v0.145.0
Summary
The Databricks Python SDK introduces a new list_commands method to the workspace sandbox service. This update allows practitioners to programmatically list and inspect commands within Databricks sandbox environments.
Summary generated by brickster.ai. For the full changelog and any code/binary attachments, follow the GitHub link above.
Topics
More from databricks/databricks-sdk-py
The AI functions service now supports an ai_decide method, and multiple Jobs and Tasks models now accept environment variable configurations. Additionally, command_path in DeploymentSpec and api_secret_ref in SchemaRegistryConfig are no longer required fields.
The MaterializedFeature class now includes job_id and pipeline_id fields. Importing the SDK runtime, WorkspaceClient, or dbutils no longer configures the root logger, preventing interference with subsequent logging.basicConfig calls.
The Databricks Python SDK introduces skill management operations to the AI Gateway workspace client. Users can now create, retrieve, list, update, finalize, and delete skills programmatically using new methods on the AI Gateway service.
This release adds support for the new Mason workspace-level service via the w.mason client. It also introduces service_credential and secret_reference fields to Unity Catalog model provider configurations.
The SDK adds new credential management methods for MCP services in AI Gateway and expands job notifications to include maintenance-related events. Support for additional data formats (Avro, Protobuf) in pipelines and new connection types (TikTok Ads, Smartsheet) extend integration capabilities.
This release adds several new optional fields across Jobs, Serving, and Settings APIs, including priority class configuration for AI Runtime tasks, UC service credentials for Amazon Bedrock, and workspace label management. A new gpu_8x_b300 accelerator type is now available for compute specifications.