Yup, i used the value file for the agent. However, i manually edited for the agentservices (as there was no example for it in the github).. Also I am not sure what is the CLEARML_HOST_IP (left it empty)

TimelyPenguin76 : Yup that's what I do now.. However, shld config to use some distributed storage later

Yes, I am already using a Pipeline.

2.  I have another project build using the Pipeline. The pipeline always loads the last commited dataset from the above Dataset project and run few other stuff.

Just not sure, how to make the Pipeline to listen to changes in the Dataset project.

i ran this in my local machine..
clearml-task --project playground --name tensorboard_toy --script tensorboard_toy.py --requirements requirements.txt --queue myqueue

AgitatedDove14 We too self host (on prem) the helm charts in our local k8s ecosystem.
Triggering - Will be nice feature indeed, currently we are using clearml.monitors to address these now
Is it the UI presenting the entire workflow? - This portion will also be nice. (Let's say someone uses a 1) clearmldataset -> 2) Pipeline Controller (Contains preprocessing, training, hyperparamter tuning) -> 3) clearml-serving ).. If they can see the entire thing, in one flow
We are using seldon f...

Hi guys,
I filled up the default_output_ur in the conf file, but it doesnt get reflected in the clearml ui.
Disclaimer : Clearml is setup as a k8s pod using the Helm charts
sdk { development { # Default Task output_uri. if output_uri is not provided to Task.init, default_output_uri will be used instead. default_output_uri: " " } }

Thanks JuicyFox94 .
Not really from devops background, Let me try to digest this.. 🙏

MagnificentSeaurchin79 How to do this ? Can it be done via ClearMl itself ?

sounds like you need to run a service to monitor for new commits in PROJ_1, to trigger the pipeline

nice... we need moarrrrrrrr !!!!!!!!
It wud be really helpful, if you cud do the next episode on setting up clearml in kubernetes.. 😇
In anyways, keep up the good work for the community

Hi, using the pipeline examples, with
step1_dataset_artifact.py, step2_data_processing.py, step3_train_model.py ==> pipeline_controller.pyIn the above example, the pipeline_controller is stringing together 3 python files, instead could it string together 3 containers instead. Of course, we can manually compile each into a docker image, but does clearml has some similar approach baked in.

Hi AgitatedDove14 , Attached my create version compared to init version..
When I enqueue both the init and create version into my clearmlQueue, it seems the create version doesnt execute at all.
It just mentions "2021-05-26 16:02:13,053 - clearml - WARNING - Terminating local execution process" and says it has completed successfully.

let me run the clearml-agent outside the k8 system.. and get back to u

Thanks.

Hi, for the values.yaml, is there some reference for it esp so , if we assign more Memory to webserver service etc. I tried googling around but so far no luck

thanks got it..

Just to add on, I am using minikube now.

` Could not load dynamic library 'libcupti.so.11.0'; dlerror: libcupti.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64
2021-03-11 09:11:17.368793: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcupti.so'; dlerror: libcupti.so: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64
2021-03-11 09...

Ah kk, it is ---laptop:0 worker is no more now.. But wrt to our original qn, I can see the agent(worker) in the clearml-server UI ..

The above screenshot is from my local settings... My agents run in the k8s system (like in a pod)

Yeah, that worked.. As I was the running the agent in a different machine as our deployment of clearml was in k8s.

Something is wierd.. It is showing workers which are not running now...

kkie.. was checking in the forum (if anyone knows anything) before asking them..

kkie.. I have two differenet projects under clearml web server.
First project , stores datasets only.. using clearml-data (PROJ_1) Second project, is a clearml-pipeline project, (PROJ_2) which pulls the latest commited dataset from (PROJ_1) and does few other steps ... Now, I manually start the PROJ_2 when i know the dataset is updated in PROJ_1.

what does a control plane do ? I cant understand this..
Like the serving engine, will get the user input, preprocess, infer it and send back the results..

TimelyPenguin76 :
from clearml import Dataset ds = Dataset.get(dataset_project="PROJ_1", dataset_name="dataset")

kkie, tks.. I will connect with you offline .