Hi, for both of them,  args.lastiter  is the exact same value. But when plotted out, they are 2 actually iterations apart.

Thanks TimelyPenguin76 , let me try it out now.

Hi TimelyPenguin76 , i am adding a debug sample to an existing task using the above method. What should i put for the iteration? I do not want to overwrite existing ones but i do not know what's the last count. This is for both scalar and media reporting.

It didn't work as expected.
` task init
task report iter 10

task init
task report iter 10

The second task pushed the reporting iteration to 20 instead. `

Hi SubstantialElk6 , you can use task.get_last_iteration() and use it, what do you think?

Hi,
basically i run this block first and ended the script.
task = Task.init(project_name="afro-nmt", task_name=args.taskname, continue_last_task=args.taskid) Logger.current_logger().report_scalar(title="BLEU",series="JW300",value=args.jwbleu, iteration=args.lastiter)Then i run another script, with series different.
task = Task.init(project_name="afro-nmt", task_name=args.taskname, continue_last_task=args.taskid) Logger.current_logger().report_scalar(title="BLEU",series="SS900",value=args.ssbleu, iteration=args.lastiter)

SubstantialElk6 if you call Task.init with continue_last_task=<task_id> it will automatically add the last_iteration of the previous run, to any logging/report so you never overwrite the previous reports 🙂

I think you could use Clone task

So you mean args.lastiter is not the last iteration? Can you try replacing it with task.get_last_iteration() ?

Just to put a ping for those on this side of the timezone to look at. Thanks.

Didnt get that, can you detail a bit?

Hi SubstantialElk6

If you like a new task, you can clone as HugePelican43 suggested.
You can also continue reporting to your task with continue_last_task parameter in your Task.init call:

from clearml import Task task = Task.init(project_name="YOUR PROJECT NAME", task_name="YOUR TASK NAME", continue_last_task=True)You also can specify the task id to continue (from the docs - https://allegro.ai/clearml/docs/rst/references/clearml_python_ref/task_module/task_task.html?highlight=continue_last_task ):

continue_last_task ( bool ) –
Continue the execution of a previously executed Task (experiment)
Note
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.

The values are:
True - Continue the the last Task ID. specified explicitly by reuse_last_task_id or implicitly with the same logic as reuse_last_task_id False - Overwrite the execution of previous Task (default). A string - You can also specify a Task ID (string) to be continued. This is equivalent to continue_last_task=True and reuse_last_task_id=a_task_id_string.