mindspore.train.CheckpointConfig

class mindspore.train.CheckpointConfig(save_checkpoint_steps=1, save_checkpoint_seconds=0, keep_checkpoint_max=5, keep_checkpoint_per_n_minutes=0, integrated_save=True, async_save=False, saved_network=None, append_info=None, enc_key=None, enc_mode='AES-GCM', exception_save=False, crc_check=False, remove_redundancy=False, format='ckpt', **kwargs)[source]

The configuration of model checkpoint.

Note

During the training process, if dataset is transmitted through the data channel, it is suggested to set 'save_checkpoint_steps' to an integer multiple of loop_size. Otherwise, the time to save the checkpoint may be biased. It is recommended to set only one save strategy and one keep strategy at the same time. If both save_checkpoint_steps and save_checkpoint_seconds are set, save_checkpoint_seconds will be invalid. If both keep_checkpoint_max and keep_checkpoint_per_n_minutes are set, keep_checkpoint_per_n_minutes will be invalid.
The enc_mode and crc_check parameters are mutually exclusive and cannot be configured simultaneously.

Parameters

save_checkpoint_steps (int) – Steps to save checkpoint. Default: 1 .
save_checkpoint_seconds (int) – Seconds to save checkpoint. Can't be used with save_checkpoint_steps at the same time. Default: 0 .
keep_checkpoint_max (int) – Maximum number of checkpoint files can be saved. Default: 5 .
keep_checkpoint_per_n_minutes (int) – Save the checkpoint file every keep_checkpoint_per_n_minutes minutes. Can't be used with keep_checkpoint_max at the same time. Default: 0 .
integrated_save (bool) – Whether to merge and save the split Tensor in the automatic parallel scenario. Integrated save function is only supported in automatic parallel scene, not supported in manual parallel. Default: True .
async_save (bool) – Whether asynchronous execution saves the checkpoint to a file. Default: False .
saved_network (Cell) – Network to be saved in checkpoint file. If the saved_network has no relation with the network in training, the initial value of saved_network will be saved. Default: None .
append_info (list) – The information save to checkpoint file. Support "epoch_num", "step_num" and dict. The key of dict must be str, the value of dict must be one of int, float, bool, Parameter or Tensor. Default: None .
enc_key (Union[None, bytes]) – Byte type key used for encryption. If the value is None, the encryption is not required. Default: None .
enc_mode (str) – This parameter is valid only when enc_key is not set to None. Specifies the encryption mode, currently supports 'AES-GCM', 'AES-CBC' and 'SM4-CBC'. Default: 'AES-GCM' .
exception_save (bool) – Whether to save the current checkpoint when an exception occurs. Default: False .
crc_check (bool) – Whether to perform crc32 calculation when saving checkpoint and save the calculation result to the end of ckpt. Default: False .
remove_redundancy (bool) – Whether to enable saving the checkpoint with redundancy removal. Redundancy removal refers to eliminating redundant data in data parallelism mode. Default: False , means redundant-free saving is not enabled.
format (str) – Format of the output file, can be "ckpt" or "safetensors". Default: "ckpt".
kwargs (dict) – Configuration options dictionary.

Raises

ValueError – If input parameter is not the correct type.

Examples

>>> from mindspore import nn
>>> from mindspore.train import Model, CheckpointConfig, ModelCheckpoint
>>>
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/master/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/master/docs/mindspore/code/mnist.py
>>> dataset = create_dataset()
>>> config = CheckpointConfig(save_checkpoint_seconds=100, keep_checkpoint_per_n_minutes=5, saved_network=net)
>>> config.save_checkpoint_steps
1
>>> config.save_checkpoint_seconds
>>> config.keep_checkpoint_max
5
>>> config.keep_checkpoint_per_n_minutes
>>> config.integrated_save
True
>>> config.async_save
False
>>> config.saved_network
>>> config.enc_key
>>> config.enc_mode
'AES-GCM'
>>> config.append_dict
>>> config.get_checkpoint_policy
>>> ckpoint_cb = ModelCheckpoint(prefix='LeNet5', directory='./checkpoint', config=config)
>>> model.train(10, dataset, callbacks=ckpoint_cb)

property append_dict

Get the value of information dict saved to checkpoint file.

Returns: dict, the information saved to checkpoint file.

property async_save

Get the value of whether asynchronous execution saves the checkpoint to a file.

Returns: bool, whether asynchronous execution saves the checkpoint to a file.

property crc_check

Get the value of the whether to enable crc check.

Returns: bool, whether to enable crc check.

property enc_key

Get the value of byte type key used for encryption.

Returns: (None, bytes), byte type key used for encryption.

property enc_mode

Get the value of the encryption mode.

Returns: str, encryption mode.

get_checkpoint_policy()[source]

Get the policy of checkpoint.

Returns: dict, the information of checkpoint policy.

property integrated_save

Get the value of whether to merge and save the split Tensor in the automatic parallel scenario.

Returns: bool, whether to merge and save the split Tensor in the automatic parallel scenario.

property keep_checkpoint_max

Get the value of maximum number of checkpoint files can be saved.

Returns: int, Maximum number of checkpoint files can be saved.

property keep_checkpoint_per_n_minutes

Get the value of save the checkpoint file every n minutes.

Returns: Int, save the checkpoint file every n minutes.

property map_param_inc

Get the value of whether to save map Parameter incrementally.

Returns: bool, whether to save map Parameter incrementally.

property save_checkpoint_seconds

Get the value of _save_checkpoint_seconds.

Returns: int, seconds to save the checkpoint file.

property save_checkpoint_steps

Get the value of steps to save checkpoint.

Returns: int, steps to save checkpoint.

property saved_network

Get the value of network to be saved in checkpoint file.

Returns: Cell, network to be saved in checkpoint file.