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

screenshot

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

thanks

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

yes i do use plt.imshow

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