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 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 ?

so just under models dir rather than artifact... any way to achieve this or i should just treat it as artifact ?

seems like setting to fileserver_url did the trick

it still tries to create a new env

while you guys gonna work on it.. just a small feature addition to it.. it would be cool to have a DAG figure which shows how models are linked under this task and ability to just click a circle in that DAG figure to navigate to given task... i think it will be very useful UX 🙂

look forward to the new job workflow part in 0.16 then 🙂

yeah that would solve it i think.. so what is the normal cadence for release.. every month or quarter ?

i mean linking more in UI.. as when i go to model detail page, i can see that a given experiment created this model and click on that to see its detail... so something similar to that for ensemble models

i understand.. its just if i have a docker image with correct env.. i would prefer if trains-agent can use that directly

an example achieving what i propose would be greatly helpful

allegroai/trains-agent-service image hash 03dc85869afe

as i am seeing now my plots but they are lending into metrics section not plot section.

once i removed the seaborn plot then CM plots becomes visible again

TimelyPenguin76 also is there any reason for trating show and imshow differently

or is there any plan to fix it in upcoming release

not just fairness but the scheduled workloads will be starved of resources if say someone run training which by default take all the available cpus

seems like if i remove the plt.figure(figsize=(16, 8)) i start to see the figure title but not figure itself

i know its not magic... all linux subsystem underneath.. just to configure it in a way as needed 🙂 for now i think i will stick with current setup of cpu-only mode and co-ordinate with in the team. later one when need comes .. will see if we go for k8s or not

i could use k8s but thats bit overkill currently

its not that they are blank.. whole page is blank including plotly plots

whereas i am using simple matplotlib now

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).

ok... is there any way to enforce using a given system wide env.. so agent doesn't need to spend time with env. creation