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

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

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

ok will give it a try and let you know

thanks AgitatedDove14 for the links.. seems like i might try first one if it works out .. before going the route to create a full framework support as in our case team uses multiple different frameworks

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

allegroai/trains-agent-service image hash 03dc85869afe

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

thanks

i guess i was not so clear may be.. say e.g. you running lightgbm model training, by default it will take all the cpus available on the box and will run that many threads, now another task got scheduled on the same box now you have 2x threads with same amount of CPU to schedule on. So yes the jobs will progress but the progression will not be the same due to context switches which will happen way more than say if we have allowed on 1/2x threads for each job

yeah i still see it.. but that seems to be due to dns address being blocked by our datacenter

yes i do use plt.imshow

with this layout.. it didn't work earlier

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

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

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

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

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

as if its couple of weeks away.. i can wait

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

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

as when it run first time after .trains/venv-build cleaning, it output this message for this package - pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1593275161868/work wondering why it specify this package as for most other packages it just prints the version number

you replied it already.. it was execute_remotely called with exit_true argument

AgitatedDove14 Morning... so what should the value of "upload_uri" to set to, fileserver_url e.g. http://localhost:8081 ?

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

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).

yes delete experiments which are old or for some other reason are not required to keep around

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 ?

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