mindspore.train.RunContext

class mindspore.train.RunContext(original_args)

Hold and manage information about the model.

RunContext is mainly used to collect context-related information about the model during training or eval and pass it into the Callback object as an input parameter to share information.

Callback objects not only can obtain the Model context information by calling by RunContext.original_args() and add extra attributes to the information, but also can stop the training process by calling request_stop method. For details of custom Callback, please check Callback Mechanism.

RunContext.original_args() holds the model context information as a dictionary variable, and different attributes of the dictionary are stored in training or eval process. Details are as follows:

Attributes supported in train	Attributes supported in eval	meaning
train_network		train network with optimizer and loss
epoch_num		Number of train epochs
train_dataset		the train dataset
loss_fn		the loss function
optimizer		the optimizer
parallel_mode		the parallel mode
device_number		the device number
train_dataset_element		the train data element of current step
last_save_ckpt_step		the last step num of save ckpt
latest_ckpt_file		the ckpt file
cur_epoch_num		number of current epoch
	eval_network	the evaluate network
	valid_dataset	the valid dataset
	metrics	the evaluate metrics
mode	mode	“train” or “eval”
batch_num	batch_num	the train/eval batch number
list_callback	list_callback	callback list
network	network	basic network
cur_step_num	cur_step_num	the train/eval step number
dataset_sink_mode	dataset_sink_mode	the train/eval sink mode
net_outputs	net_outputs	network output results

Parameters

original_args (dict) – Holding the related information of model.

Examples

>>> from mindspore import Tensor
>>> from mindspore.train import RunContext
>>> cb_params = {}
>>> cb_params["cur_epoch_num"] = 4
>>> cb_params["epoch_num"] = 4
>>> cb_params["cur_step_num"] = 2
>>> cb_params["batch_num"] = 2
>>> cb_params["net_outputs"] = Tensor(2.0)
>>> run_context = RunContext(cb_params)
>>> whether_stop = run_context.get_stop_requested()

get_stop_requested()

Return whether a stop is requested or not.

Returns

bool, if true, model.train() stops iterations.

original_args()

Get the _original_args object.

Returns

Dict, an object that holds the original arguments of model.

Tutorial Examples:

• Callback Mechanism - Customized Callback Mechanism

request_stop()

Set stop requirement during training or eval.

Callbacks can use this function to request stop of iterations. model.train() checks whether this is called or not.

Tutorial Examples:

• Callback Mechanism - Customized Training Termination Time