Do you mean reordering them with a drag and drop functionality or options for different orderings?
I don't think such a feature exists currently but you could put in a feature request on GitHub 🙂
Hi ComfortableShark77 ,
So if I understand correctly you'd like the values of the configurations hidden when viewing in the UI?
Hi FlutteringWorm14 , what happens if you try to delete those tasks manually?
Hi TroubledHedgehog16 , I don't think there is any specific documentation regarding this. Basically anything that communicates with the server (UI/SDK/Agent) will cause an increase in these calls.
You could do a test on a free account using your resource to see how many calls you would reach in a peak day.
CharmingStarfish14 , maybe SuccessfulKoala55 can assist
It's not a requirement but I guess it really depends on your setup. Do you see any errors in the docker containers? Specifically the API server
Hi @<1673501379764686848:profile|VirtuousSeaturtle4> , what do you mean? Connect to a server someone else set up?
It's a way to execute tasks remotely and even automate the entire process of data pre processing -> training -> output model 🙂
You can read more here:
https://github.com/allegroai/clearml-agent
And what is the issue? You can't access the webUI?
Hi @<1523704667563888640:profile|CooperativeOtter46> , I don't think so. What is your use case?
Can you add a full log of an experiment?
Hi @<1523704667563888640:profile|CooperativeOtter46> , are the agents inside the pods running in docker mode?
What if you point it to the fileserver? Does it still not upload the model?
In the open source you don't have users & groups, user management is done via fixed users - None
What errors are you seeing in the apiserver pod?
Hi TartSeagull57 , are you running a local ClearML server? Did you upgrade it recently or maybe did you change clearml version?
Hi @<1625666182751195136:profile|MysteriousParrot48> , I'm afraid that this looks like a pure ElasticSearch issue, I'd suggest checking on ES forums for help on this
I think you can periodically upload them to s3, I think the StorageManager would help with that. Do consider that artifacts are logged in the system with links (each artifact is a link in the end) So even if you upload it to and s3 bucket in the backend there will be a link leading to the file-server so you would have to amend this somehow.
Why not upload specific checkpoints directly to s3 if they're extra heavy?
Hi @<1535069219354316800:profile|PerplexedRaccoon19> , the agent will try to use the relevant python version according to what the experiment ran on originally. In general, it's best to run inside dockers with a docker image specified per experiment 🙂
Hi @<1535069219354316800:profile|PerplexedRaccoon19> , I think this is what you're looking for 🙂
None
From my understanding, they are 🙂
Think of it this way. You have the pipeline controller which is the 'special' task that manages the logic. Then you have the pipeline steps. Both the controller and the steps need some agent to execute them. So you need an agent to execute the controller and also you need another agent to run the steps themselves.
I would suggest by clicking on 'task_one' and going into full details. My guess it is in 'enqueued' state probably to the 'default' queue.
This is the env variable you're looking for - CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL
Hi @<1702492411105644544:profile|YummyGrasshopper29> , I suggest doing it via the webUI with developer tools open so you can see what the webUI sends to the backend and then copy that.
wdyt?
I see. Can you please elaborate on your use case a bit? What are you trying to achieve? Are the servers supposed to be persistent until aborted?
Might make life easier 🙂