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84 × Eureka!AgitatedDove14 no it doesn't work
it still tries to create a new env
whereas i am using simple matplotlib now
ok will report back
i am simply proxying it using ssh port forwarding
also one thing i noticed.. when i report confusion matrix and some other plots e.g. seaborn with matplotlib.. on server side i can the plots are there but not visible at all
trains is run using docker-compose allegroai/trains-agent-services:latest
and allegroai/trains:latest
this is when executed from directly with task.init()
i ran it this week
Cross-Origin Request Blocked: The Same Origin Policy disallows reading the remote resource at
http://localhost:8081/Trains%20Test/LightGBM.56ca0c9c9ebf4800b7e4f537295d942c/metrics/LightGBM%20Feature%20Importance%20above%200.0%20threshold/plot%20image/LightGBM%20Feature%20Importance%20above%200.0%20threshold_plot%20image_00000000.png . (Reason: CORS request did not succeed).
seems like CORS issue in the console logs
AgitatedDove14 when using OutputModel(task, name='LightGBM model', framework='LightGBM').update_weights(f"{args.out}/model.pkl")
i am seeing this in the logs No output storage destination defined, registering local model /tmp/model.pkl
when i got to trains UI.. i see the model name and details but when i try to download it point to the path file:///tmp/model.pkl
which is incorrect wondering how to fix it
looking at the code https://github.com/allegroai/trains/blob/65a4aa7aa90fc867993cf0d5e36c214e6c044270/trains/model.py#L1146 this happens when storage_uri is not defined where as i have this under trains.conf
so task should have it ?
AgitatedDove14 it seems uploading artifact and uploading models are two different things when it comes to treating fileserver... as when i upload artifact it works as expected but when uploading model using outputmodel class, it wants output_uri
path.. wondering how can i as it to store it under the fileserver
like artifacts LightGBM.1104445eca4749f89962669200481397/artifacts/Model%20object/model.pkl
looking at the above link, it seems i might be able to create it with some boilerplate as it has concept of parent and child... but not sure how status checks and dependency get sorted out
yeah i still see it.. but that seems to be due to dns address being blocked by our datacenter
as i am seeing now my plots but they are lending into metrics section not plot section.
so as you say.. i don't think the issue i am seeing is due to this error
any logs i can check or debug my side
ok will give it a try and let you know
simply changing to show
doesn't work in my case as i am displaying CM.. what about if i use matshow
it may be that i am new to trains but in my normal notebook flow they both are images and i as trains user expected it to be under the Plot
section as i think this is an image.. as in nutshell all matplotlib plots display data as an image 🙂
TimelyPenguin76 also is there any reason for trating show
and imshow
differently
an example achieving what i propose would be greatly helpful
its not that they are blank.. whole page is blank including plotly plots
my use case is more like 1st one where run the training at a certain given schedule
TimelyPenguin76 yeah when i run matplotlib with show
plots does land under Plot
section... so its mainly then the imshow
part.. i am wondering why the distinction and what is the usual way to emit plots to debug samples