And if you run the same code locally everything is reported correctly?
@<1523701070390366208:profile|CostlyOstrich36> yes it indeed shows up when running it locally
going to double check that rn
The only difference that i notice is that when i run it locally the task is completed
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()
and when i’m running it in sagemaker
the task is stil running
^ it works if i do that and use the jupyter credentials from my work space settings.
although i wish it would use the credentials in my clearml.conf file automatically
Hi @<1523701717097517056:profile|ScantMoth28> , what version of ClearML are you using? Are you using a self hosted server or the community one?
^ self hosted @<1523701070390366208:profile|CostlyOstrich36>
but none the less I’ll continue with this work around thanks!
Name: clearml Version: 1.9.1
@<1523701070390366208:profile|CostlyOstrich36> maybe cause sagemaker is running in headless mode