scvi.model.base.PyroSviTrainMixin#
- class scvi.model.base.PyroSviTrainMixin[source]#
Mixin class for training Pyro models.
Training using minibatches and using full data (copies data to GPU only once).
Methods table#
|
Train the model. |
Methods#
train
- PyroSviTrainMixin.train(max_epochs=None, use_gpu=None, train_size=0.9, validation_size=None, batch_size=128, early_stopping=False, lr=None, training_plan=<class 'scvi.train._trainingplans.PyroTrainingPlan'>, plan_kwargs=None, **trainer_kwargs)[source]#
Train the model.
- Parameters:
max_epochs (Optional[int]) – Number of passes through the dataset. If
None
, defaults tonp.min([round((20000 / n_cells) * 400), 400])
use_gpu (Optional[Union[str, int, bool]]) – Use default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str, e.g.,
'cuda:0'
), or use CPU (if False).train_size (float) – Size of training set in the range [0.0, 1.0].
validation_size (Optional[float]) – Size of the test set. If
None
, defaults to 1 -train_size
. Iftrain_size + validation_size < 1
, the remaining cells belong to a test set.batch_size (int) – Minibatch size to use during training. If
None
, no minibatching occurs and all data is copied to device (e.g., GPU).early_stopping (bool) – Perform early stopping. Additional arguments can be passed in
**kwargs
. SeeTrainer
for further options.lr (Optional[float]) – Optimiser learning rate (default optimiser is
ClippedAdam
). Specifying optimiser via plan_kwargs overrides this choice of lr.training_plan (PyroTrainingPlan) – Training plan
PyroTrainingPlan
.plan_kwargs (Optional[dict]) – Keyword args for
PyroTrainingPlan
. Keyword arguments passed totrain()
will overwrite values present inplan_kwargs
, when appropriate.**trainer_kwargs – Other keyword args for
Trainer
.