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

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

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

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

CostlyOstrich36 maybe cause sagemaker is running in headless mode

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()

still*

the task is stil running

and when i’m running it in sagemaker

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

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?

Name: clearml
Version: 1.9.1

^ self hosted CostlyOstrich36

Hi ScantMoth28 , what version of ClearML are you using? Are you using a self hosted server or the community one?