databricks Platform · Updated Aug 17, 2026

Base environments are now supported on classic compute (Beta)

You can now use base environments to manage Python dependencies on classic compute.

What it does

You can now use base environments to manage Python dependencies on classic compute. You can use Databricks provided environments or custom workspace level environments. Beta This feature is in Beta.

Why it matters

And because Databricks caches base environments, dependencies don't resolve and install at startup, so compute starts faster and avoids failures from package resolution, repository availability, or network reliability.

Key capabilities

Previous versions of Standard and

Previous versions of Standard and ML environments.

Custom: Specify a custom environment

Custom: Specify a custom environment using a YAML file. Workspace environments: Lists all compatible base environments…

Architecture

Databricks Platform → Platform

In short

You can now use base environments to manage Python dependencies on classic compute. You can use Databricks provided environments or custom workspace level environments.

Read the full documentation ↗

Key takeaway

You can now use base environments to manage Python dependencies on classic compute.

2026-08-17-base-environments-are-now-supported-on-classic-compute-beta Generated by the Databricks Updates Agent · browse all updates