Pytorch Lightning Tensorboard Logger Across Multiple Models
I'm relatively new to Lightning and Loggers vs manually tracking metrics.  I am trying to train two distinct models and have their accuracy and loss plotted on the same charts in tensorboard (or any other logger) within Colab.
What I have right now is basically:
trainer1 = pl.Trainer(gpus=n_gpus, max_epochs=n_epochs, progress_bar_refresh_rate=20, num_sanity_val_steps=0)

trainer2 = pl.Trainer(gpus=n_gpus, max_epochs=n_epochs, progress_bar_refresh_rate=20, num_sanity_val_steps=0)

trainer1.fit(Model1, train_loader, val_loader)
trainer2.fit(Model2, train_loader, val_loader)

#Then later:

%load_ext tensorboard

%tensorboard --logdir lightning_logs/

What I'd like to see at this point are those logged metrics charted together on the same chart, any help would be appreciated.  I've spent some time trying to toy with this but I'm a bit out of my depth on this, thank you!

The exact chart used for logging a specific metric depends on the key name you provide in the .log() call (its a feature that Lightning inherits from TensorBoard itself)
def validation_step(self, batch, _):
  #  This string decides which chart to use in the TB web interface
  #         vvvvvvvvv
  self.log('valid_acc', acc)

Just use the same string for both .log() calls and have both runs saved in same directory.
logger = TensorBoardLogger(save_dir='lightning_logs/', name='model1')
logger = TensorBoardLogger(save_dir='lightning_logs/', name='model2')

If you run tesnsorboard --logdir ./lightning_logs pointing at the parent directory, you should be able to see both metric in the same chart with the key named valid_acc.
