Logging Metrics using MLFLowlogger to display on MLFlow UI
What is your question?
I am trying to implement the mlflow logger into my code to log my metrics (train_loss, val loss) and for some reason my code is not working. I was able to log the hyperparams, yet still having trouble with the metrics. Also the run id stored for the mlruns is different that the run id generated by the logger.
Code
    tqdm_dict = {'train_loss': loss.item() or 0.0}
    output = OrderedDict({
        'loss': loss,
        'progress_bar': tqdm_dict,
        'log': tqdm_dict
    })

           
    return output

What have you tried?
#print(inspect.getargspec(self.logger.experiment.set_tag))
print(mlflow.start_run(run_name=self.logger.run_uuid, nested=True))
print(self.logger.experiment.set_tag(run_id= self.logger._run_id, key='train_loss', value=loss))
#run__id = self.logger.experiment.get_run(self.logger._run_id)
#for key, value in vars(output):
#   self.logger.experiment.log_metric(key, value)
#self.logger.log_metrics(output.items()) #generates tensor error
in addition to trying to get the same run ID generated by the mlrun.
print(self.logger._run_id)
print(self.logger.run_id)
both generate the same run id which is not what I am looking for
What's your environment?
Virtual Box on windows 10: Ubuntu 18.04 LTS

conda version (no venv)
conda 4.7.12
PyTorch version
1.0.2
Lightning version
0.5.3.2
Test-tube version