymentParamsUI>(initialParams ?? getDefaultParams());
|
||||
|
||||
|
|
@ -721,7 +749,9 @@ export const StartUpdateDeploymentModal: FC<StartDeploymentModalProps> = ({
|
|||
onConfigChange={setConfig}
|
||||
errors={errors}
|
||||
isUpdate={isUpdate}
|
||||
disableAdaptiveResourcesControl={!showNodeInfo}
|
||||
disableAdaptiveResourcesControl={
|
||||
showNodeInfo ? false : !nlpSettings.modelDeployment.allowStaticAllocations
|
||||
}
|
||||
deploymentsParams={model.stats?.deployment_stats.reduce<
|
||||
Record<string, DeploymentParamsUI>
|
||||
>((acc, curr) => {
|
||||
|
|
@ -811,7 +841,8 @@ export const getUserInputModelDeploymentParamsProvider =
|
|||
startServices: Pick<CoreStart, 'analytics' | 'i18n' | 'theme'>,
|
||||
startModelDeploymentDocUrl: string,
|
||||
cloudInfo: CloudInfo,
|
||||
showNodeInfo: boolean
|
||||
showNodeInfo: boolean,
|
||||
nlpSettings: NLPSettings
|
||||
) =>
|
||||
(
|
||||
model: ModelItem,
|
||||
|
|
@ -822,7 +853,8 @@ export const getUserInputModelDeploymentParamsProvider =
|
|||
model.model_id,
|
||||
getNewJobLimits(),
|
||||
cloudInfo,
|
||||
showNodeInfo
|
||||
showNodeInfo,
|
||||
nlpSettings
|
||||
);
|
||||
|
||||
const params = initialParams
|
||||
|
|
@ -834,6 +866,7 @@ export const getUserInputModelDeploymentParamsProvider =
|
|||
const modalSession = overlays.openModal(
|
||||
toMountPoint(
|
||||
<StartUpdateDeploymentModal
|
||||
nlpSettings={nlpSettings}
|
||||
showNodeInfo={showNodeInfo}
|
||||
deploymentParamsMapper={deploymentParamsMapper}
|
||||
cloudInfo={cloudInfo}
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ import {
|
|||
getAnalysisType,
|
||||
type DataFrameAnalysisConfigType,
|
||||
} from '@kbn/ml-data-frame-analytics-utils';
|
||||
import { useEnabledFeatures } from '../contexts/ml';
|
||||
import { useEnabledFeatures, useMlServerInfo } from '../contexts/ml';
|
||||
import { useTrainedModelsApiService } from '../services/ml_api_service/trained_models';
|
||||
import { getUserConfirmationProvider } from './force_stop_dialog';
|
||||
import { useToastNotificationService } from '../services/toast_notification_service';
|
||||
|
|
@ -66,6 +66,7 @@ export function useModelActions({
|
|||
} = useMlKibana();
|
||||
|
||||
const { showNodeInfo } = useEnabledFeatures();
|
||||
const { nlpSettings } = useMlServerInfo();
|
||||
|
||||
const cloudInfo = useCloudCheck();
|
||||
|
||||
|
|
@ -124,9 +125,10 @@ export function useModelActions({
|
|||
startServices,
|
||||
startModelDeploymentDocUrl,
|
||||
cloudInfo,
|
||||
showNodeInfo
|
||||
showNodeInfo,
|
||||
nlpSettings
|
||||
),
|
||||
[overlays, startServices, startModelDeploymentDocUrl, cloudInfo, showNodeInfo]
|
||||
[overlays, startServices, startModelDeploymentDocUrl, cloudInfo, showNodeInfo, nlpSettings]
|
||||
);
|
||||
|
||||
const isBuiltInModel = useCallback(
|
||||
|
|
@ -214,7 +216,10 @@ export function useModelActions({
|
|||
},
|
||||
available: (item) => {
|
||||
return (
|
||||
item.model_type === TRAINED_MODEL_TYPE.PYTORCH && item.state === MODEL_STATE.DOWNLOADED
|
||||
item.model_type === TRAINED_MODEL_TYPE.PYTORCH &&
|
||||
!!item.state &&
|
||||
item.state !== MODEL_STATE.DOWNLOADING &&
|
||||
item.state !== MODEL_STATE.NOT_DOWNLOADED
|
||||
);
|
||||
},
|
||||
onClick: async (item) => {
|
||||
|
|
@ -539,7 +544,7 @@ export function useModelActions({
|
|||
},
|
||||
{
|
||||
name: i18n.translate('xpack.ml.inference.modelsList.testModelActionLabel', {
|
||||
defaultMessage: 'Test model',
|
||||
defaultMessage: 'Test',
|
||||
}),
|
||||
description: i18n.translate('xpack.ml.inference.modelsList.testModelActionLabel', {
|
||||
defaultMessage: 'Test model',
|
||||
|
|
|
|||
|
|
@ -68,6 +68,8 @@ import {
|
|||
type ConfigSchema,
|
||||
type ExperimentalFeatures,
|
||||
initExperimentalFeatures,
|
||||
initModelDeploymentSettings,
|
||||
type NLPSettings,
|
||||
} from '../common/constants/app';
|
||||
import type { ElasticModels } from './application/services/elastic_models_service';
|
||||
import type { MlApi } from './application/services/ml_api_service';
|
||||
|
|
@ -135,11 +137,31 @@ export class MlPlugin implements Plugin<MlPluginSetup, MlPluginStart> {
|
|||
private experimentalFeatures: ExperimentalFeatures = {
|
||||
ruleFormV2: false,
|
||||
};
|
||||
private nlpSettings: NLPSettings = {
|
||||
modelDeployment: {
|
||||
allowStaticAllocations: true,
|
||||
vCPURange: {
|
||||
low: {
|
||||
min: 0,
|
||||
max: 2,
|
||||
},
|
||||
medium: {
|
||||
min: 1,
|
||||
max: 16,
|
||||
},
|
||||
high: {
|
||||
min: 1,
|
||||
max: 32,
|
||||
},
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
constructor(private initializerContext: PluginInitializerContext<ConfigSchema>) {
|
||||
this.isServerless = initializerContext.env.packageInfo.buildFlavor === 'serverless';
|
||||
initEnabledFeatures(this.enabledFeatures, initializerContext.config.get());
|
||||
initExperimentalFeatures(this.experimentalFeatures, initializerContext.config.get());
|
||||
initModelDeploymentSettings(this.nlpSettings, initializerContext.config.get());
|
||||
}
|
||||
|
||||
setup(
|
||||
|
|
@ -194,7 +216,8 @@ export class MlPlugin implements Plugin<MlPluginSetup, MlPluginStart> {
|
|||
params,
|
||||
this.isServerless,
|
||||
this.enabledFeatures,
|
||||
this.experimentalFeatures
|
||||
this.experimentalFeatures,
|
||||
this.nlpSettings
|
||||
);
|
||||
},
|
||||
});
|
||||
|
|
|
|||
|
|
@ -20,10 +20,30 @@ const compatibleModuleTypeSchema = schema.maybe(
|
|||
])
|
||||
);
|
||||
|
||||
const vCPURangeSchema = schema.object({
|
||||
min: schema.number(),
|
||||
max: schema.number(),
|
||||
static: schema.maybe(schema.number()),
|
||||
});
|
||||
|
||||
export const configSchema = schema.object({
|
||||
ad: enabledSchema,
|
||||
dfa: enabledSchema,
|
||||
nlp: enabledSchema,
|
||||
nlp: schema.maybe(
|
||||
schema.object({
|
||||
enabled: schema.boolean(),
|
||||
modelDeployment: schema.maybe(
|
||||
schema.object({
|
||||
allowStaticAllocations: schema.boolean(),
|
||||
vCPURange: schema.object({
|
||||
low: vCPURangeSchema,
|
||||
medium: vCPURangeSchema,
|
||||
high: vCPURangeSchema,
|
||||
}),
|
||||
})
|
||||
),
|
||||
})
|
||||
),
|
||||
compatibleModuleType: compatibleModuleTypeSchema,
|
||||
experimental: schema.maybe(
|
||||
schema.object({
|
||||
|
|
|
|||
|
|
@ -33,7 +33,10 @@ export const config: PluginConfigDescriptor<ConfigSchema> = {
|
|||
exposeToBrowser: {
|
||||
ad: true,
|
||||
dfa: true,
|
||||
nlp: true,
|
||||
nlp: {
|
||||
enabled: true,
|
||||
modelDeployment: true,
|
||||
},
|
||||
experimental: true,
|
||||
},
|
||||
};
|
||||
|
|
|
|||
|
|
@ -78,6 +78,15 @@ export function TrainedModelsTableProvider(
|
|||
return rows;
|
||||
}
|
||||
|
||||
/**
|
||||
* Maps the vCPU level to the corresponding value in the slider.
|
||||
*/
|
||||
public readonly vCPULevelValueMap = {
|
||||
low: 0.5,
|
||||
medium: 1.5,
|
||||
high: 2.5,
|
||||
};
|
||||
|
||||
public rowSelector(modelId: string, subSelector?: string) {
|
||||
const row = `~mlModelsTable > ~row-${modelId}`;
|
||||
return !subSelector ? row : `${row} > ${subSelector}`;
|
||||
|
|
@ -512,13 +521,25 @@ export function TrainedModelsTableProvider(
|
|||
}
|
||||
|
||||
public async setVCPULevel(value: 'low' | 'medium' | 'high') {
|
||||
const valuesMap = {
|
||||
low: 0.5,
|
||||
medium: 1.5,
|
||||
high: 2.5,
|
||||
};
|
||||
await mlCommonUI.setSliderValue('mlModelsStartDeploymentModalVCPULevel', valuesMap[value]);
|
||||
await mlCommonUI.assertSliderValue('mlModelsStartDeploymentModalVCPULevel', valuesMap[value]);
|
||||
await mlCommonUI.setSliderValue(
|
||||
'mlModelsStartDeploymentModalVCPULevel',
|
||||
this.vCPULevelValueMap[value]
|
||||
);
|
||||
await this.assertVCPULevel(value);
|
||||
}
|
||||
|
||||
public async assertVCPULevel(value: 'low' | 'medium' | 'high') {
|
||||
await mlCommonUI.assertSliderValue(
|
||||
'mlModelsStartDeploymentModalVCPULevel',
|
||||
this.vCPULevelValueMap[value]
|
||||
);
|
||||
}
|
||||
|
||||
public async assertVCPUHelperText(expectedText: string) {
|
||||
const helperText = await testSubjects.getVisibleText(
|
||||
'mlModelsStartDeploymentModalVCPUHelperText'
|
||||
);
|
||||
expect(expectedText).to.eql(helperText);
|
||||
}
|
||||
|
||||
public async assertAdvancedConfigurationOpen(expectedValue: boolean) {
|
||||
|
|
@ -544,6 +565,33 @@ export function TrainedModelsTableProvider(
|
|||
await this.assertAdvancedConfigurationOpen(open);
|
||||
}
|
||||
|
||||
public async assertAdaptiveResourcesSwitchExists(expectExist: boolean) {
|
||||
if (expectExist) {
|
||||
await testSubjects.existOrFail('mlModelsStartDeploymentModalAdaptiveResources');
|
||||
} else {
|
||||
await testSubjects.missingOrFail('mlModelsStartDeploymentModalAdaptiveResources');
|
||||
}
|
||||
}
|
||||
|
||||
public async toggleAdaptiveResourcesSwitch(enabled: boolean) {
|
||||
await mlCommonUI.toggleSwitchIfNeeded(
|
||||
'mlModelsStartDeploymentModalAdaptiveResources',
|
||||
enabled
|
||||
);
|
||||
|
||||
await this.assertAdaptiveResourcesSwitchChecked(enabled);
|
||||
}
|
||||
|
||||
public async assertAdaptiveResourcesSwitchChecked(expectedValue: boolean) {
|
||||
const isChecked = await testSubjects.isEuiSwitchChecked(
|
||||
'mlModelsStartDeploymentModalAdaptiveResources'
|
||||
);
|
||||
expect(isChecked).to.eql(
|
||||
expectedValue,
|
||||
`Expected adaptive resources switch to be ${expectedValue ? 'checked' : 'unchecked'}`
|
||||
);
|
||||
}
|
||||
|
||||
public async startDeploymentWithParams(
|
||||
modelId: string,
|
||||
params: {
|
||||
|
|
|
|||
|
|
@ -4,18 +4,28 @@
|
|||
* 2.0; you may not use this file except in compliance with the Elastic License
|
||||
* 2.0.
|
||||
*/
|
||||
import { SUPPORTED_TRAINED_MODELS } from '@kbn/test-suites-xpack/functional/services/ml/api';
|
||||
import { FtrProviderContext } from '../../../ftr_provider_context';
|
||||
|
||||
export default function ({ getService, getPageObjects }: FtrProviderContext) {
|
||||
const ml = getService('ml');
|
||||
const PageObjects = getPageObjects(['svlCommonPage']);
|
||||
|
||||
describe('Trained models list', () => {
|
||||
describe('Trained models list', function () {
|
||||
const tinyElser = SUPPORTED_TRAINED_MODELS.TINY_ELSER;
|
||||
|
||||
before(async () => {
|
||||
await PageObjects.svlCommonPage.loginWithPrivilegedRole();
|
||||
await ml.api.importTrainedModel(tinyElser.name, tinyElser.name);
|
||||
// Make sure the .ml-stats index is created in advance, see https://github.com/elastic/elasticsearch/issues/65846
|
||||
await ml.api.assureMlStatsIndexExists();
|
||||
await ml.api.syncSavedObjects();
|
||||
});
|
||||
|
||||
after(async () => {
|
||||
await ml.api.deleteAllTrainedModelsES();
|
||||
});
|
||||
|
||||
describe('page navigation', () => {
|
||||
it('renders trained models list', async () => {
|
||||
await ml.navigation.navigateToMl();
|
||||
|
|
@ -24,9 +34,42 @@ export default function ({ getService, getPageObjects }: FtrProviderContext) {
|
|||
await ml.testExecution.logTestStep(
|
||||
'should display the stats bar and the analytics table with 1 installed trained model and built in elser models in the table'
|
||||
);
|
||||
await ml.trainedModels.assertStats(1);
|
||||
await ml.trainedModels.assertStats(2);
|
||||
await ml.trainedModelsTable.assertTableIsPopulated();
|
||||
});
|
||||
});
|
||||
|
||||
describe('trained models table', () => {
|
||||
it('sets correct VCU ranges for start model deployment', async () => {
|
||||
await ml.trainedModelsTable.openStartDeploymentModal(tinyElser.name);
|
||||
await ml.trainedModelsTable.toggleAdvancedConfiguration(true);
|
||||
|
||||
await ml.testExecution.logTestStep('should have correct default VCU level');
|
||||
// Assert that the default selected level is Low
|
||||
await ml.trainedModelsTable.assertVCPULevel('low');
|
||||
// Assert VCU levels values
|
||||
await ml.trainedModelsTable.assertVCPUHelperText(
|
||||
'This level limits resources to 16 VCUs, which may be suitable for development, testing, and demos depending on your parameters. It is not recommended for production use.'
|
||||
);
|
||||
|
||||
await ml.testExecution.logTestStep(
|
||||
'should set control to high VCU level and update helper text'
|
||||
);
|
||||
await ml.trainedModelsTable.setVCPULevel('high');
|
||||
await ml.trainedModelsTable.assertVCPUHelperText(
|
||||
'Your model will scale up to a maximum of 4,096 VCUs per hour based on your search or ingest load. It will automatically scale down when demand decreases, and you only pay for the resources you use.'
|
||||
);
|
||||
|
||||
// Adaptive resources switch should be checked by default
|
||||
await ml.trainedModelsTable.assertAdaptiveResourcesSwitchChecked(true);
|
||||
|
||||
// Static allocations should be allowed for search projects
|
||||
await ml.trainedModelsTable.toggleAdaptiveResourcesSwitch(false);
|
||||
|
||||
await ml.trainedModelsTable.assertVCPUHelperText(
|
||||
'Your model will consume 4,096 VCUs, even when not in use.'
|
||||
);
|
||||
});
|
||||
});
|
||||
});
|
||||
}
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@
|
|||
* 2.0; you may not use this file except in compliance with the Elastic License
|
||||
* 2.0.
|
||||
*/
|
||||
import { SUPPORTED_TRAINED_MODELS } from '@kbn/test-suites-xpack/functional/services/ml/api';
|
||||
import { ServerlessRoleName } from '../../../../shared/lib';
|
||||
import { FtrProviderContext } from '../../../ftr_provider_context';
|
||||
|
||||
|
|
@ -13,11 +14,20 @@ export default function ({ getService, getPageObjects }: FtrProviderContext) {
|
|||
const PageObjects = getPageObjects(['svlCommonPage']);
|
||||
|
||||
describe('Trained models list', function () {
|
||||
const tinyElser = SUPPORTED_TRAINED_MODELS.TINY_ELSER;
|
||||
|
||||
before(async () => {
|
||||
await PageObjects.svlCommonPage.loginWithRole(ServerlessRoleName.PLATFORM_ENGINEER);
|
||||
await ml.api.importTrainedModel(tinyElser.name, tinyElser.name);
|
||||
// Make sure the .ml-stats index is created in advance, see https://github.com/elastic/elasticsearch/issues/65846
|
||||
await ml.api.assureMlStatsIndexExists();
|
||||
await ml.api.syncSavedObjects();
|
||||
});
|
||||
|
||||
after(async () => {
|
||||
await ml.api.deleteAllTrainedModelsES();
|
||||
});
|
||||
|
||||
describe('page navigation', () => {
|
||||
it('renders trained models list', async () => {
|
||||
await ml.navigation.navigateToMl();
|
||||
|
|
@ -27,9 +37,35 @@ export default function ({ getService, getPageObjects }: FtrProviderContext) {
|
|||
await ml.testExecution.logTestStep(
|
||||
'should display the stats bar and the analytics table with one trained model'
|
||||
);
|
||||
await ml.trainedModels.assertStats(1);
|
||||
await ml.trainedModels.assertStats(2);
|
||||
await ml.trainedModelsTable.assertTableIsPopulated();
|
||||
});
|
||||
});
|
||||
|
||||
describe('trained models table', () => {
|
||||
it('sets correct VCU ranges for start model deployment', async () => {
|
||||
await ml.trainedModelsTable.openStartDeploymentModal(tinyElser.name);
|
||||
await ml.trainedModelsTable.toggleAdvancedConfiguration(true);
|
||||
|
||||
// Adaptive resources switch should be hidden
|
||||
await ml.trainedModelsTable.assertAdaptiveResourcesSwitchExists(false);
|
||||
|
||||
await ml.testExecution.logTestStep('should have correct default VCU level');
|
||||
// Assert that the default selected level is Low
|
||||
await ml.trainedModelsTable.assertVCPULevel('low');
|
||||
// Assert VCU levels values
|
||||
await ml.trainedModelsTable.assertVCPUHelperText(
|
||||
'This level limits resources to 16 VCUs, which may be suitable for development, testing, and demos depending on your parameters. It is not recommended for production use.'
|
||||
);
|
||||
|
||||
await ml.testExecution.logTestStep(
|
||||
'should set control to high VCU level and update helper text'
|
||||
);
|
||||
await ml.trainedModelsTable.setVCPULevel('high');
|
||||
await ml.trainedModelsTable.assertVCPUHelperText(
|
||||
'Your model will scale up to a maximum of 1,024 VCUs per hour based on your search or ingest load. It will automatically scale down when demand decreases, and you only pay for the resources you use.'
|
||||
);
|
||||
});
|
||||
});
|
||||
});
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue