nanoleafapi VS microk8s

#requires numpy, pillow #eoPython - https://github.com/tk421storm/eo-python #nanoleafapi - https://github.com/MylesMor/nanoleafapi # # tested in python 3.8 on Win 11 # from urllib.request import urlretrieve from tempfile import gettempdir from os.path import join, splitext, dirname, realpath from uuid import uuid4 from pprint import pprint from time import sleep from eopython import ElectricAccount import numpy as np from nanoleafapi import Nanoleaf, WHITE from PIL import Image nanoleafIP= REPLACE_ME nanoleafAuthToken= REPLACE_ME effect_data = { "command": "add", "version":"2.0", "animName": "EO2", "animType": "plugin", "colorType": "HSB", "pluginUuid": 'ba632d3e-9c2b-4413-a965-510c839b3f71', #https://forum.nanoleaf.me/docs/openapi#_rwyy54qdnrv6 "pluginType": "color", "animData": None, "palette": [ # { # "hue": 0, being a number between 0-360 # "saturation": 100, being a number between 0-100 # "brightness": 100, being a number between 0-100 # }, ], "pluginOptions": [ { "name": "transTime", "value": 100 }, # { useful for some plugins, but doesnt effect currently selected (random) # "name": "linDirection", # "value": "right" # }, { "name": "loop", "value": True }, { "name": "nColorsPerFrame", "value": 2 } ], "brightnessRange": {'minValue':100, 'maxValue':100}, "loop": True } # # thanks to unutbu at https://stackoverflow.com/questions/18801218/build-a-color-palette-from-image-url # def palette(img): """ Return palette in descending order of frequency """ arr = np.asarray(img) palette, index = np.unique(asvoid(arr).ravel(), return_inverse=True) palette = palette.view(arr.dtype).reshape(-1, arr.shape[-1]) count = np.bincount(index) order = np.argsort(count) return palette[order[::-1]] def asvoid(arr): """View the array as dtype np.void (bytes) This collapses ND-arrays to 1D-arrays, so you can perform 1D operations on them. http://stackoverflow.com/a/16216866/190597 (Jaime) http://stackoverflow.com/a/16840350/190597 (Jaime) Warning: >>> asvoid([-0.]) == asvoid([0.]) array([False], dtype=bool) """ arr = np.ascontiguousarray(arr) return arr.view(np.dtype((np.void, arr.dtype.itemsize * arr.shape[-1]))) #log in eoAccount=ElectricAccount(REPLACE_ME_USERNAME, REPLACE_ME_PASSWORD) #get device (from a list, assuming the first) eo2=eoAccount.devices[0] #get the current artwork id currentID=eo2.current_artwork_id() currentURL=eo2.current_artwork_preview() extension=splitext(currentURL.split("?")[0])[1] tempFile=join(gettempdir(), uuid4().hex+extension) print('downloading artwork to '+tempFile) urlretrieve(currentURL, tempFile) img = Image.open(tempFile, 'r').convert('HSV') palette=palette(img) print('found palette with '+str(len(palette))+' colors') #filter found colors based on saturation so we're only getting colors, not white/blacks colorsDesired=10 saturationThreshold=25 luminanceThreshold=50 hueMinDiff=40 filteredPalette=[] storedHues=[] for color in palette: hue=color[0] sat=color[1] val=color[2] if sat>saturationThreshold and val>luminanceThreshold: #also include minimum hue difference check (so we dont end up with a palette of all one color) diffEnough=True for value in storedHues: if hue<=(value+hueMinDiff) and hue>=(value-hueMinDiff): diffEnough=False break if diffEnough: filteredPalette.append(color) storedHues.append(color[0]) if len(filteredPalette)>=colorsDesired: break print('filtered palette to '+str(len(filteredPalette))+' colors') print(filteredPalette) #make effect for nanoleaf for color in filteredPalette: effect_data['palette'].append({'hue':int(color[0]/256)*360), 'saturation':int((color[1]/256)*100), 'brightness':int((color[2]/256)*100)}) effect_name="EO2"#_"+str(currentID) effect_data["animName"]=effect_name #connect to nanoleaf and display effect nano=Nanoleaf(nanoleafIP, nanoleafAuthToken) nano.set_color(WHITE) #response=requests.put(nano.url+"/effects", data = json.dumps({"command":"delete","animName":"EO2"})) #pprint(response.text) pprint(effect_data) nano.write_effect(effect_data) nano.set_effect(effect_name)

FROM postgres:11CMD sleep 30 && postgres I'm reusing this example from the same issue with microk8s where I could solve it by changing the kubelet and kubeapi-server configuration files (see https://github.com/ubuntu/microk8s/issues/770 in case you're interested). I assume this is not possible with GKE clusters as they don't expose these files, probably for good reasons.

$ kubectl get podsNAME READY STATUS RESTARTS AGEbackend-deployment-66cff7d4c6-gwbzf 0/1 ImagePullBackOff 0 18s Before that it was ErrImagePull. So, my question is, how to tell it to use local docker images? Somewhere on the internet I read that I need to build images using microk8s.docker but it seems to be removed.

There's a whole community of hobbyists building Raspberry Pi clusters, porting things to work on various Arm processors, exploring and contributing to minimalist distros like k0s and microk8s, etc.

same pvc worked fine on "GKE" (Google Kubernetes Engine) but failing in my local cluster using microk8s

Local Kubernetes Clusters: It is now possible to run Kubernetes on local machines with tools such as Kubernetes in Docker (kind), minikube or MicroK8s. This allows developers to run their first experiments completely isolated from others and with low risk and low cost.

Local Kubernetes clusters are clusters that are running on the individual computer of the developer. There are many tools that provide such an environment, such as Minikube, microk8s, k3s, or kind. While they are not all the same, their use as a development environment is quite comparable.

Since the developer is the only one who has to access this cluster for development, local clusters can be a feasible solution for this purpose. Over time, several solutions have emerged that are particularly made for running Kubernetes in local environments. The most important ones are Kubernetes in Docker (kind), MicroK8s, minikube and k3s. For a comparison of these local Kubernetes options, you can look at this post.

Another very important distinction of this level is that developers have direct access to Kubernetes for the first time. While it is not strictly necessary, the standard case for the Kubernetes access with these tools is to use a local Kubernetes cluster, i.e. a Kubernetes cluster started with tools such as minikube, kind or MicroK8s on the local computer of the developer.