Does any exit code appear? What is the status message and status reason in the 'INFO' section?

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?)

I'm not sure how to even troubleshoot this.

Yes it shows on the UI and has the first epoch for some of the metrics but that's it. It has run like 50 epochs, it says it is still running but there are no updates to the scalars or debug samples

Then we also connect two dictionaries for configs

    task.connect(model_config)
    task.connect(DataAugConfig)

The machine currently having the issue is on tensorboard==2.16.2

STATUS MESSAGE: N/A
STATUS REASON: Signal None

So I was able to repeat the same behavior on a machine running this example None

by adding the following callback

class TensorBoardImage(TensorBoard):
    @staticmethod
    def make_image(tensor):
        from PIL import Image
        import io
        tensor = np.stack((tensor, tensor, tensor), axis=2)
        height, width, channels = tensor.shape
        image = Image.fromarray(tensor)
        output = io.BytesIO()
        image.save(output, format='PNG')
        image_string = output.getvalue()
        output.close()
        return tf.Summary.Image(height=height,
                                width=width,
                                colorspace=channels,
                                encoded_image_string=image_string)

    def on_epoch_end(self, epoch, logs=None):
        if logs is None:
            logs = {}
        super(TensorBoardImage, self).on_epoch_end(epoch, logs)
        images = self.validation_data[0]  # 0 - data; 1 - labels
        img = (255 * images[0].reshape(28, 28)).astype('uint8')

        image = self.make_image(img)
        summary = tf.Summary(value=[tf.Summary.Value(tag='image', image=image)])
        self.writer.add_summary(summary, epoch)

So it seems like there is some bug in the how ClearML is logging tensorbaord images that causes everything to fail

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?

Can you try with auto_connect_streams=True ? Also, what version of clearml sdk are you using?

Not sure if this is helpful but this is what I get when I cntrl-c out of the hung script

^C^CException ignored in atexit callback: <bound method Reporter._handle_program_exit of <clearml.backend_interface.metrics.reporter.Reporter object at 0x70fd8b7ff1c0>>
Event reporting sub-process lost, switching to thread based reporting
Traceback (most recent call last):
  File "/home/richard/.virtualenvs/temp_clearml/lib/python3.10/site-packages/clearml/backend_interface/metrics/reporter.py", line 317, in _handle_program_exit
    self.wait_for_events()
  File "/home/richard/.virtualenvs/temp_clearml/lib/python3.10/site-packages/clearml/backend_interface/metrics/reporter.py", line 337, in wait_for_events
    return report_service.wait_for_events(timeout=timeout)
  File "/home/richard/.virtualenvs/temp_clearml/lib/python3.10/site-packages/clearml/backend_interface/metrics/reporter.py", line 129, in wait_for_events
    if self._empty_state_event.wait(timeout=1.0):
  File "/home/richard/.virtualenvs/temp_clearml/lib/python3.10/site-packages/clearml/utilities/process/mp.py", line 445, in wait
    return self._event.wait(timeout=timeout)
  File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 349, in wait
    self._cond.wait(timeout)
  File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 261, in wait
    return self._wait_semaphore.acquire(True, timeout)
KeyboardInterrupt: 

I just created a new virtual environment and the problem persists. There are only two dependencies clearml and tensorflow. @<1523701070390366208:profile|CostlyOstrich36> what logs are you referring to?

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.

Console output and also what you get on the ClearML task page under the console section

Thank you @<1719524641879363584:profile|ThankfulClams64> for opening the GI, hopefully we will be able to reproduce it and fox ot quickly

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)

It was working for me. Anyway I modified the callback. Attached is the script that has the issue for me whenever I add random_image_logger to the callbacks It only logs some of the scalars for 1 epoch. It then is stuck and never recovers. When I remove random_image_logger the scalars are correctly logged. Again this only on 1 computer, other computers we have logging work perfectly fine

sometimes I get no scalars, but the console logging always seems to be working

@<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

Yes it is logging to the console. The script does hang whenever it completes all the epochs when it is having the issue.

Console logs

When the script is hung at the end the experiment says failed in ClearML

I'm not sure if it still reports logs. But it will continue running on the machine

I found that setting store_uncommitted_code_diff: false instead of true seems to fix the issue

Not sure why that is related to saving images

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?

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?

The same training works sometimes. But I'm not sure how to troubleshoot when it stops logging the metrics