Hi NastyOtter17 , I'm not sure I understand - can you explain what you see in the UI after running this as opposed to what you expect to see?

Sure!
Before:
{'model': {'accuracy': {'name': 'accuracy', 'x': [0, 1], 'y': [0.5789473652839661, 1.0]}}
After:
{'model': {'accuracy': {'name': 'accuracy', 'x': [0, 1, 2 ], 'y': [0.5789473652839661, 1.0, 2.0 ]}}
Expected:
{'model': {'accuracy': {'name': 'accuracy', 'x': [ 0, 1], 'y': [ 2.0 , 1.0]}}

NastyOtter17 from Task.init() 's docstring regarding continue_last_task :
` continue_last_task (bool ) – Continue the execution of a previously executed Task (experiment)

When continuing the executing of a previously executed Task, all previous artifacts / models/ logs are intact. New logs will continue iteration/step based on the previous-execution maximum iteration value. For example: The last train/loss scalar reported was iteration 100, the next report will be iteration 101. `

This seems to be in line with what you see

Indeed, does what stated in the docu, however I think its a bit odd, as .report_scalar() works quite different in this case compared to the normal case and iteration is not an optional param but will be ignored anyway