We are running the same code on multiple machines and it just randomly happens. Currently we are having the issue on 1 out of 4
My bad, if you set auto_connect_streams to false, you basically disable the console logging... Please see the documentation:
auto_connect_streams (Union[bool, Mapping[str, bool]]) – Control the automatic logging of stdout and stderr.
@<1719524641879363584:profile|ThankfulClams64> you could try using the compare function in the UI to compare the experiments on the machine the scalars are not reported properly and the experiments on a machine that runs the experiments properly. I suggest then replicating the environment exactly on the problematic machine. None
@<1719524641879363584:profile|ThankfulClams64> , can you provide a small code snippet that reproduces this behaviour? Can you also test with the latest version of clearml ?
It seems similar to this None is it possible saving too many model weights causes metric logging thread to die?
That makes sense... If you turn auto_connect_streams to false this mean that auto reporting will be disabled as per the documentation.. If you turn it to True then logging should resume.
Yes tensorboard. It is still logging the tensorboard scalers and images. It just doesn't log the console output
The same training works sometimes. But I'm not sure how to troubleshoot when it stops logging the metrics
It is still getting stuck. I think the issue might have something to do with the iterations versus epochs. I notice that one of the scalars that gets logged early is logging the epoch while the remaining scalars seem to be iterations because the iteration value is 1355 instead of 26
I am still having this issue. An update is that the "abort" does not work. Even though the state is correctly tracked in ClearML when I try to abort the experiment through the UI it says it does it but the experiment remains running on the computer.
Okay I will do another run to capture the console output. We currently set auto_connect_streams to False to reduce the number of API calls. So there isn't really anything in the ClearML task page console section
Is there someway to kill all connections of a machine to the ClearML server this does seem to be related to restarting a task / running a new task quickly after a task fails or is aborted
I am on 1.16.2
task = Task.init(project_name=model_config['ClearML']['project_name'],
task_name=model_config['ClearML']['task_name'],
continue_last_task=False,
auto_connect_streams=True)
Hi @<1719524641879363584:profile|ThankfulClams64> ,the logging is done by a separate process, I'm pretty sure it's not terminating all of the sudden. Did you manage to get a full log of such an experiment to share?
Thanks @<1719524641879363584:profile|ThankfulClams64> having a code that can reproduce it is exactly what we need.
One thing I might have missed and is very important , what is your tensorboard package version?
So even if you abort it on the start of the experiment it will keep running and reporting logs?
Any chance you have some uncommited code changes that, when not included, this works fine?
Not sure why that is related to saving images
It is not always reproducible it seems like something that we do not understand happens then the machine consistently has this issue. We believe it has something to do with stopping and starting experiments
STATUS MESSAGE: N/A
STATUS REASON: Signal None
Hi @<1719524641879363584:profile|ThankfulClams64>
I am using ClearML Pro and pretty regularly I will restart an experiment and nothing will get logged to ClearML.
I use ClearML with pytorch 1.7.1, pytorch-lightning 1.2.2 and Tensorboard auto
All ClearML has the latest stable updates. (clearml 1.7.4, clearml-agent 1.7.2)
Is this still happening with the latest clearml ( clearml==1.16.3rc2 ) ?
What is the TB version?
I remember a fix regrading lightining support
Also just making sure, are you using the default lightning TB logger ?
How are you initializing the Task.init (i.e. could you copy here the code?)
task.connect(model_config)
task.connect(DataAugConfig)
If these are separate dictionaries , you should probably use two sections:
task.connect(model_config, name="model config")
task.connect(DataAugConfig, name="data aug")
It is still getting stuck.
I notice that one of the scalars that gets logged early is logging the epoch while the remaining scalars seem to be iterations because the iteration value is 1355 instead of 26
wait so you are seeing Some scalars ?
while the remaining scalars seem to be iterations because the iteration value is 1355 instead of 26
what are you seeing in your TB?
So I am only seeing values for the first epoch. It seems like it does not track all of them so maybe something is happening when it tries to log scalars.
I have seen it only log iterations but setting task.set_initial_iteration(0) seemed to fix that so it now seems to be logging the correct epoch
Tensorboard is correct and works. I have never seen an issue in the tensorboard logs
Then we also connect two dictionaries for configs
task.connect(model_config)
task.connect(DataAugConfig)
Hi @<1719524641879363584:profile|ThankfulClams64> , does the experiment itself show on the ClearML UI?
Just to make sure, did the logging to the clearml server work previously and stoped working at some point?
Hi we are currently having the issue. There is nothing in the console regarding ClearML besides
ClearML Task: created new task id=0174d5b9d7164f47bd10484fd268e3ff
======> WARNING! Git diff too large to store (3611kb), skipping uncommitted changes <======
ClearML results page:
The console logs continue to come in put no scalers or debug images show up.
Yea I am fine not having the console logging. My issues is the scalers and debug images occasionally don't record to ClearML