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I Am Using Clearml Pro And Pretty Regularly I Will Restart An Experiment And Nothing Will Get Logged To Clearml. It Shows The Experiment Running (For Days) And It'S Running Fine On The Pc But No Scalers Or Debug Samples Are Shown. How Do We Troubleshoot T

I am using ClearML Pro and pretty regularly I will restart an experiment and nothing will get logged to ClearML. It shows the experiment running (for days) and it's running fine on the PC but no scalers or debug samples are shown.
How do we troubleshoot this?

  
  
Posted 2 months ago
Votes Newest

Answers 69


Just to make sure, did the logging to the clearml server work previously and stoped working at some point?

  
  
Posted one month ago

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)
  
  
Posted one month ago

@<1719524641879363584:profile|ThankfulClams64> , if you set auto_connect_streams to false nothing will be reported from your frameworks. With what frameworks are you working, tensorboard?

  
  
Posted one month ago

Correct, so I get something like this

ClearML Task: created new task id=6ec57dcb007545aebc4ec51eb5b34c67
======> WARNING! Git diff too large to store (2536kb), skipping uncommitted changes <======
ClearML results page: 

but that is all

  
  
Posted one month ago

Can someone help with this?

  
  
Posted one month ago

@<1719524641879363584:profile|ThankfulClams64> , can you provide a small code snippet that reproduces this behaviour? Can you also test with the latest version of clearml ?

  
  
Posted one month ago

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

  
  
Posted one month ago

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

  
  
Posted one month ago

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

  
  
Posted one month ago
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