Well you could start by setting the output_uri to True in Task.init .

I can only say I’ve found ClearML to be very helpful, even given the documentation issue.
I think they’ve been working on upgrading it for a while, hopefully something new comes out soon.
Maybe @<1523701205467926528:profile|AgitatedDove14> has further info 🙂

Sure, for example when reporting HTML files:

Still; anyone? 🥹 @<1523701070390366208:profile|CostlyOstrich36> @<1523701205467926528:profile|AgitatedDove14>

Would be great if it is 😍 We have few files that change frequently and are quite large in size, and it would be quite a storage hit to save all of them

I'll try upgrading to 1.1.5, one moment

I couldn't find it directly in the SDK at least (in the APIClient)... 🤔

We have a read-only user with personal access token for these things, works seamlessly throughout and in our current on premise servers... So perhaps something missing in the autoscaler definitions?

SuccessfulKoala55 could this be related to the monkey patching for logging platform? We have our own logging handlers that we use in this case

Bump?

Woot! What about clearml-agent 1.2.0? 🙂

The results from searching in the "Add Experiment" view (can't resize column widths -> can't see project name ...)

We have a more complicated case but I'll work around it 😄

Follow up though - can configuration objects refer to one-another internally in ClearML?

Last but not least - can I cancel the offline zip creation if I'm not interested in it 🤔
EDIT: I see not, guess one has to patch ZipFile ...

https://clear.ml/docs/latest/docs/references/sdk/services_monitor

Then you can run this as a task, see also this example https://clear.ml/docs/latest/docs/guides/services/slack_alerts

After the task was initialized? 🤔

Thanks LazyFish41 , that explains it 👍

I think you're interested in the Monitor class:)

Using the PipelineController with add_function_step

Thanks David! I appreciate that, it would be very nice to have a consistent pattern in this!

It misses the repository information of course, but the 'configuration/Args' were logged. So something weird in identifying the repository

I'm trying to decide if ClearML is a good use case for my team 🙂
Right now we're not looking for a complete overhaul into new tools, just some enhancements (specifically, model repository, data versioning).

We've been burnt by DVC and the likes before, so I'm trying to minimize the pain for my team before we set out to explore ClearML.

Yeah, and just thinking out loud what I like about the numpy/pandas documentation

Or is just integrated in the ClearML slack space and for some reason it's showing the clearml address then?

We load the endpoint (and S3 credentials) from a .env file, so they're not immediately available at the time of from clearml import Task .
It's a convenience thing, rather than exporting many environment variables that are tied together.

Hi SuccessfulKoala55 !
Could you elaborate on how best to delete these from the database?

Another example - trying to validate dataset interactions ends with

` else:
self._created_task = True
dataset_project, parent_project = self._build_hidden_project_name(dataset_project, dataset_name)
task = Task.create(
project_name=dataset_project, task_name=dataset_name, task_type=Task.TaskTypes.data_processing)
if bool(Session.check_min_api_server_version(Dataset.__min_api_version)):
get_or_create_proje...

If everything is managed with a git repo, does this also mean PRs will have a messy metadata file attached to them?

I think so, it was just missing from the official documentation 🙂 Thanks!

On it! Should I include the additional user filters described above?