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Migration guide

From 0.67.x to 0.68.0​

(Optional) Vertical Pod Autoscaler (VPA) support​

MLOps version 0.68.0 introduces Vertical Pod Autoscaler (VPA) support for the Deployer. Note that VPA activation is optional and performed upon request. VPA allows dynamic scaling of CPU and memory resources based on application usage, improving resource efficiency and optimizing costs.

If the VPA is activated in MLOps, then VPA is supported in the cluster and the VPA CRDs and controllers are up and running alongside the Metrics Server.

For more information, see the Installation section of the VPA GitHub README and the Metrics Server installation instructions.

Note: For a list of known limitations, see the Known limitations section of the VPA GitHub README.

Key changes​
  • VPA Resource Specifications: Added VPA resource specification logic to the Scoring Apps and App Composer, allowing for the dynamic adjustment of their resource limits based on real-time demand.
  • API Updates: New API logic has been added for specifying and validating VPA resources.
  • New VPA Utility Functions: Implemented utility methods for creating and managing VPA resources, including validation and resource quantity handling.
  • Deprecated Function Removal: Removed the deprecated Fabric8 createOrReplace usage in the Scoring Apps.

Removal of HT runtime based on Python 3.8​

The Hydrogen Torch (HT) runtime based on Python 3.8, which was available by default in MLOps version 0.67.x, has been removed as of MLOps version 0.68.0. However, you can still use this runtime by registering it through extra runtimes.

The following requirements need to be met so that the runtime registered through extra runtimes is also visible in the UI:

  • The mlflow/flavors/python_function/loader_module must match mlflow.pyfunc.model.
  • The runtime name must adhere to this pattern: (python-scorer_hydrogen_torch_)(\w*)(38)(\w*).

Configure maximum number of Kubernetes replicas​

With H2O MLOps v0.68.0, you can configure the maximum number of Kubernetes replicas that can be specified when creating a new deployment. To do this, update maxDeploymentReplicas in the values.yaml file (charts/mlops/values.yaml). By default, the maxDeploymentReplicas value is set to 5.

Removal of MLflow runtimes based on Python 3.8​

MLflow runtimes based on Python 3.8 have been removed in MLOps version 0.68.0. Python 3.8 has officially reached end of life as of October 07, 2024.

Pickle runtime based on Python 3.12​

MLOps version 0.68.0 introduces a pickle runtime using Python 3.12. Choose one of the following options:

  • Update your models to work with Python 3.12.

  • If you cannot update your models, the original pickle runtime based on Python 3.8.18 can be configured during MLOps installation by replacing the pickle-3.12.7 image with pickle-3.8.18.

Scheduler routine for MLOps Telemetry​

MLOps version 0.68.0 introduces the SCHEDULER_INTERVAL_SECONDS env variable to run scheduler routine inside the application itself, replacing the use of a cron job. As a result, MLOps Telemetry is deployed as a long-running deployment in the K8s cluster that publishes event data at scheduled intervals. The default value is as follows:

SCHEDULER_INTERVAL_SECONDS=300

Restructured environment security options​

Environment-related security options are now configured in a different way. Prior to v0.68.0, security options were specified using their corresponding numerical values. For example:

securityOptions: [1,2,3]

From v0.68.0 onwards, activated security options are configured in the values.yaml file (charts/mlops/values.yaml) using the security option name. For example:

securityOptions:
    activated:
        - .......
        - "AUTHORIZATION_PROTOCOL_OIDC"
        - .......

You can also set the default security option in the values.yaml file (charts/mlops/values.yaml) using the security option name. The default option serves as the default security setting that will be applied in the UI when creating a deployment and it must be a part of the Activated Security Options List. 

securityOptions:
    activated:
        - .......
        - "PASSPHRASE_HASH_TYPE_PLAINTEXT"
        - .......
    default: "PASSPHRASE_HASH_TYPE_PLAINTEXT"

The following security options are supported in v0.68.0:

  • DISABLED: No security options are activated.
  • PASSPHRASE_HASH_TYPE_PLAINTEXT: Passphrase hash type is plaintext.
  • PASSPHRASE_HASH_TYPE_BCRYPT: Passphrase hash type is bcrypt.
  • AUTHORIZATION_PROTOCOL_OIDC: OIDC authorization protocol is activated.
Notes
  • The Activated Security Options List can not be empty.
  • The default option must be part of the Activated Security Options List.

From v0.68.0 onwards, the way to create a deployment with No Security via API call also differs from previous versions. This change includes the following modifications to the h2o-mlops Python Client:

  • security_options is now a required field for the create_single method of the MLOpsScoringDeployments class.

  • To ensure backward compatibility, v0.68.0 includes a new attribute for the SecurityOptions class, called disabled_security. This attribute allows handling cases with the No Security option by setting it to True, instead of treating None or SecurityOptions() as No Security.

  • Users of MLOps assembly v0.68.0 or above must set disabled_security=True to use the No Security option. For users on older versions, No Security mode can be accessed by using SecurityOptions with default values.

Helm changes​

  • As of version 0.68.0, the ENABLE_USER_EXTERNALID_UPDATE environment variable has been removed from storage, as it is no longer necessary.
  • deploymentEnvironment.corsOrigin has been removed. Use global.cors.allowedOrigin instead.

Default deployment security option​

As of version 0.68.0, the default security option for deployment is PASSPHRASE_HASH_TYPE_PLAINTEXT. Prior to this version, deployments were not secured by default.

Cloud migration information: MLOps storage​

Starting with version 0.68.0, H2O MLOps will no longer support PVCs for storage, transitioning instead to cloud blob storage. MLOps storage will support blob storage from all three major cloud providers—AWS, Azure, and GCP—as well as Minio for on-premises installations. Consequently, all existing data must be migrated from PVC to blob storage during the upgrade to MLOps 0.68.0. All the data migrations steps will be taken care of by MLOps when MLOps storage is deployed in the MIGRATE mode and no manual user intervention is needed. End users shouldn't experience any down time or data loss while the migration is in progress.

Installation instructions​

Deploy storage in MIGRATE mode​

Note: Only follow the instructions in this section if MLOps storage was previously deployed with LOCAL mode using a Kubernetes PVC as the storage.

For AWS environments with S3​

IAM auth is used to access the bucket. Following annotation should be set to the storage service account.

eks.amazonaws.com/role-arn: <iam-role-arn>
storage:
  serviceAccount:
    create: true
    annotations: {
      eks.amazonaws.com/role-arn: <iam-role-arn>
    }
  persistence:
    enabled: true
  cloudPersistence: 
    enabled: true
    url: s3://<bucket-name>?region=<bucket-region>&prefix=<optional-prefix>
  pvcMigration:
    enabled: true
    cloudProvider: s3
    bucketName: <bucket-name>
    region: <bucket-region>
    prefix: <optional-prefix>
For GCP environments with Google Cloud Storage​

Workload identify is used to access the bucket. The following annotation must be set to the storage service account:

iam.gke.io/gcp-service-account: <service_account_email>

Helm values must be set as follows:

storage:
  serviceAccount:
    create: true
    annotations: {
      iam.gke.io/gcp-service-account: <service_account_email>
    }
  persistence:
    enabled: true
  cloudPersistence: 
    enabled: true
    url: gs://<bucket-name>
  pvcMigration:
    enabled: true
    cloudProvider: gcs
    bucketName: <bucket-name>
    region: <bucket-region>
For Azure environments with Azure Blob Storage​

Workload identify is used to access the bucket. The following annotation must be set to the storage service account:

azure.workload.identity/client-id=<client-id>

The following label must be set to storage pods (service and migrator job):

azure.workload.identity/use=true

Helm values must be set as follows:

storage:
  serviceAccount:
    create: true
    annotations: {
      azure.workload.identity/client-id=<client-id>
    }
  extraPodLabels: {
    azure.workload.identity/use=true
  }
  persistence:
    enabled: true
  cloudPersistence: 
    enabled: true
    url: azblob://<bucket-name>
  pvcMigration:
    enabled: true
    cloudProvider: azureblob
    bucketName: <bucket-name>
    region: <bucket-region>
    accountName: <storage-account-name>
For on-premise environments with Minio​
storage:
  persistence:
    enabled: true
  cloudPersistence: 
    enabled: true
    url: s3://<minio-bucket-name>?endpoint=<minio-url>&region=<minio-region>&hostname_immutable=true
    access_key_id: <minio-access-key-id>
    secret_access_key: <minio-secret-access-key>
  pvcMigration:
    enabled: true
    cloudProvider: minio
    bucketName: <bucket-name>
    region: <minio-region>
    endpoint: <minio-url>
    access_key_id: <minio-access-key-id>
    secret_access_key: <minio-secret-access-key>

From 0.66.1 to 0.67.0​

Announcement: Upcoming Java MOJO Runtime removal​

The Java MOJO Runtime will be removed in the 0.69.0 MLOps release. Version 0.68.0 will be the last release to include the Java MOJO Runtime.

Users are advised to migrate to the C++ MOJO Runtime, which is a 1:1 mapping of the Java runtime that accepts a wider range of algorithms Driverless AI may use that the Java runtime does not support, including BERT, GrowNet, and TensorFlow models.

Scoring runtimes​

  • MLflow Runtimes images are twice as large now. This means that deployments of these run-times can take longer due to longer pulling times.

  • Runtimes for DAI 1.10.4.3 and older are removed as of MLOps version 0.67.0.

  • MLflow runtimes support Python 3.8 and later starting with MLOps version 0.67.0.

For more information on scoring runtimes in H2O MLOps, see Scoring runtimes.

Python client​

Starting with version 0.67.0, the official Python client of H2O MLOps is h2o-mlops. The minimum Python version required for the client is Python 3.9.

Built on top of the legacy Python client, h2o-mlops retains all previous functionalities. You can continue to access the legacy client's features through h2o-mlops as needed. For more information, see Python client tutorials - Backend.

Note that users of the legacy client can switch to the new Python client (h2o-mlops) by importing h2o-mlops before using any features of the legacy client. This switch can be made without needing to modify any existing code or import statements.

Removal of Conda from Wave app​

With the removal of Conda as of MLOps version 0.67.0, third-party models can no longer be uploaded to the MLOps frontend using serialized Pickle files. However, you can still upload models from frameworks like scikit-learn, PyTorch, XGBoost, LightGBM, and TensorFlow using MLflow packaged files.

Monitoring data retention​

  • Starting with version 0.67.0, per project data retention duration can be set for monitoring data stored on InfluxDB. To enable this feature, set the MONITOR_INFLUXDB_PER_PROJECT_DATA_RETENTION_PERIOD env to the deployer with a correct duration string. Minimum retention period is 1h and the max is INF. INF will be the default If MONITOR_INFLUXDB_PER_PROJECT_DATA_RETENTION_PERIOD is not set, INF is the default duration.

    -monitor_influxdb_per_project_data_retention_period is exposed for H2O MLOps helm charts to set the MONITOR_INFLUXDB_PER_PROJECT_DATA_RETENTION_PERIOD for deployer.

Emissary​

Switch from emissary to gateway-api:

- Emissary's CRDs are no longer used.
- For mapping deployments to http, Gateway API's HTTPRoute CRD is used.
- Gateway API implemented with [Envoy Gateway](https://gateway.envoyproxy.io/).
- (**Breaking change**) Gateway API doesn't support custom error responses. This means that if a deployment is scaled down, the following custom error body is no longer displayed: `Deployment is scaled down to zero replicas. Please increase the number of replicas to use the deployment.` For more information, see [Custom error responses](https://www.getambassador.io/docs/emissary/latest/topics/running/custom-error-responses).
- (**Breaking change**) - If a deployment is scaled down, error code **500** is thrown instead of **503**.

Other changes​

  • External model registry is removed as of version 0.67.0.
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