[ML] Fixing space checks for recently changed trained model apis (ela…

…stic#156238) Fixes issues raised in elastic#155375 (comment) Kibana trained model endpoints for `_stop`, `_update` and `infer` now require the model ID was well as the deployment ID to be passed to them. Also fixes the stop trained model api when stopping more than one model. It's very likely the elasticsearch `_stop` api will not support a comma separated list of deployment IDs for this release, and so this change calls `_stop` in a loop for each deployment. It also allows for better reporting if any of the deployments fail to stop.
kibanamachine · May 3, 2023 · 9559bee · 9559bee
1 parent 7a4ae72
commit 9559bee
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Showing 11 changed files with 153 additions and 44 deletions.
diff --git a/x-pack/plugins/ml/public/application/model_management/model_actions.tsx b/x-pack/plugins/ml/public/application/model_management/model_actions.tsx
@@ -42,7 +42,7 @@ export function useModelActions({
  onTestAction: (model: ModelItem) => void;
  onModelsDeleteRequest: (modelsIds: string[]) => void;
  onLoading: (isLoading: boolean) => void;
- fetchModels: () => void;
+ fetchModels: () => Promise<void>;
  modelAndDeploymentIds: string[];
 }): Array<Action<ModelItem>> {
  const {
@@ -236,9 +236,13 @@ export function useModelActions({
 
  try {
  onLoading(true);
- await trainedModelsApiService.updateModelDeployment(deploymentParams.deploymentId!, {
- number_of_allocations: deploymentParams.numOfAllocations,
- });
+ await trainedModelsApiService.updateModelDeployment(
+ item.model_id,
+ deploymentParams.deploymentId!,
+ {
+ number_of_allocations: deploymentParams.numOfAllocations,
+ }
+ );
  displaySuccessToast(
  i18n.translate('xpack.ml.trainedModels.modelsList.updateSuccess', {
  defaultMessage: 'Deployment for "{modelId}" has been updated successfully.',
@@ -294,19 +298,38 @@ export function useModelActions({
 
  try {
  onLoading(true);
- await trainedModelsApiService.stopModelAllocation(deploymentIds, {
- force: requireForceStop,
- });
+ const results = await trainedModelsApiService.stopModelAllocation(
+ item.model_id,
+ deploymentIds,
+ {
+ force: requireForceStop,
+ }
+ );
  displaySuccessToast(
  i18n.translate('xpack.ml.trainedModels.modelsList.stopSuccess', {
- defaultMessage: 'Deployment for "{modelId}" has been stopped successfully.',
+ defaultMessage:
+ '{numberOfDeployments, plural, one {Deployment} other {Deployments}} for "{modelId}" has been stopped successfully.',
  values: {
  modelId: item.model_id,
+ numberOfDeployments: deploymentIds.length,
  },
  })
  );
- // Need to fetch model state updates
- await fetchModels();
+ if (Object.values(results).some((r) => r.error !== undefined)) {
+ Object.entries(results).forEach(([id, r]) => {
+ if (r.error !== undefined) {
+ displayErrorToast(
+ r.error,
+ i18n.translate('xpack.ml.trainedModels.modelsList.stopDeploymentWarning', {
+ defaultMessage: 'Failed to stop "{deploymentId}"',
+ values: {
+ deploymentId: id,
+ },
+ })
+ );
+ }
+ });
+ }
  } catch (e) {
  displayErrorToast(
  e,
@@ -319,6 +342,8 @@ export function useModelActions({
  );
  onLoading(false);
  }
+ // Need to fetch model state updates
+ await fetchModels();
  },
  },
  {

diff --git a/x-pack/plugins/ml/public/application/model_management/test_models/models/inference_base.ts b/x-pack/plugins/ml/public/application/model_management/test_models/models/inference_base.ts
@@ -280,7 +280,8 @@ export abstract class InferenceBase<TInferResponse> {
  const inferenceConfig = getInferenceConfig();
 
  const resp = (await this.trainedModelsApi.inferTrainedModel(
- this.deploymentId ?? this.model.model_id,
+ this.model.model_id,
+ this.deploymentId,
  {
  docs: this.getInferDocs(),
  ...(inferenceConfig ? { inference_config: inferenceConfig } : {}),

diff --git a/x-pack/plugins/ml/public/application/services/ml_api_service/trained_models.ts b/x-pack/plugins/ml/public/application/services/ml_api_service/trained_models.ts
@@ -8,8 +8,9 @@
 import * as estypes from '@elastic/elasticsearch/lib/api/typesWithBodyKey';
 
 import { useMemo } from 'react';
-import { HttpFetchQuery } from '@kbn/core/public';
-import { MlSavedObjectType } from '../../../../common/types/saved_objects';
+import type { HttpFetchQuery } from '@kbn/core/public';
+import type { ErrorType } from '@kbn/ml-error-utils';
+import type { MlSavedObjectType } from '../../../../common/types/saved_objects';
 import { HttpService } from '../http_service';
 import { basePath } from '.';
 import { useMlKibana } from '../../contexts/kibana';
@@ -142,32 +143,43 @@ export function trainedModelsApiProvider(httpService: HttpService) {
  });
  },
 
- stopModelAllocation(deploymentsIds: string[], options: { force: boolean } = { force: false }) {
+ stopModelAllocation(
+ modelId: string,
+ deploymentsIds: string[],
+ options: { force: boolean } = { force: false }
+ ) {
  const force = options?.force;
 
- return httpService.http<{ acknowledge: boolean }>({
- path: `${apiBasePath}/trained_models/${deploymentsIds.join(',')}/deployment/_stop`,
+ return httpService.http<Record<string, { acknowledge: boolean; error?: ErrorType }>>({
+ path: `${apiBasePath}/trained_models/${modelId}/${deploymentsIds.join(
+ ','
+ )}/deployment/_stop`,
  method: 'POST',
  query: { force },
  });
  },
 
- updateModelDeployment(modelId: string, params: { number_of_allocations: number }) {
+ updateModelDeployment(
+ modelId: string,
+ deploymentId: string,
+ params: { number_of_allocations: number }
+ ) {
  return httpService.http<{ acknowledge: boolean }>({
- path: `${apiBasePath}/trained_models/${modelId}/deployment/_update`,
+ path: `${apiBasePath}/trained_models/${modelId}/${deploymentId}/deployment/_update`,
  method: 'POST',
  body: JSON.stringify(params),
  });
  },
 
  inferTrainedModel(
  modelId: string,
+ deploymentsId: string,
  payload: estypes.MlInferTrainedModelRequest['body'],
  timeout?: string
  ) {
  const body = JSON.stringify(payload);
  return httpService.http<estypes.MlInferTrainedModelResponse>({
- path: `${apiBasePath}/trained_models/infer/${modelId}`,
+ path: `${apiBasePath}/trained_models/infer/${modelId}/${deploymentsId}`,
  method: 'POST',
  body,
  ...(timeout ? { query: { timeout } as HttpFetchQuery } : {}),

diff --git a/x-pack/plugins/ml/server/lib/ml_client/ml_client.ts b/x-pack/plugins/ml/server/lib/ml_client/ml_client.ts
@@ -132,6 +132,17 @@ export function getMlClient(
  }
  }
 
+ function switchDeploymentId(
+ p: Parameters<MlClient['stopTrainedModelDeployment']>
+ ): Parameters<MlClient['stopTrainedModelDeployment']> {
+ const [params] = p;
+ if (params.deployment_id !== undefined) {
+ params.model_id = params.deployment_id;
+ delete params.deployment_id;
+ }
+ return p;
+ }
+
  async function checkModelIds(modelIds: string[], allowWildcards: boolean = false) {
  const filteredModelIds = await mlSavedObjectService.filterTrainedModelIdsForSpace(modelIds);
  let missingIds = modelIds.filter((j) => filteredModelIds.indexOf(j) === -1);
@@ -491,17 +502,24 @@ export function getMlClient(
  return mlClient.startTrainedModelDeployment(...p);
  },
  async updateTrainedModelDeployment(...p: Parameters<MlClient['updateTrainedModelDeployment']>) {
- const { model_id: modelId, number_of_allocations: numberOfAllocations } = p[0];
+ await modelIdsCheck(p);
+
+ const { deployment_id: deploymentId, number_of_allocations: numberOfAllocations } = p[0];
  return client.asInternalUser.transport.request({
  method: 'POST',
- path: `/_ml/trained_models/${modelId}/deployment/_update`,
+ path: `/_ml/trained_models/${deploymentId}/deployment/_update`,
  body: { number_of_allocations: numberOfAllocations },
  });
  },
  async stopTrainedModelDeployment(...p: Parameters<MlClient['stopTrainedModelDeployment']>) {
+ await modelIdsCheck(p);
+ switchDeploymentId(p);
+
  return mlClient.stopTrainedModelDeployment(...p);
  },
  async inferTrainedModel(...p: Parameters<MlClient['inferTrainedModel']>) {
+ await modelIdsCheck(p);
+ switchDeploymentId(p);
  // Temporary workaround for the incorrect inferTrainedModelDeployment function in the esclient
  if (
  // @ts-expect-error TS complains it's always false

diff --git a/x-pack/plugins/ml/server/lib/ml_client/types.ts b/x-pack/plugins/ml/server/lib/ml_client/types.ts
@@ -5,23 +5,44 @@
  * 2.0.
  */
 
-import { ElasticsearchClient } from '@kbn/core/server';
+import type { TransportRequestOptionsWithMeta } from '@elastic/elasticsearch';
+import type * as estypes from '@elastic/elasticsearch/lib/api/typesWithBodyKey';
+import type { ElasticsearchClient } from '@kbn/core/server';
 import { searchProvider } from './search';
 
 type OrigMlClient = ElasticsearchClient['ml'];
 export interface UpdateTrainedModelDeploymentRequest {
  model_id: string;
+ deployment_id?: string;
  number_of_allocations: number;
 }
 export interface UpdateTrainedModelDeploymentResponse {
  acknowledge: boolean;
 }
 
-export interface MlClient extends OrigMlClient {
+export interface MlStopTrainedModelDeploymentRequest
+ extends estypes.MlStopTrainedModelDeploymentRequest {
+ deployment_id?: string;
+}
+
+export interface MlInferTrainedModelRequest extends estypes.MlInferTrainedModelRequest {
+ deployment_id?: string;
+}
+
+export interface MlClient
+ extends Omit<OrigMlClient, 'stopTrainedModelDeployment' | 'inferTrainedModel'> {
  anomalySearch: ReturnType<typeof searchProvider>['anomalySearch'];
  updateTrainedModelDeployment: (
  payload: UpdateTrainedModelDeploymentRequest
  ) => Promise<UpdateTrainedModelDeploymentResponse>;
+ stopTrainedModelDeployment: (
+ p: MlStopTrainedModelDeploymentRequest,
+ options?: TransportRequestOptionsWithMeta
+ ) => Promise<estypes.MlStopTrainedModelDeploymentResponse>;
+ inferTrainedModel: (
+ p: MlInferTrainedModelRequest,
+ options?: TransportRequestOptionsWithMeta
+ ) => Promise<estypes.MlInferTrainedModelResponse>;
 }
 
 export type MlClientParams =

diff --git a/x-pack/plugins/ml/server/routes/schemas/inference_schema.ts b/x-pack/plugins/ml/server/routes/schemas/inference_schema.ts
@@ -14,6 +14,17 @@ export const modelIdSchema = schema.object({
  modelId: schema.string(),
 });
 
+export const modelAndDeploymentIdSchema = schema.object({
+ /**
+ * Model ID
+ */
+ modelId: schema.string(),
+ /**
+ * Deployment ID
+ */
+ deploymentId: schema.string(),
+});
+
 export const threadingParamsSchema = schema.maybe(
  schema.object({
  number_of_allocations: schema.number(),
@@ -55,3 +66,9 @@ export const pipelineSimulateBody = schema.object({
  docs: schema.arrayOf(schema.any()),
 });
 export const pipelineDocs = schema.arrayOf(schema.string());
+
+export const stopDeploymentSchema = schema.object({
+ modelId: schema.string(),
+ /** force stop */
+ force: schema.maybe(schema.boolean()),
+});
diff --git a/x-pack/plugins/ml/server/routes/trained_models.ts b/x-pack/plugins/ml/server/routes/trained_models.ts
@@ -6,12 +6,14 @@
  */
 
 import { schema } from '@kbn/config-schema';
+import { ErrorType } from '@kbn/ml-error-utils';
 import { RouteInitialization } from '../types';
 import { wrapError } from '../client/error_wrapper';
 import {
  getInferenceQuerySchema,
  inferTrainedModelBody,
  inferTrainedModelQuery,
+ modelAndDeploymentIdSchema,
  modelIdSchema,
  optionalModelIdSchema,
  pipelineSimulateBody,
@@ -327,9 +329,9 @@ export function trainedModelsRoutes({ router, routeGuard }: RouteInitialization)
  */
  router.post(
  {
- path: '/api/ml/trained_models/{modelId}/deployment/_update',
+ path: '/api/ml/trained_models/{modelId}/{deploymentId}/deployment/_update',
  validate: {
- params: modelIdSchema,
+ params: modelAndDeploymentIdSchema,
  body: updateDeploymentParamsSchema,
  },
  options: {
@@ -338,9 +340,10 @@ export function trainedModelsRoutes({ router, routeGuard }: RouteInitialization)
  },
  routeGuard.fullLicenseAPIGuard(async ({ mlClient, request, response }) => {
  try {
- const { modelId } = request.params;
+ const { modelId, deploymentId } = request.params;
  const body = await mlClient.updateTrainedModelDeployment({
  model_id: modelId,
+ deployment_id: deploymentId,
  ...request.body,
  });
  return response.ok({
@@ -361,9 +364,9 @@ export function trainedModelsRoutes({ router, routeGuard }: RouteInitialization)
  */
  router.post(
  {
- path: '/api/ml/trained_models/{modelId}/deployment/_stop',
+ path: '/api/ml/trained_models/{modelId}/{deploymentId}/deployment/_stop',
  validate: {
- params: modelIdSchema,
+ params: modelAndDeploymentIdSchema,
  query: forceQuerySchema,
  },
  options: {
@@ -372,13 +375,25 @@ export function trainedModelsRoutes({ router, routeGuard }: RouteInitialization)
  },
  routeGuard.fullLicenseAPIGuard(async ({ mlClient, request, response }) => {
  try {
- const { modelId } = request.params;
- const body = await mlClient.stopTrainedModelDeployment({
- model_id: modelId,
- force: request.query.force ?? false,
- });
+ const { deploymentId, modelId } = request.params;
+
+ const results: Record<string, { success: boolean; error?: ErrorType }> = {};
+
+ for (const id of deploymentId.split(',')) {
+ try {
+ const { stopped: success } = await mlClient.stopTrainedModelDeployment({
+ model_id: modelId,
+ deployment_id: id,
+ force: request.query.force ?? false,
+ allow_no_match: false,
+ });
+ results[id] = { success };
+ } catch (error) {
+ results[id] = { success: false, error };
+ }
+ }
  return response.ok({
- body,
+ body: results,
  });
  } catch (e) {
  return response.customError(wrapError(e));
@@ -428,9 +443,9 @@ export function trainedModelsRoutes({ router, routeGuard }: RouteInitialization)
  */
  router.post(
  {
- path: '/api/ml/trained_models/infer/{modelId}',
+ path: '/api/ml/trained_models/infer/{modelId}/{deploymentId}',
  validate: {
- params: modelIdSchema,
+ params: modelAndDeploymentIdSchema,
  query: inferTrainedModelQuery,
  body: inferTrainedModelBody,
  },
@@ -440,9 +455,10 @@ export function trainedModelsRoutes({ router, routeGuard }: RouteInitialization)
  },
  routeGuard.fullLicenseAPIGuard(async ({ mlClient, request, response }) => {
  try {
- const { modelId } = request.params;
+ const { modelId, deploymentId } = request.params;
  const body = await mlClient.inferTrainedModel({
  model_id: modelId,
+ deployment_id: deploymentId,
  body: {
  docs: request.body.docs,
  ...(request.body.inference_config