thanks

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 🙂

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

ok will report back

i know it support conda.. but i have another system wide env which is not base .. say ml so wondering if i can comnfigure trains-agent to use that... not standard practice but just asking if it is possible

trains is run using docker-compose allegroai/trains-agent-services:latest and allegroai/trains:latest

allegroai/trains image hash f038c8c6652d

i am simply proxying it using ssh port forwarding

will report back

screenshot

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

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

any example in the repo which i can go through

with this layout.. it didn't work earlier

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

thanks for the update... it seems currently i can not pass the http/s proxy parameters as when agent creates a new env and try to download some package its being blocked by our corp firewall... all outgoing connection needs to pass through a proxy.. so is it possible to specify that or environment variables to agent

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..

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...

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

seems like port forwarding had an issue.. fixed that.. now running test again to see if things workout as expected

this is when executed from directly with task.init()

i ran it this week

there are multiple scripts under test/scripts folder.. example is running one script from that folder

i don't need this right away.. i just wanted to know the possibility fo dividing the current machine into multiple workers... i guess if its not readily available then may be you guys can discuss to see if it makes sense to have it on roadmap..

thanks for your help AgitatedDove14

couldn't find the licensing price for enterprise version

0.15.1-367

thanks

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

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