How to save model weights to mlflow tracking server while using MLFLogger to save metrics.
❓ Questions and Help
Anybody knows simple way to accomplish it?
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What is your question?
I'm searching for a way to save model weights to mlflow tracking server while using MLFLogger to save metrics.
my problem is, I cannot find a way to save model weight to same run which was created inside  MLFLogger.
When I run mlflow.pytorch.log_model() after trainer.fit(),  metrics and model weight are saved to different run.
Code
  
    mlf_logger = MLFlowLogger(
        experiment_name="WatchNetExperiment",
    )

    trainer = pl.Trainer(gpus=hparams.gpus, distributed_backend='dp', min_nb_epochs=hparams.min_epochs, max_nb_epochs=hparams.max_epochs, logger=mlf_logger)
    trainer.fit(model)
    mlflow.pytorch.log_model(model.model, "my_model")

What have you tried?
To work around the problem, I stopped to use MLFLogger and modify my training code to save metrics at train_step() and validation_end.
What's your environment?

OS: Linux
Packaging conda
Version 0.5.3.2