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    <title>topic Re: Serverless Custom Environment Imaging in Data Engineering</title>
    <link>https://community.databricks.com/t5/data-engineering/serverless-custom-environment-imaging/m-p/157406#M54547</link>
    <description>&lt;P&gt;Hi&amp;nbsp;&lt;a href="https://community.databricks.com/t5/user/viewprofilepage/user-id/230790"&gt;@AlexM&lt;/a&gt;&lt;/P&gt;
&lt;P class="wnfdntf _1ibi0s3f5 _1ibi0s3ce _1ibi0s3ea" data-pm-slice="1 3 []"&gt;There isn’t currently a way to bring a pre-built container image into serverless notebooks/jobs. Serverless supports custom environment YAML files and dependency installation/caching, but Databricks Container Services isn’t supported on serverless compute.&lt;/P&gt;
&lt;P class="wnfdntf _1ibi0s3f5 _1ibi0s3ce _1ibi0s3ea"&gt;So if the goal is to reduce startup time and avoid repeated installs, the best-supported path today is usually to use a workspace-based environment... which is a reusable YAML spec that defines the serverless environment version plus your additional Python packages. Those base environments are pre-built and cached, helping&amp;nbsp;notebooks and jobs start faster.&lt;/P&gt;
&lt;P class="wnfdntf _1ibi0s3f5 _1ibi0s3ce _1ibi0s3ea"&gt;Also note that&amp;nbsp;Init scripts / compute policies aren’t available on serverless&lt;SPAN&gt;, so environment customisation needs to go through the Environment panel / YAML route rather than cluster bootstrap logic.&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN&gt;Databricks says it &lt;/SPAN&gt;automatically caches the notebook virtual environment&lt;SPAN&gt;, so reopening an existing notebook usually doesn’t require reinstalling everything again, even after inactivity.&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN&gt;That cache behaviour also helps &lt;/SPAN&gt;jobs&lt;SPAN&gt; when tasks in the same run share the same dependency set.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P class="wnfdntf _1ibi0s3f5 _1ibi0s3ce _1ibi0s3ea"&gt;For serverless jobs using custom base environments, only the dependencies required for the task are installed at runtime. If your use case needs fully baked images or OS/system-level packages, I’d probably look at standard or dedicated compute with Databricks Container Services instead of serverless.&lt;/P&gt;
&lt;P class="p1"&gt;&lt;FONT size="2" color="#FF6600"&gt;&lt;I&gt;If this answer resolves your question, could you mark it as “Accept as Solution”? That helps other users quickly find the correct fix.&lt;/I&gt;&lt;/FONT&gt;&lt;I&gt;&lt;/I&gt;&lt;/P&gt;</description>
    <pubDate>Thu, 21 May 2026 11:51:40 GMT</pubDate>
    <dc:creator>Ashwin_DSA</dc:creator>
    <dc:date>2026-05-21T11:51:40Z</dc:date>
    <item>
      <title>Serverless Custom Environment Imaging</title>
      <link>https://community.databricks.com/t5/data-engineering/serverless-custom-environment-imaging/m-p/157346#M54533</link>
      <description>&lt;P&gt;Hi,&lt;BR /&gt;&lt;BR /&gt;I'm looking at moving from job clusters to serverless environments. Ideally to reduce cost and improve start up time.&lt;BR /&gt;I can see that it is now possible to specify a custom environment .yaml file - and specify Python packages to be installed.&lt;BR /&gt;&lt;BR /&gt;Is there any mechanism to 'pre-install' or use a container image for the serverless? As these packages can take a while to install. I read that there is some caching going on - but the details are a bit opaque?&lt;BR /&gt;&lt;BR /&gt;Would appreciate any advice,&lt;BR /&gt;&lt;BR /&gt;Thanks,&lt;BR /&gt;Alex&lt;BR /&gt;&lt;BR /&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Wed, 20 May 2026 15:40:41 GMT</pubDate>
      <guid>https://community.databricks.com/t5/data-engineering/serverless-custom-environment-imaging/m-p/157346#M54533</guid>
      <dc:creator>AlexM</dc:creator>
      <dc:date>2026-05-20T15:40:41Z</dc:date>
    </item>
    <item>
      <title>Re: Serverless Custom Environment Imaging</title>
      <link>https://community.databricks.com/t5/data-engineering/serverless-custom-environment-imaging/m-p/157406#M54547</link>
      <description>&lt;P&gt;Hi&amp;nbsp;&lt;a href="https://community.databricks.com/t5/user/viewprofilepage/user-id/230790"&gt;@AlexM&lt;/a&gt;&lt;/P&gt;
&lt;P class="wnfdntf _1ibi0s3f5 _1ibi0s3ce _1ibi0s3ea" data-pm-slice="1 3 []"&gt;There isn’t currently a way to bring a pre-built container image into serverless notebooks/jobs. Serverless supports custom environment YAML files and dependency installation/caching, but Databricks Container Services isn’t supported on serverless compute.&lt;/P&gt;
&lt;P class="wnfdntf _1ibi0s3f5 _1ibi0s3ce _1ibi0s3ea"&gt;So if the goal is to reduce startup time and avoid repeated installs, the best-supported path today is usually to use a workspace-based environment... which is a reusable YAML spec that defines the serverless environment version plus your additional Python packages. Those base environments are pre-built and cached, helping&amp;nbsp;notebooks and jobs start faster.&lt;/P&gt;
&lt;P class="wnfdntf _1ibi0s3f5 _1ibi0s3ce _1ibi0s3ea"&gt;Also note that&amp;nbsp;Init scripts / compute policies aren’t available on serverless&lt;SPAN&gt;, so environment customisation needs to go through the Environment panel / YAML route rather than cluster bootstrap logic.&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN&gt;Databricks says it &lt;/SPAN&gt;automatically caches the notebook virtual environment&lt;SPAN&gt;, so reopening an existing notebook usually doesn’t require reinstalling everything again, even after inactivity.&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN&gt;That cache behaviour also helps &lt;/SPAN&gt;jobs&lt;SPAN&gt; when tasks in the same run share the same dependency set.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P class="wnfdntf _1ibi0s3f5 _1ibi0s3ce _1ibi0s3ea"&gt;For serverless jobs using custom base environments, only the dependencies required for the task are installed at runtime. If your use case needs fully baked images or OS/system-level packages, I’d probably look at standard or dedicated compute with Databricks Container Services instead of serverless.&lt;/P&gt;
&lt;P class="p1"&gt;&lt;FONT size="2" color="#FF6600"&gt;&lt;I&gt;If this answer resolves your question, could you mark it as “Accept as Solution”? That helps other users quickly find the correct fix.&lt;/I&gt;&lt;/FONT&gt;&lt;I&gt;&lt;/I&gt;&lt;/P&gt;</description>
      <pubDate>Thu, 21 May 2026 11:51:40 GMT</pubDate>
      <guid>https://community.databricks.com/t5/data-engineering/serverless-custom-environment-imaging/m-p/157406#M54547</guid>
      <dc:creator>Ashwin_DSA</dc:creator>
      <dc:date>2026-05-21T11:51:40Z</dc:date>
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