Comments (3)
mrocklin
commented on September 16, 2024
If you provide the dashboard address into the text field at the top and
then press enter then it will show those plots.
So if you navigate to the status page of your dashboard in your local
browser, then copy that address and put it into the labextension dashboard
field, then you should be good to go.
…On Thu, Sep 12, 2019 at 8:27 AM Diego Dorgam ***@***.***> wrote:
Hi there,
I've been trying to use dask labextension's jupyter widgets to monitor
Dask processing tasks and etc, but not from a LocalCluster, as it comes
configured by Default, but from a Kubernetes Cluster, created by it's
default Helm Chart
<https://github.com/helm/charts/tree/master/stable/dask>.
I have a JupyterHub environment setted up with this Dask Scheduler+Worker
in a Kubernetes Cluster (of my own) which I use to spread workers over as I
need.
But it seems that the visualizations provided by this extension only
allows me to monitor LocalClusters, when I try to connect to my Kubernetes
Cluster using:
from dask.distributed import Client
client = Client("tcp://dask-scheduler:8786")
client
I'm then able to use normally the Dask workers spread over the Kubernetes
Cluster, but the widgets inside Jupyter lab won't show any data about those
workers.
is it possible to configure the extension widgets to monitor other cluster
rather then the LocalCluster?
tks for any help
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diegodorgam
commented on September 16, 2024
thks @mrocklin , I've been trying to do it but I can't get it.
As I'm running the Dask Scheduler in k8s, I have to execute a port-forward command to access the Dask dashboard, like this:
kubectl port-forward --namespace datascience dask-scheduler-86cf9f746b-2qvbg 8787
Forwarding from 127.0.0.1:8787 -> 8787
Forwarding from [::1]:8787 -> 8787
where dask-scheduler-86cf9f746b-2qvbgis the name of the container in my datascience namespace.
So with this command I can get access to the Dask Dashboard throw http://localhost:8787/status
I've tried all sort of combinations with the dashboard URL, here:
tried:
- dask-scheduler
- dask-scheduler:8787
- dask-scheduler:8787/status
- dask-scheduler:8787/dashboard
and all of them withhttp://andtcp://in front of the address, and still nothing happens.
When I ask for a new cluster, a LocalCluster is created and the URL looks like this:
dask/dashboard/391a9de9-8d8e-4ae6-8e52-151d314603eb
any ideas on how could I find the right URL to put in there?
tks in advance
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diegodorgam
commented on September 16, 2024
took a look at #41 and found out something.
With my port-forward enabled I was able to connect to the dashboard with this link http://localhost:8787 (http is important)
As I watched my browser console I've noticed that Jupyter is trying to reach the URL trough HTTPS request and is getting everything blocked due CORS issues...
So my guess is that I should disable CORS stuff in my browser, or create a valid HTTPS address to my dask dashboard...
thinking about trying the second one..
Thanks
from dask-labextension.
Related Issues (20)
- Dashboard doesn't show when scheduler does not listen on localhost HOT 1
- Update branding
- Open Dashboard programmatically HOT 3
- Specify default address to look for scheduler HOT 13
- Dashboard shows cluster of different user HOT 1
- No visibility (black background) with JupyterLab in Dark theme HOT 3
- Can't connect to local cluster using local IP and port HOT 1
- 5.3.0 does not create a schemas/dask-labextension/plugin.json file on install HOT 11
- Default layout HOT 4
- Need to press the new "Launch in JupyterLab" button twice HOT 1
- Help connecting to existing KubeCluster using the build-in Discovery Mechanism HOT 2
- Using +NEW freezes Jupyterlab completely HOT 3
- JLab 4.0 side-effect: Class 'RenderedCommon' incorrectly implements interface 'IRenderer'. HOT 12
- Logos no longer working well HOT 4
- Conflicting dependencies with jupyterlab=4.0.2 HOT 3
- Inject client code adds an empty cell. HOT 3
- Release HOT 3
- Button Launch Dashboard in Jupyterlab not available HOT 1
- JLab 4-compatible release HOT 5
- Dashboard does not display graphs with bokeh = 3.3.0 HOT 3
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