Runtime and orchestration Multi-pod Kubernetes step operator jobs: Command steps can now run across multiple Kubernetes pods with the Kubernetes step operator. Set pod_count in KubernetesStepOperatorSettings to launch the step as an indexed job, making it easier to distribute command-style workloads across pods. PR #5104 Local Docker sandbox: ZenML now includes a local Docker sandbox, plus a unifi…
Known issues Artifact version deletion via the API is broken in this release and will be fixed in 0.96.3. Artifact versions can still be deleted using the python SDK: from zenml.client import Client Client().delete_artifact_version(..., server_side=False) Dynamic pipelines Explicit start ordering for dynamic steps: Dynamic pipelines now support start_after=... when calling steps, letting you contr…
Run pipelines and steps over SSH: ZenML now includes an SSH orchestrator and SSH step operator for executing workloads on remote machines accessible via SSH (PR #4953). This makes it easier to use existing servers or on-prem infrastructure as execution targets without adopting a full cluster-based backend, while still managing runs through ZenML. What's Changed Add version 0.95.1 to legacy docs by…
Known Issues If the Docker credential helper contains invalid credentials for a registry that ZenML is trying to use, those will have precendence over the credentials set by the service connector and will cause the following error: RuntimeError: Docker error: denied: Your authorization token has expired. Reauthenticate and try again... This will be fixed in the next release, in the meantime docker…
Dynamic pipelines with step operators Dynamic pipeline execution is more reliable when steps use step operators. Fixed Fixed an issue where running a step with a step operator in a dynamic pipeline could fail unless that step was explicitly listed in pipeline.depends_on. ZenML now falls back to the orchestrator image in this case, matching the behavior already used for isolated steps without step…
Known Issues Enabling debug logs while having a GCP, Azure or S3 artifact log store will cause a deadlock at the end of a pipeline or step run. Fixed in 0.95.1. Breaking Changes PR #4844: ZenML now supports Python 3.14, and environments using the local or server extras must also accommodate the SQLModel upgrade from 0.18.0 to 0.38.0. If you depend on those extras, review and update any pinned SQLM…
Infrastructure & Deployment Improvements Enhanced GKE Private Cluster Support: Fixed GCP service connector failures when connecting to private GKE clusters that use Google's DNS-based control plane endpoint. ZenML now connects using the same method as gcloud container clusters get-credentials --dns-endpoint, ensuring reliable access to private clusters. PR #4856 Fixed Docker Build Requirements: Re…
🚀 Live Event Streaming for Pipeline Runs You can now stream custom events in real-time from your running pipelines! Call zenml.streaming.publish() from inside any step or dynamic pipeline to push events that can be consumed via Server-Sent Events (SSE). Enable this feature by setting stream_broker_implementation_source in your server configuration. The initial implementation includes a Redis-based…
New Databricks Step Operator You can now run individual pipeline steps on Databricks using the new Databricks step operator PR #4648. This is useful when you want specific steps to execute in the Databricks runtime while the rest of your pipeline uses a different orchestrator. The Databricks orchestrator also now supports optional tag settings to label jobs and cluster resources for cost tracking,…
What's Changed Add version 0.94.1 to legacy docs by @github-actions[bot] in #4701 Bump the minor-and-patch group with 2 updates by @dependabot[bot] in #4705 Fix zizmor SHA/version mismatch and add zizmor to lint.sh by @strickvl in #4703 Fix broken docs header - triggers by @Json-Andriopoulos in #4707 Misc kitaru extensions by @schustmi in #4715 Introduce a container engine abstraction and add podm…
🎨 Dashboard Enhancements The ZenML dashboard now includes a Run Summary View that provides a comprehensive overview of your pipeline runs at a glance PR #1029. Timeline rows now automatically resize for better visualization of your pipeline execution history PR #1028. 🔧 Pipeline & Trigger Improvements Artifact Name Substitutions for Dynamic Pipelines: You can now use artifact name substitutions in…
🎯 Pipeline Execution Control Pause and Resume Pipeline Runs: Introduced zenml.wait(...) to pause dynamic pipelines while waiting for external inputs, automatically freeing resources until the input is provided. Runs can be resumed automatically (when using remote orchestrators with snapshot support) or manually via zenml pipeline runs resume <ID>. PR #4588 Override Step Inputs on Replay: You can n…
Breaking Changes Old endpoints and client methods for legacy triggers, actions and event sources have been removed. This shouldn't affect you unless you explicitly used those endpoints or methods in your code. Custom step operator flavors must implement new submit_step and get_step_status methods to work with dynamic pipelines. The legacy launch method will only work in static pipelines as a fallb…
Adding the new version to the necessary files. Release notes Co-authored-by: ZenML GmbH info@zenml.io Co-authored-by: Michael Schuster michael.schuster.ffb@googlemail.com