Release notes
Version 0.70.7 (May 30, 2025)β
New Featuresβ
- Added support for the Scoring Runtime in H2O Driverless AI 2.1.0
Version 0.70.6 (May 29, 2025)β
Fixesβ
- [Python Client] Unpinned Python package dependencies.
- [Wave UI] Fixed security vulnerabilities.
Version 0.70.5 (Apr 25, 2025)β
Fixesβ
- Fixed an issue where batch scoring jobs failed when Azure was used as the input data source.
Version 0.70.4 (Apr 8, 2025)β
Fixesβ
- [Deployer] Added missing service accounts to existing deployments during deployment updates.
Version 0.70.3 (Apr 3, 2025)β
Fixesβ
- Fix deployer memory leak
Version 0.70.2 (Apr 3, 2025)β
Fixesβ
- Ensured that the Driverless AI Scoring Pipeline does not run under the root user.
- Resolved regression that prevented scoring on the Java runtime by upgrading to the latest MOJO library.
- Fixed an issue where the Storage connection was incorrectly closed in the deployment server.
Version 0.70.1 (Mar 31, 2025)β
New Featuresβ
- [Runtimes] Added support for H2O Driverless AI v2.0.0.
Fixesβ
- Added missing component versions to Go-based H2O MLOPs components.
- [Batch Scoring] Added missing security context to batch scoring job pods.
- [Security] Fixed newly discovered vulnerabilities.
- [Deployer] Added missing access token for the
readArtifactAsAdmin
operation. - [Helm] Added configuration to refresh intervals for JWKS keys in service-to-service authentication. This ensures timely updates, regardless of Cache-Control headers.
Version 0.70.0 (Mar 13, 2025)β
New featuresβ
- Added support for Workload Identity in all MLOPs components.
- Enabled IAM support across all MLOps components.
- Replaced SPIFFE with service accounts for service-to-service authentication.
- Removed the custom TLS implementation. Users are encouraged to use a service mesh, such as Istio, to secure in-cluster communication.
- Introduced native Batch Scoring implementation. Users can access this capability through the Python client and the new user interface.
- Launched a new user interface that runs alongside the existing one for the upcoming release.
- [Helm] Moved global CORS configuration to Helm.
Fixesβ
- Fixed vulnerabilities across all MLOps components.
- [Storage] Updated to return a
Not Found
gRPC error code when a dataset cannot be found in the storage database. - [Monitor Proxy] Updated to pass secrets instead of plain text for Kafka credentials.
- [Helm] Fixed automatic cleanup of the updater job.
- [Wave UI] Prevented redirection to projects while filling out the Create deployment form.
Version 0.69.7 (Feb 17, 2025)β
Fixesβ
- [Deployer] Resolved an issue where resource limit specifications were not correctly applied to runtime processors.
Version 0.69.6 (Feb 13, 2025)β
Fixesβ
- [Security] Applied security patches from the latest major release.
- [Deployer] Fixed an issue by adding the missing volume mount for Kafka TLS-enabled deployments.
Version 0.69.5 (Feb 6, 2025)β
Fixesβ
- [Wave App] Prevented redirection to
#projects
while filling out thecreate deployment
form.
Version 0.69.4 (Jan 21, 2025)β
Fixesβ
- [Helm Chart] Updated the InfluxDB network policy to allow connections from pods with any of the required labels.
Version 0.69.3 (Jan 17, 2025)β
Fixesβ
- [Wave UI] Fixed an issue where Driverless AI version 1.11.1.1 was incorrectly displayed as 1.11.1 in the UI.
Version 0.69.2 (Jan 14, 2025)β
This release includes new features and fixes.
New featuresβ
- [Deployment Updater] Added functionality to update the image repository during deployment update jobs.
Fixesβ
- [Deployer] Fixed
CVE-2023-3635
.
Version 0.69.1 (Jan 9, 2025)β
This release includes a new feature.
New featuresβ
- [Runtimes] Added support for the Driverless AI runtime version 1.11.1.1.
Version 0.69.0 (Dec 19, 2024)β
This release includes new features and fixes.
New featuresβ
- [Runtimes] Added support for MLflow Model Scorer runtime for Python 3.11 and 3.12, and Dynamic MLflow Model Scorer runtime for Python 3.12.
- [Runtimes] Added support for H2O Driverless AI runtime version 1.10.7.3.
- [Runtimes] Exposed Kubernetes readiness probe on deployments.
- [UI] Replaced bcrypt with PBKDF2 hashing when creating deployments with the
Passphrase (Stored Hashed)
security option. - [Python Client] Added support for SSL settings via the Client constructor.
- [Python Client] Added support for PBKDF2 hashed security option.
- [Deployer] Introduced PBKDF2-based passphrase hashing for improved security.
- [Deployer] Added support for Generic Ephemeral Volumes in the Runtimes.
- [Deployer] Introduced
/readyz
readiness probe endpoint for dynamically deployed runtimes. - [Deployer] Introduced a pod disruption budget for enhanced stability.
- [Helm] Enabled JVM config passing to the monitoring proxy.
- [Helm] Added support for configuring limits and JVM settings in the deployer.
- [Helm] Defined
MLOPS_WAVE_APP_URL
as an environment variable for better configuration.
Fixesβ
- [UI] Ensured UI accessibility even when listing deployments fails.
- [UI] Fixed the issue where signing in from the access denied page resulted in an
Missing parameters: id_token_hint
error. - [Monitor Proxy] Stopped sending
TransactionTransmission
events to downstream transmitters whenenableTransaction
isfalse
. - [Python Client] Added
ValueError
for missing or invalid protocol in the gateway URL. - [Python Client] Configured SSL settings for the functions
get_capabilities
,get_sample_request
, andget_schema
since they access deployment endpoints. - [Storage] Removed storage cleanup cron job and implemented it within a thread of storage itself.
- [Helm] Ensured proper RBAC configuration when multiple groups are specified.
- [Helm] Removed legacy
LOCAL
andMIGRATE
mode code. - [Runtimes] Fixed memory leak in MOJO2 runtimes by upgrading the internal MOJO2 library.
- [Deployer] Ensured that stale deployments will be redeployed.
- [Deployer] Skipped routing migration in cases of errors not related to deployment migration.
- [Deployer] Used response header modifier instead of request header modifier for CORS.
- [Deployer] Added configurable Kubernetes client timeout for better performance and reliability.
Version 0.68.0 (Nov 05, 2024)β
This release includes new features and fixes.
New featuresβ
- [UI] Enabled model and model version deletion.
- [UI] Enabled to use the default deployment security option from the backend.
- [UI] Added support for H2O Driverless AI runtime versions 1.10.7.2 and 1.11.1.
- [Python Client] Introduced a timeout parameter (default: 5 seconds) for MLOpsScoringDeployment's methods:
get_capabilities
,get_sample_request
, andget_schema
. - [Python Client] Added support for creating deployment with token-based authentication as a security option.
- [Python Client] Enabled model deletion.
- [Python Client] Enabled the option to unregister an experiment from a model.
- [Python Client] Introduced the
disabled_security
option to manage deployments with No-Security. - [Storage] Storage only supports blob storage from this release onwards. A one-time migrator job was introduced to migrate all the storage data from K8S PVC to blob storage to support seamless upgrades for users.
- [Telemetry] The MLOps-Telemetry component is no longer running as a cron job; it is now a long-running microservice that publishes event data at scheduled intervals.
- [Helm] MLOps storage can be configured to use blob storages from any of the 3 main clouds AWS, Azure and GCP. Minio is also supported for on-premise installations.
- [Helm] Added
H2O_SCORER_MODEL_LOADING_MODE
set to "subprocess" across all MLOps Python-based runtimes. - [Helm] Introduced a migration job for transferring persistent storage to cloud platforms, now supporting Minio and Azure Blob.
- [Helm] Introduced a
SCHEDULER_INTERVAL_SECONDS
environment variable to configure the interval of mlops-telemetry events publishing. - [Deployer] Introduced Vertical Pod Autoscaling (VPA) support.
- [Deployer] Exposed easy access to the security options available in the cluster.
- [Deployer] Restructured environment security options:
- Activated security options list
- Configurable default security option
- [Deployer] Introduced the No-Security option.
Fixesβ
- [UI] Resolved an error occurring when attempting to view experiment details for experiments with missing metadata.
- [UI] Made the maximum selectable count for deployment replicas configurable.
- [UI] Removed support for MLflow Model Scorer and Dynamic Model Scorers for Python 3.8.
- [UI] Removed support for HT Flexible Runtimes for Python 3.8, including both GPU and CPU variants.
- [Python Client] Improved handling of missing deployment attributes (security and monitor) in backend responses.
- [Python Client] Upgraded the minimum supported Python version to 3.9.
- [gRPC Gateway] Updated
/healthz
to return a 200 status if at least one health check passes, fixing an issue where the gateway would restart if any service was unhealthy. - [Helm] Removed Python 3.8 support for HT and MLFlow runtimes.
- [Helm] Removed the
EnableUserExternalIDUpdate
environment variable from storage for simpler configuration. - [Helm] Added a
-job
suffix to theapp.kubernetes.io/
component label for the monitoring backend job to improve component labeling. - [Helm] Updated rclone configurations to enhance compatibility with Google Cloud Storage (GCS).
- [Helm] Set the telemetry serviceβs replica count to one to optimize resource usage.
- [Helm] Changed the telemetry schedulerβs default interval to 300 seconds for more efficient scheduling.
- [Storage]
IDP_ID
(i.e. keycloak/ Okta ID) is now used as the primary key for the Users table in MLOps Storage. The username is also not a unique field anymore. Existing user data will be migrated accordingly by the Storage itself when it's spinning up. [Deployer] Only "internal" grpc status are now logged at the ERROR level.
Version 0.67.4 (Oct 10, 2024)β
This release includes various fixes.
Fixesβ
- [Helm] Gateway creation is now skipped when
Values.gatewayApi.create
is set to false. - [Helm] You can now specify extra ingress for Influx.
Version 0.67.3 (Oct 01, 2024)β
This release includes new features and fixes.
New featuresβ
- [Runtimes] Added support for the Driverless AI 1.11.1 Python scoring pipeline.
Fixesβ
- [Security] Fixed critical vulnerabilities on Java-based rest scorer and monitoring proxy.
- [Helm] Ensure that registry specification on each image has higher priority over the global image registry configuration.
Version 0.67.2 (Sep 19, 2024)β
This release includes new features and fixes.
New featuresβ
- [Runtimes] Added support for the Driverless AI 1.10.7.2 Python scoring pipeline.
Fixesβ
- [Helm] Removed hard-coded dev/vorvan prefix.
- [Helm] Influx network policy was missing a specific label which lead to cleanup job not running.
Version 0.67.1 (Sep 13, 2024)β
This release includes various fixes.
Fixesβ
- [Monitoring Backend] Updated Dockerfile to use numerical user ID, preventing false warnings in systems that check for root access.
- [Drift] Fixed an issue where the worker image could not find the
datatable
dependency.
Version 0.67.0 (Sep 02, 2024)β
This release includes various vulnerability fixes.
New featuresβ
- [Monitor Proxy] Per project monitoring data retention period can be set for Influx DB during the MLOps installation or upgrade.
- [UI] Added the functionality to log out from the Wave app.
- [UI] Added support for new HT Flexible Runtimes for Python
3.10
, including GPU and CPU variants. - [UI] Added support for DAI runtime versions
1.10.6.3
,1.10.7.1
, and1.11.0
. - [UI] Added support for MLflow Model Scorer and Dynamic Model Scorers for Python
3.10
and3.11
. - [Deployer] Added support for token based authentication for deployments.
- [Runtimes] Added support for DAI runtime versions
1.10.6.3
,1.10.7.1
, and1.11.0
. - [Runtimes] Added support for new HT Flexible Runtimes for Python
3.10
. - [Helm Chart] Added component configuration support for applying tolerations, node selectors, and affinity settings to cron jobs.
- [Helm Chart] Added CA certificate support to the API Gateway deployment.
- [Helm Chart] Replaced Ambassador with Gateway API due to the removal of Emissary.
Fixesβ
- [UI] Removed the functionality for importing models from external model repositories.
- [UI] Removed the ability to upload experiments as serialised Python (
.pkl
/.pickle
) files. - [UI] Disallowed the creation of tags with commas.
- [UI] Reduced the timeout for notification bars.
- [UI] Fixed the issue where a red cross appeared when registering a model shortly after creating an experiment.
- [UI] Removed support for DAI Python runtimes for
1.10.4.3
and older versions. - [UI] Removed support for MLFlow Model Scorer for Python
3.6
and Python3.7
. - [Runtimes] Removed support for DAI Python runtimes for
1.10.4.3
and older versions. - [Python Client] The
external_registry
package has been removed. - [Runtimes] Fix critical vulnerabilities in all runtimes except DAI Python based one.
- [Runtimes] Fix critical and high vulnerabilities in rest scorer.
- [Helm Chart] Corrected the nodeSelector YAML formatting.
- [Helm Chart] Renamed environment variable
STORAGE_URL
toAPI_GATEWAY_URL
in the Wave app. - [Helm Chart] Updated
H2O_WAVE_POST_REDIRECT_URL
to resolve "Page Not Found" errors when logging out from the Wave app. - [Helm Chart] Updated the Wave app secret
H2O_WAVE_OIDC_END_SESSION_URL
for improved logout functionality. - [Helm Chart] The
enable_user_externalid_update
setting is now configurable. - [Helm Chart] Exposed resource requests and limits for monitoring-drift, model-ingest, and api-gateway components.
Version 0.66.1β
This release includes various vulnerability fixes.
New featuresβ
-
Released Base Python Scorer v1.2.0 (BYOM).
-
Released Python based runtimes v1.2.0 (BYOM).
-
Released HT runtime v1.2.0 (BYOM).
-
Released MLflow runtime v1.2.0 (BYOM).
Version 0.66.0 (June 04, 2024)β
This release includes new features, improvements, bug fixes, and security improvements.