Hi SwankyCrab22 ,

Regarding Task.init() did you try passing docker_bash_setup_script and it didn't work? Because according to the docs it should be available with Task.init() as well. Also, after the Task.init() you can use the following method:
https://clear.ml/docs/latest/docs/references/sdk/task#set_base_docker
to also add a docker_setup_bash_script in it's args.

Regarding running the script after the repository is downloaded - I'm not sure. But certainly...

Hi SteepDeer88 , I think this is the second case. Each artifact URL is simply saved as a string in the DB.

I think you can write a very short migration script to rectify this directly on MongoDB OR manipulate it via the API using tasks.edit endpoint

None

Here is an example for auto cleaning. Did you delete ALL experiments?

Hi @<1579280543999070208:profile|SourFly7> , this index holds scalars of some experiments. You can reduce it by deleting some experiments. Do you have any other large scalar indices?

Can you please paste the response from events.debug_images ?

Looping in @<1523703436166565888:profile|DeterminedCrab71> & @<1523701435869433856:profile|SmugDolphin23> for visibility

You'd have to change the URLs in elastic itself

Anything in Elastic? Can you add logs of the startup of the apiserver?

Hi @<1693795218895147008:profile|FlatMole2> , is it possible that the apiserver.conf file isn't persistent and somehow changes?

Also when in this view, open developer tools (F12) and see what calls you get back for debug samples

YummyLion54 , please try the following:

` from clearml import Task

base_task = Task.get_task(task_id="base task id")
cloned_task = Task.clone(source_task=base_task)
cloned_task.connect_configuration(name="OmegaConf", configuration="path/to/conf/file") `

YummyLion54 hi!
are you referring to PARAMETERS  section OR to the  CONFIGURATION OBJECTS

That's an interesting question. I'm pretty sure file deltas aren't saved (Although you do get file sizes so you might see changes there)
Let me check if maybe they are saved somehow or if that information can be extrapolated somehow 🙂

Hi @<1559349204206227456:profile|BeefyStarfish55> , you would need to integrate ClearML with k8s for that.
I think this helm chart is what you're looking for
None

Hi @<1547028031053238272:profile|MassiveGoldfish6> , are you self hosted or on the community server? What project is this, a pipelines/dataset project or just some regular project?

Looks like it's not running in docker mode 🙂
Otherwise you'd have the 'docker run' command at the sttart

For example:
task 613b77be5dac4f6f9eaea7962bf4e034 pulled from eb1c9d9c680d4bdea2dbf5cf90e54af2 by worker worker-bruce:3 Running task '613b77be5dac4f6f9eaea7962bf4e034' Storing stdout and stderr log to '/tmp/.clearml_agent_out._sox_04u.txt', '/tmp/.clearml_agent_out._sox_04u.txt'

Also, in the Scalers section you can see the machine statistics to maybe get an idea. If the memory usage is high this might be the issue. If not then we can cancel out this hypothesis (probably)

did you setup agent.git_pass & agent.git_user in clearml.conf ?

PanickyMoth78 , if I'm not mistaken that should be the mechanism. I'll look into that 🙂

TartSeagull57 , I couldn't make the sample you gave me work 😞

Can you please provide a self contained example that would reproduce the issue?

ScaryBluewhale66 , Hi 🙂
Regarding your questions
I think you can just reset the task and enqueue it You can stop it either in the UI or programmatically I'm guessing the scheduler would show errors in it's log if for some reason it failed at something for some reason

Hi @<1529995795791613952:profile|NervousRabbit2> , if you're running in docker mode you can easily pass it in the docker_args parameter for example so you can set env variables with -e docker arg

https://clear.ml/docs/latest/docs/references/sdk/model_model#id
This can help 🙂

Hi ShinyRabbit94 ,
Do you get any error when you attempt to delete the Tasks?

Hi @<1523702932069945344:profile|CheerfulGorilla72> , regarding 1 - Do you mean the name of the worker? 2 I don't think this exists but you can very easily extract this via the API

ResponsiveHedgehong88 , do you have an option to log into the machine and see the state or if there were any errors? Is there any chance it's running out of memory? The agent also keeps a local log, can you take a look there to see if there is any discrepancy?

When you generate new credentials in the GUI, it comes up with a section to copy and paste into either

clearml-init

or

~/clearml.conf

. I want the files server displayed here to be a GCP address

Regarding this - I think you should open a github feature request since there is currently no way to do this via UI

Hi @<1523701868901961728:profile|ReassuredTiger98> , in the end the container mounts to some volumes on the original machine so it has to be somewhere. Are you basically asking about 'cleaning' the data once a session finishes?

'CLEARML_CONFIG_FILE': '/home/ubuntu/clearml.conf'