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

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

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

seems like setting to fileserver_url did the trick

couldn't find the licensing price for enterprise version

is it because of something wrong with this package build from their owner or something else

i can not check the working directory today due to vpn issues in accessing server but script path was -m test.scripts it was missing script from it

allegroai/trains image hash f038c8c6652d

i ran it this week

i am simply proxying it using ssh port forwarding

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

thanks for letting me know.. but it turns out after i have recreated my whole system environment from scratch, trains agent is working as expected..

so i was expecting that uploaded model will be for example LightGBM.1104445eca4749f89962669200481397/models/Model%20object/model.pkl

any example in the repo which i can go through

ok so controller task is a simple place holder which run infinitely and fetch a task template and queue it..

my use case is more like 1st one where run the training at a certain given schedule

test package is not installed but its in the current working directory

trains-agent version as mentioned is 0.16.1 and server is 0.16.1 as well

AgitatedDove14 it seems i am having issues when i restart the agent... it fails in creating/setting up the env again... when i clean up the .trains/venv-builds folder and run a job for agent.. it is able to create the env fine and run job successfully.. when i restart the agent it fails with messages like
` Requirement already satisfied: cffi@ file:///home/conda/feedstock_root/build_artifacts/cffi_1595805535531/work from file:///home/conda/feedstock_root/build_artifacts/cffi_1595805535...

that seems like a bit of extra thing a user needs to bother about.. better deployment model should be that its part of api-server deployment and configurable from UI itself.. may be i am asking too much 😛

TimelyPenguin76 is there any way to do this using UI directly or as a schedule... otherwise i think i will run the cleanup_service as given in docs...

i think for now it should do the trick... was just thinking about the roadmap part

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

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

an example achieving what i propose would be greatly helpful

this looks good... also do you have any info/eta on next controller/service release you mentioning

in the above example task id is from a newly generated task like Task.init() ?

thanks for your help AgitatedDove14

ok will report back