mindspore.train.TimeMonitor

class mindspore.train.TimeMonitor(data_size=None)

Monitor the time in train or eval process.

Parameters

data_size (int) – How many steps are the intervals between print information each time. if the program get batch_num during training, data_size will be set to batch_num, otherwise data_size will be used. Default: None .

Raises

ValueError – If data_size is not positive int.

Examples

>>> from mindspore import nn
>>> from mindspore.train import Model, TimeMonitor
>>>
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/r2.1/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
>>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
>>> model = Model(net, loss_fn=loss, optimizer=optim)
>>> # Create the dataset taking MNIST as an example. Refer to
>>> # https://gitee.com/mindspore/docs/blob/r2.1/docs/mindspore/code/mnist.py
>>> dataset = create_dataset()
>>> time_monitor = TimeMonitor()
>>> model.train(10, dataset, callbacks=time_monitor)

epoch_begin(run_context)

Record time at the beginning of epoch.

Parameters

run_context (RunContext) – Context of the process running. For more details, please refer to mindspore.train.RunContext.

epoch_end(run_context)

Print process cost time at the end of epoch.

Parameters

run_context (RunContext) – Context of the process running. For more details, please refer to mindspore.train.RunContext.