GCP顶点AI托管笔记本无法使用自定义容器
在GCP Vertex AI中，我通过指定一个与用户管理的笔记本电脑内核完美配合的自定义容器来创建一个托管笔记本。 托管笔记本开始，Jupyter Lab似乎没有任何错误迹象。
不幸的是，如果我查看jupyter实验室中的可用内核，则只列出了默认内核，但不列出我的自定义内核。
右侧的活动日志条目显示了一个旋转的车轮“来自[自定义容器]的加载内核”，该内核永远不会消失。
看一下终端，
docker image ls
也不显示自定义容器。显然，它甚至没有被拉到托管笔记本上。
执行
docker Pull [自定义容器]
如果我在终端中 ，以测试与人工制品注册表的连接性，则根据预期正确拉动容器。 但是，在Jupyter Lab（即使是笔记本重新启动之后），自定义内核仍然不可见。
In GCP Vertex AI, I created a Managed Notebook by specifying one of our custom containers which work perfectly with User-Managed Notebook kernels.
The Managed Notebook starts, and Jupyter Lab seems to work without any signs of error.
Unfortunately, if I look at the available kernels in Jupyter Lab, only the default kernels are listed but not my custom kernel.
An activity log entry on the right shows a spinning wheel "Loading kernel from [custom container]" which never disappears.
Taking a look at the terminal,
docker image ls
does not show the custom container either; obviously, it was not even pulled to the Managed Notebook.
If I perform
docker pull [custom container]
in the terminal, to test connectivity to the Artifact Registry then it pulls the container correctly as expected.
However, the custom kernel is still not visible in Jupyter Lab (even after a notebook restart).
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我今天遇到了同样的问题，经过一些实验，我发现我还没有满足 vertex ai docs 。
特别是 - 码头容器图像必须支持睡眠无限。
我的dockerfile包含
启用睡眠无穷大的我替换了
entrypoint然后导入了kernelspec，但笔记本电脑无法连接。
因此，我完全删除了
entrypoint，然后导入了kernelspec，笔记本电脑能够成功连接。希望有帮助。
I faced the same issue today, and after some experimentation, I found that I haven't fulfilled all the requirements in Vertex AI docs.
One in particular - The Docker container image must support sleep infinity.
And my Dockerfile contained
To enable sleep infinity I replaced
ENTRYPOINTwiththen the kernelspec was imported, but the notebook wasn't able to connect.
So I removed the
ENTRYPOINTentirely and then the kernelspec was imported and the notebook was able to connect successfully.Hope that helps.