But I do not know how it can help me:(
In your code itself after the Task.init
call add:task.set_initial_iteration(0)
See reply here:
https://github.com/allegroai/clearml/issues/496#issuecomment-980037382
Okay I think I know what's going on (there is a race that for some reason on CoLab acts differently).
As a quick hack you can do the following:Task._report_subprocess_enabled = False task = Task.init(...) task.set_initial_iteration(0)
And it works correctly when running on my computer, and if I use colab, then for some reason it has no effect.
I think I'm lost on this one, when running in colab, is this continuing a previous experiment ?
Yey! okay let me make sure we add this feature to the Task.init arguments so one can control it from code 🙂
I can't think of any actual difference in flow ...
Can you try the following?task._setup_reporter() task.set_initial_iteration(0)
I'm so happy to see that this problem has been finally solved!
I get gaps in the graphs.
For example, the first time I run, I create a task and run a loop:
Hi SourOx12
Is this related to this one?
https://github.com/allegroai/clearml/issues/496
When I work through Colab, when I continue experimenting, I get gaps in the graphs.
For example, the first time I run, I create a task and run a loop:for i in range(1,100):
clearml.Logger.current_logger().report_scalar("test", "loss", iteration=i, value=i)
Then, on the second run, I continue the task via continue_last_task and reuse_last_task_id and write task.set_initial_iteration(0). Then I start the cycle:for i in range(100,200):
clearml.Logger.current_logger().report_scalar("test", "loss", iteration=i, value=i)
And then on the graphs I get a gap.
Can you give a small snippet to play with? Just to understand, when you run on local machine everything works fine? What do you do with Google Colab?
Hmm, it seems as if the task.set_initial_iteration(0) is ignored...
What's the clearml version you are using ?
Is it the same one you have on the local machine ?