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

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

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

👍

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

yep

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

Thanks !

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?

Is there also a method to get all the project names?

This code snippet does reproduce:
` import os
from clearml import Task

parameters = {
'experiment': {
'project_name': 'test',
'experiment_name': 'test_exp',
'tags': []
}
}

Task.set_offline(True)
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='testing',
tags=parameters['experiment...

👍

how do I install the RC?

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

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

This is the full log

Some of the experiments are done on a GCP instance instead of the local server on which we also run ClearML. The experiments running on GCP report to the same local clearml server, but the IP address for clearml configured on the GCP instance is different (and then forwarded). Is this the problem?

I restarted the cleanup service. Now I get some messages like this:

2021-07-16 12:39:46,736 - clearml.storage - ERROR - Failed creating storage object file:// Reason: 'NoneType' object has no attribute 'replace'
2021-07-16 12:39:46,736 - clearml.Task - ERROR - Failed deleting None: 'NoneType' object has no attribute 'delete'
WARNING:root:Could not delete Task ID=eb11c92928af477e9e732d0cad47a57e, sequence item 0: expected str instance, NoneType found

any idea?

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

Updated docker-compose from 1.17 to 1.24.1 and using the latest docker-compose.yml solves the issue, thanks

I see, however it looks like medical images are not supported. We use nifty images, except for an 3D array the image also contains voxel spacing, and origin and direction in a world frame

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

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

Okay finalize works. I was looking here: https://github.com/allegroai/clearml/blob/master/docs/datasets.md

nice, will give it a try

Thanks, that was it

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

I dont see that option in my ~/clearml.conf?

using auto_connect_frameworks={'pytorch': False} now