and when i’m running it in sagemaker

although i wish it would use the credentials in my clearml.conf file automatically

The only difference that i notice is that when i run it locally the task is completed

still*

the task is stil running

Hi @<1523701717097517056:profile|ScantMoth28> , what version of ClearML are you using? Are you using a self hosted server or the community one?

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from clearml import Task

# Connecting ClearML with the current process,
# from here on everything is logged automatically
# Create a new task, disable automatic matplotlib connect
task = Task.init(project_name='Something', task_name='Something')

# Create plot and explicitly report as figure
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = (30 * np.random.rand(N))**2  # 0 to 15 point radii
plt.scatter(x, y, s=area, c=colors, alpha=0.5)
plt.show()

Name: clearml
Version: 1.9.1

@<1523701070390366208:profile|CostlyOstrich36> yes it indeed shows up when running it locally

going to double check that rn

And if you run the same code locally everything is reported correctly?

^ self hosted @<1523701070390366208:profile|CostlyOstrich36>

%env CLEARML_WEB_HOST={-----}
%env CLEARML_API_HOST={-----}
%env CLEARML_FILES_HOST=undefined
%env CLEARML_API_ACCESS_KEY={-----}
%env CLEARML_API_SECRET_KEY={-----}

@<1523701070390366208:profile|CostlyOstrich36> maybe cause sagemaker is running in headless mode

^ it works if i do that and use the jupyter credentials from my work space settings.

but none the less I’ll continue with this work around thanks!