ctx: Context object

In mlverse/luz: Higher Level 'API' for 'torch'

Context object

Description

Context objects used in luz to share information between model methods, metrics and callbacks.

Details

The ctx object is used in luz to share information between the training loop and callbacks, model methods, and metrics. The table below describes information available in the ctx by default. Other callbacks could potentially modify these attributes or add new ones.

Attribute	Description
verbose	The value (TRUE or FALSE) attributed to the verbose argument in fit .
accelerator	Accelerator object used to query the correct device to place models, data, etc. It assumes the value passed to the accelerator parameter in fit.
model	Initialized nn_module object that will be trained during the fit procedure.
optimizers	A named list of optimizers used during training.
data	The currently in-use dataloader. When training it’s ctx$train_data, when doing validation its ctx$valid_data. It can also be the prediction dataset when in predict.
train_data	Dataloader passed to the data argument in fit. Modified to yield data in the selected device.
valid_data	Dataloader passed to the valid_data argument in fit. Modified to yield data in the selected device.
min_epochs	Minimum number of epochs the model will be trained for.
max_epochs	Maximum number of epochs the model will be trained for.
epoch	Current training epoch.
iter	Current training iteration. It’s reset every epoch and when going from training to validation.
training	Whether the model is in training or validation mode. See also help("luz_callback_train_valid")
callbacks	List of callbacks that will be called during the training procedure. It’s the union of the list passed to the callbacks parameter and the default callbacks.
step	Closure that will be used to do one step of the model. It’s used for both training and validation. Takes no argument, but can access the ctx object.
call_callbacks	Call callbacks by name. For example call_callbacks("on_train_begin") will call all callbacks that provide methods for this point.
batch	Last batch obtained by the dataloader. A batch is a list() with 2 elements, one that is used as input and the other as target.
input	First element of the last batch obtained by the current dataloader.
target	Second element of the last batch obtained by the current dataloader.
pred	Last predictions obtained by ctx$model$forward . Note: can be potentially modified by previously ran callbacks. Also note that this might not be available if you used a custom training step.
loss_fn	The active loss function that will be minimized during training.
loss	Last computed loss from the model. Note: this might not be available if you modified the training or validation step.
opt	Current optimizer, ie. the optimizer that will be used to do the next step to update parameters.
opt_nm	Current optimizer name. By default it’s opt , but can change if your model uses more than one optimizer depending on the set of parameters being optimized.
metrics	list() with current metric objects that are updated at every on_train_batch_end() or on_valid_batch_end(). See also help("luz_callback_metrics")
records	list() recording metric values for training and validation for each epoch. See also help("luz_callback_metrics") . Also records profiling metrics. See help("luz_callback_profile") for more information.
handlers	A named list() of handlers that is passed to rlang::with_handlers() during the training loop and can be used to handle errors or conditions that might be raised by other callbacks.
epoch_handlers	A named list of handlers that is used with rlang::with_handlers(). Those handlers are used inside the epochs loop, thus you can handle epoch specific conditions, that won’t necessarily end training.

Context attributes

See Also

Context object: context

mlverse/luz documentation built on Sept. 19, 2024, 11:20 p.m.