Closed rabernat closed 6 years ago
It looks like you might also want to set request? http://callmeradical.com/post/k8s-resource-limits-requests-qos/ I'm not sure.
Two other points
@jacobtomlinson any thoughts on what's going on with the travis test failure on this PR?
the test failure might have to do with the fact that I just make a quick PR by editing within github (rather than forking / branching / pushing) and therefore created another branch rabernat-patch-1
within this repo.
The error comes from chartpress. I wouldn't worry for now. I'm actively looking at updating the Travis stuff on this repo.
While I agree that this makes sense I'm wondering if we shouldn't be updating the defaults here and instead updating the GCE deployment config here.
I guess it depends if you think this change should be made for everyone, or just for pangeo.pydata.org. Happy to defer to you on this!
I agree it makes sense to update the pangeo.pydata.org specific config, rather than the master template. The problem is that I don’t understand the relationship between these pieces very well.
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On Apr 9, 2018, at 7:01 AM, Jacob Tomlinson notifications@github.com wrote:
The error comes from chartpress. I wouldn't worry for now. I'm actively looking at updating the Travis stuff on this repo.
While I agree that this makes sense I'm wondering if we shouldn't be updating the defaults here and instead updating the GCE deployment config here.
I guess it depends if you think this change should be made for everyone, or just for pangeo.pydata.org. Happy to defer to you on this!
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That's fair.
This repo is the master template. Perhaps I should update the README to make that clearer.
What is the relationship between values.yaml
in this repo and pangeo/gce/jupyter-config.yaml
in the other repo? Does that one somehow "extend" this one?
nYes, the config files stack.
On Mon, Apr 9, 2018 at 8:32 AM, Ryan Abernathey notifications@github.com wrote:
What is the relationship between values.yaml in this repo and pangeo/gce/jupyter-config.yaml in the other repo? Does that one somehow "extend" this one?
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I think it would be good to have more RAM and CPU in the main notebook pod. Since that's where the scheduler runs, RAM can become a bottleneck for large dask graphs. Also, the notebook pod has to do more work than the worker pods because it does all the visualization etc.
Would this change accomplish that?