i will attempt to start that now.

the project wasn't hidden before. I'm aware of the pipeline tasks being hidden, that makes sense for organization. but the actual project itself as an entirety has a ghost icon.

she created a new project and started working in there, it was visible in the UI... and just now it disappeared again. it's kind of like running the pipeline makes it disappear.

one note is that it happened after I tried deploying a set of workers to a new queue, which she tried to use to run the tasks in parallel instead of our default queue which is only serviced by one worker (a container i built)

I opened github.com/allegroai/clearml/pull/1083 as an attempt to help catch this.

i think we may have found the frankenbug?

the argument to the dataset name was not being overridden correctly (mistyped), so the default value of an empty string (instead of a placeholder like "CHANGE_ME") in the parent task caused the dataset to basically get created with an empty name, and somehow that hid the whole project, despite hundreds of existing tasks in it.

and no way to un-hide it as far as I can tell?

ah, thank you for the clarity. A quarterly release schedule makes sense, it's about what I've observed.
Let me know if I can be of any assistance in early testing!

tasks that create pipelines feels like a hack and i found they dont show up in the UI (have to use the link in the console).

I've found that sometimes i need to right click "Run" a couple of times before the parameters are filled in properly.

namely, I'm very interested in testing this unmerged feature, will be trying to leverage it as soon as possible
None

I believe pipe.connect_configuration is what you're looking for?

Yup if you scroll through the logs in the console, near the top (post config dump), you’ll see a git clone and checkout to the specific hash.

PS You can actually change this parameter in an experiment’s configuration if it is in draft mode.

my approach was to spin up an EC2 and run the deployment there from within the EBS volume mount.

I symlinked /opt/clearml to /mnt/xvda/clearml to minimize docker-compose changes. been working out fine so far.

with aws-cdk, the deployment steps can be automated (format the volume, clone a repo with the config, etc). I can link you to a resource that may help with that if you're interested.

then back to CLI, updated the pipeline to point the tasks to the new queue. run it, shows up in the UI (same container as default worker, just replicated w a new docker-compose and CMD to point to the new queue).

you can put task.execute_remotely() to create it in draft mode. I've taken to configuring defaults to run things very quickly just in case i forget though (e.g. placeholder string for dataset, bail out early if not changed… or just do one epoch on a small subset of samples, etc).

@<1541954607595393024:profile|BattyCrocodile47> put together None

and now its gone.

the dataset, task, and pipeline were under the same project name. i'm seeing what happens if the dataset project name was different ( f"{project_name}_data" ). which project would get deleted... the dataset one or the project of the task that kicked it off?

and the answer is...
the project is preserved, the dataset's project hidden.

so ... empty dataset names due to a small typo in parameter override + the choice for the dataset to have the same project name as the task that created it (...

didnt disappear this time.

maybe an important note: I mounted the same cache directory for the agents.

so when the task completed successfully (changed the queue to default and let it finish instead of aborting), the project disappeared.

credentials for the server to do things with s3 will be in /opt/clearml/apiserver.conf.

waiting now to see if they disappear.
any problems you may have spotted with the versions used?

project hasn't disappeared just yet. but it's happened twice now

yeah let's step through this, i'm having her execute these steps as we speak.

create a task with the new project name. its created as a draft. can see it in the UI under the new project.

pipeline script is updated with new project name for. execute script to create pipeline. now see in UI under this new project name. nothing hidden.

the pipeline is running. when the queue is default (only serviced by one container with agent in it ( clearml-agent==1.5.2 ). abort it. everything is still ...

the clearml github, search for a file named cleanup service dot py (or something to that effect)

I think you’d have to run the cleanup service. That’s what seems to be what is controlling deletion based on archived status and some other temporal filters

probably, but the syntax would be in that of a git diff, so it’d be a touch clunky if you asked me
Are you trying to avoid local development?

If you can hit the endpoint with curl, you for sure can hook it up to many frontend frameworks.

Personal recs: gradio, streamlit

Abstract the interaction into a function call, and wrap it all in some UI elements using python.

Weird . I recently implemented a function that talked to this exact endpoint and found it had to exclude the version and api paths . Is there some sort of redirect that happens?

For reproducibility, it kind of makes sense though. The existence of the file is contingent on the worker cloning the source code. I'm sure things can be done to maintain state differently but I personally adapted to the git-based workflow for managing files pretty quickly.

though yes I will admit I had the same thought first: why must I run it each time?

Beware: squash merges will ruin the ability to reproduce the experiment at that time since the git commit will be lost (presuming th...

thank you!
I'll add a volume mount to the services-agent container, and from what I understand that will become the template it uses?

is this the structure of the file?
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or is it the "dot" syntax (like what shows up in the console when the task executes / your snippet)?

Might be under examples