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

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

With a name of 98 characters errors like 'munmap_chunk(): invalid pointer',
'double free or corruption (!prev)' or 'free(): invalid next size (normal) occur later in my script. But maybe this is not related to clearml but to the filesystem. Just wondering if there was a maximum length from the clearml side

nice, will give it a try

I created it with clearml-init, nothing special. It looks like

# 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
credentials {"access_key": "NMWOE5C3RGTX473M9D3M", "secret_key": "L@G50jO+TJ23#8Eerp1E$4y=elUt11P!BL...

I see task.get_output_destination() returns a url like http://localhost:8081 . Is it possible to get the folder with the artifacts/models?

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

Thanks !

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

see None

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

Yes other then the link it generates it works fine

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

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

Is there a way to test this? It seems my git user and token are correct. I can do git clone https://<NAME_TOKEN >:<ACCESSTOKEN> gitlab.com/mycompany/repo.git

However when starting the service it fails with:

cloning: git@gitlab.com:mycompany/repo.git
Using user/pass credentials - replacing ssh url

:mycompany/repo.git' with https url '

'
2021-01-18 20:04:08
User aborted: stopping task (3)

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

how do I install the RC?

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

self-hosted

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

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>?

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

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...

and None

` # 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...

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

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?

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

Yes, now I new unique folder is created per experiment where the predictions are saved. That works. The only thing is that now there is the folder that clearml makes for an experiment and the folder that saves the resuts. So two folders with artifacts per experiment. I was wondering if there was a more efficient solution and if it could be combined.

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