Hi SubstantialElk6

32 CPU cores, 64GB ram

Should be plenty, this sounds like network bottle neck issue, I can't imagine the server is actually CPU bounded

an implementation of this kind is interesting for you or do you suggest to fork

You mean adding a config map storing a default trains.conf for the agent?

Hi PanickyMoth78 an RC with a fix is out, let me know if it works (notice you can now set the max_workers from CLI or Dataset functions) pip install clearml==1.8.1rc1

PanickyMoth78 quick update the fix is already being tested, I'm hoping an RC tomorrow 🙂

If the manual execution (i.e. pycharm) was working it should have stored it on the Pipeline Task.

its should logged all in the end as I understand

Hmm let me check the code for a minute

Are you saying that in the UI you do not see "confusion matrix" at all, only on the GS bucket ?

My pleasure

No that's okay

yes its the JWT issue

AstonishingRabbit13 so is it working now ?

Yep the automagic only kick in with Task.init... The main difference and the advantage of using a Dataset object is the underlying Task resides in a specific structure that is used when searching based on project/name/version, but other than that, it should just work

Could I use "register artifact"

I think this is somewhat deprecated and we should probably replace it with something similar to what you mentioned (i.e. watch a file change).
Right now the easiest way would e to manually upload the trainer_state.json every checkpoint:
Task.current_task().upload_artifact('trainer_state.json, name='state') `

I think your use case is the original idea behind "use_current_task" option, it was basically designed to connect code that creates the Dataset together with the dataset itself.
I think the only caveat in the current implementation is that it should "move" the current Task into the dataset project / set the name. wdyt?

PanickyMoth78

LockException: [Errno 11] Resource temporarily unavailable

I'm not sure I understand how you got to this error (obviously creating datasets and getting them back works), what is unique in the setup/flow itself ?

However, this one should be a feature to work on, and should be fairly easy to implement.

Feel free to add as GitHub issue 🙂
Main challenge is understanding what needs to be added as "uncommitted changes"

Awesome ! thank you so much!
1.0.2 will be out in an hour

Debug samples can only be controlled via api.file_server (or programatically)
Model/Artifacts see above

This has no effect. I am not able to change the files_sever, e.g. I can not change from

You are Not changing the files_server just where your Taskj uploads Models/Artifacts, these are two diff things (and again Only applies to Artifacts/Models)

I just think that the create function should expect

dataset_name

to be None in the case of

use_current_task=True

(or allow the dataset name to differ from the task name)

I think you are correct, at least we should output a warning that it is ignored ... I'll make sure we do 🙂

I meant even just a link to a blank comparison and one can then add the experiments from that view

Just making sure you are aware, once you are in comparison you can always add Tasks (any Task):
Notice you can press on the "Add experiments", then select Any experiment (including all projects! as filters)
Notice you need to remove all filters (right side red x on the filter Icon)

Hi @<1661180197757521920:profile|GiddyShrimp15>
I think the is a better channel for this kind of question
(they will be able to help with that)

I have a task where I create a dataset but I also create a set of matplotlib figures, some numeric statistics and a pandas table that describe the data which I wish to have associated with the dataset and vieawable from the clearml web page for the dataset.

Oh sure, use https://clear.ml/docs/latest/docs/references/sdk/dataset#get_logger they will be visible on the Dataset page on the version in question

🤞

If the only issue is this line
task.execute_remotely(..., exit_process=True)It has to finish the static analysis of the entire repository (which usually happens in the background but now we have to wait for it). If the repo is large this could actually take 20sec (depending on CPU/drive of the machine itself)

Hurrah Hurrah

Hi ScaryLeopard77
Could that be solved with this PR?
https://github.com/allegroai/clearml/pull/548

You can always click on the name of the series and remove it for display.
Why would you need three graphs?

Let me try to add some color to this process analysis process.
Basically clearml will try to statically analyze the code (i.e. look for import/from packages)
Then it will list them in a pip requirements.txt format under installed packages.
When running inside conda environment, it will check which packages were installed via "conda install" (instead of pip install) and mark them internally. This process ensures that when the clearml-agent is running with conda package manager, it "knows" whic...

no worries

Thanks VexedCat68 !
This is a great example, maybe PR it to the cleamrl-servvng repo ? wdyt?