Comments (8)
Hi, sorry you're experiencing this issue. Could you paste the search space you are trying out?
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I also tried the default with just:
tuner.fit(adata, metric="validation_loss")
param_space = {
'n_hidden': {
'distribution': 'choice',
'choices': [64, 128, 256, 512],
},
'n_latent': {
'distribution': 'choice',
'choices': [5, 10, 20, 30],
},
'n_layers': {
'distribution': 'choice',
'choices': [1, 2, 3],
},
'dropout_rate': {
'distribution': 'uniform',
'low': 0.0,
'high': 0.5,
},
'gene_likelihood': {
'distribution': 'choice',
'choices': ['zinb', 'nb'],
}
}
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I am also a little confused because in the model.history there is no validation loss even after successful training. Only reconstruction_loss_train.
(only shows up when early_stopping = True)
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I checked earlier versions of scvi-tools:
1.0.4 did not work
0.20.3 does work
from scvi-tools.
It's possible that this is due to a new release of Ray. Could you pass in param_space
formatted using the tune search API:
from ray import tune
param_space = {
'n_hidden': tune.choice([64, 128, 256, 512]),
'n_latent': tune.choice([5, 10, 20, 30]),
'n_layers': tune.choice([1, 2, 3]),
... # other params
}
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Regarding validation loss, our default is set to not run validation steps. This can be enabled with passing in check_val_every_n_epoch=1
to the train method.
from scvi-tools.
Ok, great. That fixed it. I was a little confused because the default search_space was returning the same error. Thanks!
from scvi-tools.
Yeah we format the default search space a little differently because of the way we parse it internally. That might have been a bit confusing, sorry.
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