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

Version: 0.17.4

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

Tested with clearml 1.1.3 and I could not reproduce the issue 👍

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?

btw the same happens when I try this on localhost

# ClearML SDK configuration file api { # Notice: 'host' is the api server (default port 8008), not the web server. api_server: web_server: files_server: # Credentials are generated using the webapp, # Override with os environment: CLEARML_API_ACCESS_KEY / CLEARML_API_SECRET_KEY

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

Started clearml server again and now everything seems to work fine

` # 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 tried it but it doesnot work. I checked the clearml-agent-services container and it does have the right CLEARML_WEB_HOST
echo $CLEARML_WEB_HOST
http://192.168.4.1:8080
however, the messages on slack still point to apiserver:8080

👍

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

it is the same with rc4. Under the variables tab it keeps hanging on 'collecting data...' OS: Ubuntu 18.04, PyCharm CE 2020.3

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?

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

I am using gitlab, I can create an access token. From the gitlab page:
"Personal Access Tokens
You can generate a personal access token for each application you use that needs access to the GitLab API."

However, now I have an access token, not an username/password. Is there also an option to authenticate with the access token?

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

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

👍

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

Thanks ClearML team, is there an example of the metric snapshot plot in the project overview UI available in the demo dashboard?

using auto_connect_frameworks={'pytorch': False} now

It is the folder the clearml creates and the folder we create ourself to store the predictions

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...

Yes that is the easiest solution, I will create the main task when the others are closed

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

Ubuntu 18.04 and python 3.6. the subprocess is done by subclassing multiprocessing.Process and then calling the .start() method

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

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

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>?