Nested in the UI is not possible I think? I mean that it is possible to start the subtask while the main task is still active. Maybe I should just try

Okay, so I have to first save the generated image somewhere and then with logger.report_media it is copied to the folder?

yep

upgrading to? 2020.3.3 is the latest version? https://www.jetbrains.com/pycharm/download/other.html

It is for storing the predictions a trained model makes, so two different models do create slightly different images

I see, will it be possible in the future to directly write custom/not supported formats to the folder? Because we are working with very big files, having them stored at multiple locations is something we try to avoid

I see this indeed when I create a new project with an empty description. Is this also possible for older project created before clearml 1.0? For these projects this button is not there

using auto_connect_frameworks={'pytorch': False} now

Yes I see:

"The default location for output models and other artifacts. If True is passed, the default files_server will be used for model storage. In the default location, ClearML creates a subfolder for the output. The subfolder structure is the following: <output destination name> / <project name> / <task name>.<Task ID>"

So it makes a folder in the output destination <project_name>/<task name>.<Task ID>. It is not possible to specify the full output destination right?

Thanks !

No problems with this example, I will try to create a reproducible example

how do I install the RC?

👍

` # Never save to clearml demo server
Task.set_offline(parameters['experiment'].get('offline', False))
if not Task.is_offline():
os.environ['CLEARML_NO_DEFAULT_SERVER'] = '1'
task = Task.init(
project_name=parameters['experiment']['project_name'],
task_name=parameters['experiment']['experiment_name'],
task_type=task_type,
tags=parameters['experiment']['tags'],
auto_connect_arg_parser=True,
auto_connect_streams=True,
auto_connect_frameworks=True,
auto_resour...

I enqueue to service to the services queue, not done anything myself with agents

Old legacy code that has its own folder structure per experiment. I can also do it the other way around. Does task.get_output_destination() return the folder including project name and <task_name>.<task_id>?

and None

I was looking for all the metric names, similar as what you get when clicking the '+ metric' in customize columns. But turns out I will implement it in a different way, not needed anymore

Okay, I am working with medical images. And when running a testing script I want to save the predictions (also big medical images of another modality). What happens when I do logger.upload_artifact(..). Then a file is copied to this folder?

Yes that is the error I get when trying to launch a custom slack alert service (when not running it locally)

nice, will give it a try

Okay, but for example slack alerts do not need git access I assume?

Started clearml server again and now everything seems to work fine

Yes, I add these metrics as extra columns and then I sort them. I want to know which experiments performs best in daylight for example or which during night. Therefore I think a is not the right choice

This is the full log

Yes, it did I think that makes sense

yes, I wanted the confirmation that this is also a good solution for datasets with medical images

This was with using one task in a multiprocessing.pool and the next one in the main process. I switched to have all tasks in a separate process via ProcessPoolExecutor and now it runs fine 👍 (version 0.17.5)

And is there an easy way to get all the metrics associated with a project?

Thanks, that was it