<p>I have a lot of standard runtime docker images like python3 with tensorflow 1.7 installed and I want to use these standard images to run some customers code out side of them. The scenario seems quite similar with the serverless. So what is the best way to put the code into runtime dockers?</p>

<p>Right now I am trying to use a persistent volume to mount the code into runtime. But it has a lot of work. Is there some solution easier for this?</p>

<p><strong>UPDATE</strong></p>

<p>What is the workflow for google machine learning engine or floydhub. I think what I want is similar. They have a command line tool to make the local code combine with a standard env.</p>

<p>Following cloud native practices, <a href="https://12factor.net/build-release-run" rel="nofollow noreferrer">code should be immutable</a>, and releases and their dependencies uniquely identifiable for repeat-ability, replic-ability, etc - in short: you should really create images with your src code.</p>

<p>In your case, that would mean basing your Dockerfile on upstream python3 or TF images, there are a couple projects that may help with the <em>workflow</em> for above (code+build-release-run):</p>

<ul>
<li><a href="https://github.com/Azure/draft" rel="nofollow noreferrer">https://github.com/Azure/draft</a> -- looks like better suited for your case</li>
<li><a href="https://github.com/GoogleContainerTools/skaffold" rel="nofollow noreferrer">https://github.com/GoogleContainerTools/skaffold</a> -- more golang friendly afaics</li>
</ul>

<p>Hope it helps --jjo</p>
