mindspore.RunContext
- class mindspore.RunContext(original_args)[source]
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 callingby RunContext.original_args() and add extral attributes to the information, but also can stop the training process by calling request_stop method. For details of custom Callback, please check Callback.
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.
- get_stop_requested()[source]
Return whether a stop is requested or not.
- Returns
bool, if true, model.train() stops iterations.