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I have a Jupyter Python Notebook that is exposing an HTTP Server via Kernel Gateway. The dockerfile works locally perfectly, but when I deploy it on AWS, it seems to be stuck.
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It turns out my kubernetes pod was low on resources. I checked the resource usage locally and updated my pod to use the same amount of resources and now it's working great.
@r4881t - good sleuthing. The gateway was working fine, just that the kernel process, probably when attempting to execute some code, terminated. Gateway (jupyter_client) detected that termination and attempted to restart the kernel process. This scenario is typically the result of missing packages, resources, etc. I.e., something to do with what the kernel process needs to accomplish and, very much, a function of its runtime environment.
By the way. If you want to have your kernel executed in a separate pod (given your Gateway is running in K8s anyway), you should take a look at adding Gateway Provisioners to your container and configure a kubernetes-based kernel to house your execution. This allows you to apply different resource configurations specific to your kernel that are not needed by the launching server.
Description
I have a Jupyter Python Notebook that is exposing an HTTP Server via Kernel Gateway. The dockerfile works locally perfectly, but when I deploy it on AWS, it seems to be stuck.
My Dockerfile
My code structure is as below
When I run it on my local, I get it working fine with the following outputs
However, when I deploy it on AWS EKS Cluster, it seems to be stuck
Expected behavior
I expect the kernel gateway to expose the HTTP Server.
Paste the output from running
jupyter troubleshoot
from the command line here.The text was updated successfully, but these errors were encountered: