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
] (default:None
)) – Number of passes through the dataset. If None, defaults to np.min([round((20000 / n_cells) * 400), 400])use_gpu (
Union
[str
,int
,bool
,None
] (default:None
)) – 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
(default:0.9
)) – Size of training set in the range [0.0, 1.0].validation_size (
Optional
[float
] (default:None
)) – Size of the test set. If None, defaults to 1 - train_size. If train_size + validation_size < 1, the remaining cells belong to a test set.batch_size (
int
(default:128
)) – Minibatch size to use during training. If None, no minibatching occurs and all data is copied to device (e.g., GPU).early_stopping (
bool
(default:False
)) – Perform early stopping. Additional arguments can be passed in **kwargs. SeeTrainer
for further options.lr (
Optional
[float
] (default:None
)) – Optimiser learning rate (default optimiser isClippedAdam
). Specifying optimiser via plan_kwargs overrides this choice of lr.training_plan (
PyroTrainingPlan
(default:<class 'scvi.train._trainingplans.PyroTrainingPlan'>
)) – Training planPyroTrainingPlan
.plan_kwargs (
Optional
[dict
] (default:None
)) – Keyword args forPyroTrainingPlan
. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.**trainer_kwargs – Other keyword args for
Trainer
.