An alternative is to use Library utility (dbutils.library) on a Databricks Runtime cluster, or to upgrade your cluster to Databricks Runtime 7.5 ML or Databricks Runtime 7.5 for Genomics or above. On a High Concurrency cluster running Databricks Runtime 7.4 ML or Databricks Runtime 7.4 for Genomics or below, notebook-scoped libraries are not compatible with table access control or credential passthrough. Notebook-scoped libraries using magic commands are enabled by default.

To install libraries for all notebooks attached to a cluster, use workspace or cluster-installed libraries.ĭ and APIs are removed in Databricks Runtime 11.0. The library utility is supported only on Databricks Runtime, not Databricks Runtime ML or Databricks Runtime for Genomics. On Databricks Runtime 10.5 and below, you can use the Azure Databricks library utility.This article describes how to use these magic commands. Databricks recommends using this approach for new workloads. Run the %pip magic command in a notebook.There are two methods for installing notebook-scoped libraries: You must reinstall notebook-scoped libraries at the beginning of each session, or whenever the notebook is detached from a cluster. Notebook-scoped libraries do not persist across sessions. Other notebooks attached to the same cluster are not affected. When you install a notebook-scoped library, only the current notebook and any jobs associated with that notebook have access to that library. Notebook-scoped libraries let you create, modify, save, reuse, and share custom Python environments that are specific to a notebook.

Install package pip