# Deploy and monitor models on GCP > Deploy and monitor models on GCP - Deploy and monitor DataRobot models on Google Cloud Platform > (GCP). This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context. Companion generated at `2026-04-24T16:03:56.550433+00:00` (UTC). ## Primary page - [Deploy and monitor models on GCP](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html): Full documentation for this topic (HTML). ## Sections on this page - [Overview](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#overview): In-page section heading. - [Prerequisites](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#prerequisites): In-page section heading. - [Procedure](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#procedure): In-page section heading. - [Download a model package](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#download-a-model-package): In-page section heading. - [Create a Docker container image](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#create-a-docker-container-image): In-page section heading. - [Run Docker locally](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#run-docker-locally): In-page section heading. - [Push Docker image to the Container Registry](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#push-docker-image-to-the-container-registry): In-page section heading. - [Create the GKE cluster](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#create-the-GKE-cluster): In-page section heading. - [Deploy the Docker image to GKE](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#deploy-the-docker-image-to-gke): In-page section heading. - [Expose your model](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#expose-your-model): In-page section heading. - [Create an external deployment](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#create-an-external-deployment): In-page section heading. - [Link PPS on K8s to MLOps](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#link-pps-on-k8s-to-mlops): In-page section heading. - [Deploy new Docker image (optional)](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#deploy-new-docker-image-optional): In-page section heading. - [Clean up](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#clean-up): In-page section heading. - [K8s configuration files](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#K8s-configuration-files): In-page section heading. - [Deployment configuration file](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#deployment-configuration): In-page section heading. - [Service configuration file](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#service-configuration): In-page section heading. ## Related documentation - [Classic UI documentation](https://docs.datarobot.com/en/docs/classic-ui/index.html): Linked from this page. - [Integrations](https://docs.datarobot.com/en/docs/classic-ui/integrations/index.html): Linked from this page. - [Google](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/index.html): Linked from this page. - [DataRobot MLOps](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html): Linked from this page. - [Portable Prediction Server](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html): Linked from this page. - [documentation](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html#register-external-model-packages): Linked from this page. - [Developer Tools](https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html#api-key-management): Linked from this page. ## Documentation content # Deploy and monitor models on GCP > [!NOTE] Availability information > The MLOps model package export feature used in this procedure is off by default. Contact your DataRobot representative or administrator for information on enabling it. > > Feature flag: Enable MMM model package export The following describes the process of deploying a DataRobot model on the Google Cloud Platform (GCP) using the Google Kubernetes Engine (GKE). ## Overview [DataRobot MLOps](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html) provides a central hub to deploy, monitor, manage, and govern all your models in production. With MLOps, you aren't limited to serving DataRobot models on the dedicated scalable prediction servers inside the DataRobot cluster. You can also deploy DataRobot models into Kubernetes (K8s) clusters while maintaining the advantages of DataRobot's model monitoring capabilities. This exportable DataRobot model is called a [Portable Prediction Server](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html) (PPS) and is similar to Docker containers in the flexibility and portability it provides. A PPS is based on Docker containers and contains a DataRobot model with embedded monitoring agents. Using this approach, a DataRobot model is made available via a scalable deployment environment for usage, and associated data can be tracked in the centralized DataRobot MLOps dashboard with all of its monitoring and governance advantages. Unifying the portability of DataRobot model Docker images with the scalability of a K8s platform results in a powerful ML solution ready for production usage. ### Prerequisites You must complete the following steps before creating the main configuration. 1. Install Google Cloud SDK appropriate for your operating system (seeGoogle's documentation). 2. Run the following at a command prompt: gcloud init You will be asked to choose the existing project or to create a new one and also to select the compute zone. For example: 3. Install the Kubernetes command-line tool: gcloud components install kubectl The output will be similar to: ## Procedure The following sections, each a step in the process, describe the procedure for deploying and monitoring DataRobot models on the GCP platform via a PPS. The examples use the [Kaggle housing prices](https://www.kaggle.com/c/home-data-for-ml-course/data) dataset. ### Download a model package Build models using the housing prices dataset. Once Autopilot finishes, you can create and download the MLOps model package. To do this, navigate to the Models tab to select a model and click Predict > Deploy. In the MLOps Package section, select Generate & Download. DataRobot generates a model package (.mlpkg file) containing all the necessary information about the model. ### Create a Docker container image To create a Docker container image with the MLOps package: 1. After the model package download (started in the previous step) completes, download thePPS base image. 2. Once you have the PPS base image, use the following Dockerfile to generate an image that includes the DataRobot model package: NoteTo copy the.mlpkgfile into the Docker image, make sure the Dockerfile and the.mlpkgfile are in the same folder. FROMdatarobot/datarobot-portable-prediction-api: COPY.mlpkg/opt/ml/model 3. Set thePROJECT_IDenvironment variable to your Google Cloud project ID (the project ID you defined during the Google Cloud SDK installation). ThePROJECT_IDassociates the container image with your project's Container Registry: export PROJECT_ID= ai-XXXXXX-XXXXXX 4. Build and tag the Docker image. For example: docker build -t gcr.io/${PROJECT_ID}/house-regression-model:v1 5. Run thedocker imagescommand to verify that the build was successful: The generated image contains the DataRobot model and the monitoring agent used to transfer the service and model health metrics back to the DataRobot MLOps platform. ### Run Docker locally While technically an optional step, best practice advises always testing your image locally to save time and network bandwidth. To run locally: 1. Run your Docker container image: docker run --rm --name house-regression -p 8080:8080 -it gcr.io/${PROJECT_ID}/house-regression-model:v1 2. Score the data locally to test if the model works as expected: curl -X POST http://localhost:8080/predictions -H "Content-Type: text/csv" --data-binary @/Users/X.X/community/docker/kaggle_house_test_dataset.csv NoteUpdate the path to thekaggle_house_test_dataset.csvdataset to match the path locally on your workstation. ### Push Docker image to the Container Registry Once you have tested and validated the container image locally, upload it to a registry so that your Google Kubernetes Engine (GKE) cluster can download and run it. 1. Configure the Docker command-line tool to authenticate to Container Registry: gcloud auth configure-docker 2. Push the Docker imageyou builtto the Container Registry: docker push gcr.io/${PROJECT_ID}/house-regression-model:v1 > [!NOTE] Note > Pushing to the Container Registry may result in the `storage.buckets.create` permission issue. If you receive this error, contact the administrator of your GCP account. ### Create the GKE cluster After storing the Docker image in the Container Registry, you next create a GKE cluster, as follows: 1. Set your project ID and Compute Engine zone options for thegcloudtool: gcloud config set project $PROJECT_ID gcloud config set compute/zone europe-west1-b 2. Create the cluster: gcloud container clusters create house-regression-cluster This command finishes as follows: 3. After the command completes, run the following command to see the cluster worker instances: gcloud compute instances list The output is similar to: > [!NOTE] Note > Pushing to the Container Registry may result in the `gcloud.container.clusters.create` permission issue. If you receive this error, contact the administrator of your GCP account. ### Deploy the Docker image to GKE To deploy your image to GKE: 1. Create a Kubernetes deployment for your Docker image: kubectl create deployment house-regression-app --image=gcr.io/${PROJECT_ID}/house-regression-model:v1 2. Set the baseline number of deployment replicas to 3 (i.e., the deployment will always have 3 running pods). kubectl scale deployment house-regression-app --replicas=3 3. K8s provides the ability to manage resources in a flexible, automatic manner. For example, you can create a HorizontalPodAutoscaler resource for your deployment: kubectl autoscale deployment house-regression-app --cpu-percent=80 --min=1 --max=5 4. Run the following command to check that the pods you created are all operational and in a running state (e.g., you may to see up to 5 running pods as requested in the previous autoscale step): kubectl get pods The output is similar to: #### Expose your model The default service type in GKE is called ClusterIP, where the service gets an IP address reachable only from inside the cluster. To expose a Kubernetes service outside of the cluster, you must create a service of type `LoadBalancer`. This type of service spawns an External Load Balancer IP for a set of pods, reachable via the internet. 1. Use thekubectl exposecommand to generate a Kubernetes service for thehouse-regression-appdeployment: kubectl expose deployment house-regression-app --name=house-regression-app-service --type=LoadBalancer --port 80 --target-port 8080 Where: - --port is the port number configured on the Load Balancer - --target-portis the port number that thehouse-regression-appcontainer is listening on. - Run the following command to view service details: kubectl get service The output is similar to: - Copy theEXTERNAL-IPaddress from the service details. - Score your model using theEXTERNAL-IPaddress. curl -X POST http://XX.XX.XX.XX/predictions -H "Content-Type: text/csv" --data- binary @/Users/X.X/community/docker/kaggle_house_test_dataset.csv NoteUpdate the IP address placeholder above with theEXTERNAL-IPaddress you copied and update the path to thekaggle_house_test_dataset.csvdataset to match the path locally on your workstation. > [!NOTE] Note > The cluster is open to all incoming requests at this point. See the [Google documentation](https://cloud.google.com/kubernetes-engine/docs/concepts/access-control) to apply more fine-grained role-based access control (RBAC). ## Create an external deployment To create an external deployment in MLOps: 1. Navigate to theModel Registry > Model Packages > Add New Packageand follow the instructions in thedocumentation. ClickAdd new external model package. 2. Make a note of theMLOps model IDfound in the URL. You will use this whenlinking PPS and MLops. While still on theModel Registrypage and within the expanded new package, select theDeploymentstab and clickCreate new deployment. The deployment page loads prefilled with information from the model package you created. 3. Make a note of theMLOps deployment ID(earlier, you copied the model ID). You will use this whenlinking PPS and MLOps. ### Link PPS on K8s to MLOps Finally, update the K8s deployment configuration with the PPS and monitoring agent configuration. Add the following environment variables into the K8s Deployment configuration (see the complete configuration file [here](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#K8s-configuration-files)): ``` PORTABLE_PREDICTION_API_WORKERS_NUMBER=2 PORTABLE_PREDICTION_API_MONITORING_ACTIVE=True PORTABLE_PREDICTION_API_MONITORING_SETTINGS=output_type=output_dir;path=/tmp;max_files=50;file_max_size=10240 000;model_id=;deployment_id= MONITORING_AGENT=True MONITORING_AGENT_DATAROBOT_APP_URL= MONITORING_AGENT_DATAROBOT_APP_TOKEN= ``` > [!NOTE] Note > You can obtain the `MONITORING_AGENT_DATAROBOT_APP_TOKEN` from the [Developer Tools](https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html#api-key-management). ### Deploy new Docker image (optional) To upgrade the deployed Docker image, simply: 1. Create a new version of your Docker image: docker build -t gcr.io/${PROJECT_ID}/house-regression-model:v2 2. Push the new image to the Container Registry: docker push gcr.io/${PROJECT_ID}/house-regression-model:v2` 3. Apply a rolling update to the existing deployment with an image update: kubectl set image deployment/house-regression-app house-regression-model=gcr.io/${PROJECT_ID}/house-regression-model:v2 4. Watch the pods running the v1 image terminate, and new pods running the v2 image spin up: kubectl get pods ### Clean up To finish the process of setting up using the GCP platform via a Portable Prediction Server (PPS) for deployments, do the following. 1. Delete the service: kubectl delete service house-regression-app-service 2. Delete the cluster: gcloud container clusters delete house-regression-cluster ## K8s configuration files The following sections provide deployment and service configuration files for reference. ### Deployment configuration file ``` apiVersion: apps/v1 kind: Deployment metadata: annotations: deployment.kubernetes.io/revision: "2" creationTimestamp: "2020-07-08T12:47:27Z" generation: 8 labels: app: house-regression-app name: house-regression-app namespace: default resourceVersion: "14171" selfLink: /apis/apps/v1/namespaces/default/deployments/house-regression-app uid: 2de869fc-c119-11ea-8156-42010a840053 spec: progressDeadlineSeconds: 600 replicas: 5 revisionHistoryLimit: 10 selector: matchLabels: app: house-regression-app strategy: rollingUpdate: maxSurge: 25% maxUnavailable: 25% type: RollingUpdate template: metadata: creationTimestamp: null labels: app: house-regression-app spec: containers: - env: - name: PORTABLE_PREDICTION_API_WORKERS_NUMBER value: "2" - name: PORTABLE_PREDICTION_API_MONITORING_ACTIVE value: "True" - name: PORTABLE_PREDICTION_API_MONITORING_SETTINGS value: output_type=output_dir;path=/tmp;max_files=50;file_max_size=10240000;model_id=;deployment_id= - name: MONITORING_AGENT value: "True" - name: MONITORING_AGENT_DATAROBOT_APP_URL value: https://app.datarobot.com/ - name: MONITORING_AGENT_DATAROBOT_APP_TOKEN value: image: gcr.io/${PROJECT_ID}/house-regression-model:v1 imagePullPolicy: IfNotPresent name: house-regression-model resources: {} terminationMessagePath: /dev/termination-log terminationMessagePolicy: File dnsPolicy: ClusterFirst restartPolicy: Always schedulerName: default-scheduler securityContext: {} terminationGracePeriodSeconds: 30 status: availableReplicas: 5 conditions: - lastTransitionTime: "2020-07-08T12:47:27Z" lastUpdateTime: "2020-07-08T13:40:47Z" message: ReplicaSet "house-regression-app-855b44f748" has successfully progressed. reason: NewReplicaSetAvailable status: "True" type: Progressing - lastTransitionTime: "2020-07-08T13:41:39Z" lastUpdateTime: "2020-07-08T13:41:39Z" message: Deployment has minimum availability. reason: MinimumReplicasAvailable status: "True" type: Available observedGeneration: 8 readyReplicas: 5 replicas: 5 updatedReplicas: 5 ``` #### Service configuration file ``` apiVersion: v1 kind: Service metadata: creationTimestamp: "2020-07-08T12:58:13Z" labels: app: house-regression-app name: house-regression-app-service namespace: default resourceVersion: "5055" selfLink: /api/v1/namespaces/default/services/house-regression-app-service uid: aeb836cd-c11a-11ea-8156-42010a840053 spec: clusterIP: 10.31.242.132 externalTrafficPolicy: Cluster ports: - nodePort: 30654 port: 80 protocol: TCP targetPort: 8080 selector: app: house-regression-app sessionAffinity: None type: LoadBalancer status: loadBalancer: ingress: - ip: XX.XX.XXX.XXX ```