mindspore.train

SummaryRecord.

User can use SummaryRecord to dump the summary data, the summary is a series of operations to collect data for analysis and visualization.

class mindspore.train.summary.SummaryRecord(log_dir, queue_max_size=0, flush_time=120, file_prefix='events', file_suffix='_MS', network=None)

Summary log record.

SummaryRecord is used to record the summary value. The API will create an event file in a given directory and add summaries and events to it.

Parameters

• log_dir (str) – The log_dir is a directory location to save the summary.

• queue_max_size (int) – The capacity of event queue.(reserved). Default: 0.

• flush_time (int) – Frequency to flush the summaries to disk, the unit is second. Default: 120.

• file_prefix (str) – The prefix of file. Default: “events”.

• file_suffix (str) – The suffix of file. Default: “_MS”.

• network (Cell) – Obtain a pipeline through network for saving graph summary. Default: None.

Raises

• TypeError – If queue_max_size and flush_time is not int, or file_prefix and file_suffix is not str.

• RuntimeError – If the log_dir can not be resolved to a canonicalized absolute pathname.

Examples

>>> summary_record = SummaryRecord(log_dir="/opt/log", queue_max_size=50, flush_time=6,
>>>                                file_prefix="xxx_", file_suffix="_yyy")

close()

Flush all events and close summary records.

Examples

>>> summary_record = SummaryRecord(log_dir="/opt/log", queue_max_size=50, flush_time=6,
>>>                                file_prefix="xxx_", file_suffix="_yyy")
>>> summary_record.close()

flush()

Flush the event file to disk.

Call it to make sure that all pending events have been written to disk.

Examples

>>> summary_record = SummaryRecord(log_dir="/opt/log", queue_max_size=50, flush_time=6,
>>>                                file_prefix="xxx_", file_suffix="_yyy")
>>> summary_record.flush()

property log_dir

Get the full path of the log file.

Examples

>>> summary_record = SummaryRecord(log_dir="/opt/log", queue_max_size=50, flush_time=6,
>>>                                file_prefix="xxx_", file_suffix="_yyy")
>>> print(summary_record.log_dir)

Returns

String, the full path of log file.

record(step, train_network=None)

Record the summary.

Parameters

• step (int) – Represents training step number.

• train_network (Cell) – The network that called the callback.

Examples

>>> summary_record = SummaryRecord(log_dir="/opt/log", queue_max_size=50, flush_time=6,
>>>                                file_prefix="xxx_", file_suffix="_yyy")
>>> summary_record.record(step=2)

Returns

bool, whether the record process is successful or not.

Callback related classes and functions.

class mindspore.train.callback.Callback

Abstract base class used to build a callback function.

Callback function will execution some operating to the current step or epoch.

Examples

>>> class Print_info(Callback):
>>>     def step_end(self, run_context):
>>>         cb_params = run_context.original_args()
>>>         print(cb_params.cur_epoch_num)
>>>         print(cb_params.cur_step_num)
>>>
>>> print_cb = Print_info()
>>> model.train(epoch, dataset, callback=print_cb)

begin(run_context)

Called once before the network executing.

Parameters

run_context (RunContext) – Include some information of the model.

end(run_context)

Called once after network training.

Parameters

run_context (RunContext) – Include some information of the model.

epoch_begin(run_context)

Called before each epoch beginning.

Parameters

run_context (RunContext) – Include some information of the model.

epoch_end(run_context)

Called after each epoch finished.

Parameters

run_context (RunContext) – Include some information of the model.

step_begin(run_context)

Called before each epoch beginning.

Parameters

run_context (RunContext) – Include some information of the model.

step_end(run_context)

Called after each step finished.

Parameters

run_context (RunContext) – Include some information of the model.

class mindspore.train.callback.CheckpointConfig(save_checkpoint_steps=1, save_checkpoint_seconds=0, keep_checkpoint_max=5, keep_checkpoint_per_n_minutes=0)

The config for model checkpoint.

Parameters

• save_checkpoint_steps (int) – Steps to save checkpoint. Default: 1.

• save_checkpoint_seconds (int) – Seconds to save checkpoint. Default: 0. Can’t be used with save_checkpoint_steps at the same time.

• keep_checkpoint_max (int) – Maximum step to save checkpoint. Default: 5.

• keep_checkpoint_per_n_minutes (int) – Keep one checkpoint every n minutes. Default: 0. Can’t be used with keep_checkpoint_max at the same time.

Raises

ValueError – If the input_param is None or 0.

Examples

>>> config = CheckpointConfig()
>>> ckpoint_cb = ModelCheckpoint(prefix="ck_prefix", directory='./', config=config)
>>> model.train(10, dataset, callbacks=ckpoint_cb)

get_checkpoint_policy()

Get the policy of checkpoint.

property keep_checkpoint_max

Get the value of _keep_checkpoint_max.

property keep_checkpoint_per_n_minutes

Get the value of _keep_checkpoint_per_n_minutes.

property save_checkpoint_seconds

Get the value of _save_checkpoint_seconds.

property save_checkpoint_steps

Get the value of _save_checkpoint_steps.

class mindspore.train.callback.LossMonitor(per_print_times=1)

Monitor the loss in training.

If the loss is NAN or INF, it will terminate training.

Note

If per_print_times is 0 do not print loss.

Parameters

per_print_times (int) – Print loss every times. Default: 1.

Raises

ValueError – If print_step is not int or less than zero.

class mindspore.train.callback.ModelCheckpoint(prefix='CKP', directory=None, config=None)

The checkpoint callback class.

It is called to combine with train process and save the model and network parameters after traning.

Parameters

• prefix (str) – Checkpoint files names prefix. Default: “CKP”.

• directory (str) – Lolder path into which checkpoint files will be saved. Default: None.

• config (CheckpointConfig) – Checkpoint strategy config. Default: None.

Raises

• ValueError – If the prefix is invalid.

• TypeError – If the config is not CheckpointConfig type.

end(run_context)

Save the last checkpoint after training finished.

Parameters

run_context (RunContext) – Context of the train running.

property latest_ckpt_file_name

Return the latest checkpoint path and file name.

step_end(run_context)

Save the checkpoint at the end of step.

Parameters

run_context (RunContext) – Context of the train running.

class mindspore.train.callback.RunContext(original_args)

Provides information about the model.

Run call being made. Provides information about original request to model function. callback objects can stop the loop by calling request_stop() of run_context.

Parameters

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

get_stop_requested()

Returns whether a stop is requested or not.

Returns

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

original_args()

Get the _original_args object.

Returns

_InternalCallbackParam, a object holding the original arguments of model.

request_stop()

Sets stop requested during training.

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

class mindspore.train.callback.SummaryStep(summary, flush_step=10)

The summary callback class.

Parameters

• summary (Object) – Summary recode object.

• flush_step (int) – Number of interval steps to execute. Default: 10.

step_end(run_context)

Save summary.

Parameters

run_context (RunContext) – Context of the train running.

Model and parameters serialization.

mindspore.train.serialization.export(net, *inputs, file_name, file_format='GEIR')

Exports MindSpore predict model to file in specified format.

Parameters

• net (Cell) – MindSpore network.

• inputs (Tensor) – network inputs.

• file_name (str) – File name of model to export.

• file_format (str) –

  MindSpore currently supports ‘GEIR’, ‘ONNX’ and ‘LITE’ format for exported model.

  • GEIR: Graph Engine Intermidiate Representation. An intermidiate representation format of

    Ascend model.

  • ONNX: Open Neural Network eXchange. An open format built to represent machine learning models.

  • LITE: Huawei model format for mobile.

mindspore.train.serialization.load_checkpoint(ckpoint_file_name, net=None)

Loads checkpoint info from a specified file.

Parameters

• ckpoint_file_name (str) – Checkpoint file name.

• net (Cell) – Cell network. Default: None

Returns

Dict, key is parameter name, value is a Parameter.

Raises

ValueError – Checkpoint file is incorrect.

mindspore.train.serialization.load_param_into_net(net, parameter_dict)

Loads parameters into network.

Parameters

• net (Cell) – Cell network.

• parameter_dict (dict) – Parameter dict.

Raises

TypeError – Argument is not a Cell, or parameter_dict is not a Parameter dict.

mindspore.train.serialization.save_checkpoint(parameter_list, ckpoint_file_name)

Saves checkpoint info to a specified file.

Parameters

• parameter_list (list) – Parameters list, each element is a dict like {“name”:xx, “type”:xx, “shape”:xx, “data”:xx}.

• ckpoint_file_name (str) – Checkpoint file name.

Raises

RuntimeError – Failed to save the Checkpoint file.

Auto mixed precision.

mindspore.train.amp.build_train_network(network, optimizer, loss_fn=None, level='O0', **kwargs)

Build the mixed precision training cell automatically.

Parameters

• network (Cell) – Definition of the network.

• loss_fn (Union[None, Cell]) – Definition of the loss_fn. If None, the network should have the loss inside. Default: None.

• optimizer (Optimizer) – Optimizer to update the Parameter.

• level (str) –

  Supports [O0, O2]. Default: “O0”.

  • O0: Do not change.

  • O2: Cast network to float16, keep batchnorm and loss_fn (if set) run in float32, using dynamic loss scale.

• cast_model_type (mindspore.dtype) – Supports mstype.float16 or mstype.float32. If set to mstype.float16, use float16 mode to train. If set, overwrite the level setting.

• keep_batchnorm_fp32 (bool) – Keep Batchnorm run in float32. If set, overwrite the level setting.

• loss_scale_manager (Union[None, LossScaleManager]) – If None, not scale the loss, or else scale the loss by LossScaleManager. If set, overwrite the level setting.

Loss scale manager abstract class.

class mindspore.train.loss_scale_manager.DynamicLossScaleManager(init_loss_scale=16777216, scale_factor=2, scale_window=2000)

Dynamic loss-scale manager.

Parameters

• init_loss_scale (float) – Init loss scale. Default: 2**24.

• scale_factor (int) – Coefficient of increase and decrease. Default: 2.

• scale_window (int) – Maximum continuous normal steps when there is no overflow. Default: 2000.

Examples

>>> loss_scale_manager = DynamicLossScaleManager()
>>> model = Model(net, loss_scale_manager=loss_scale_manager)

get_drop_overflow_update()

Get the flag whether to drop optimizer update when there is overflow happened

get_loss_scale()

Get loss scale value.

get_update_cell()

Returns the cell for TrainOneStepWithLossScaleCell

update_loss_scale(overflow)

Update loss scale value.

Parameters

overflow – Boolean. Whether it overflows.

class mindspore.train.loss_scale_manager.FixedLossScaleManager(loss_scale=128.0, drop_overflow_update=True)

Fixed loss-scale manager.

Parameters

• loss_scale (float) – Loss scale. Default: 128.0.

• drop_overflow_update (bool) – whether to do optimizer if there is overflow. Default: True.

Examples

>>> loss_scale_manager = FixedLossScaleManager()
>>> model = Model(net, loss_scale_manager=loss_scale_manager)

get_drop_overflow_update()

Get the flag whether to drop optimizer update when there is overflow happened

get_loss_scale()

Get loss scale value.

get_update_cell()

Returns the cell for TrainOneStepWithLossScaleCell

update_loss_scale(overflow)

Update loss scale value.

Parameters

overflow (bool) – Whether it overflows.

class mindspore.train.loss_scale_manager.LossScaleManager

Loss scale manager abstract class.

get_loss_scale()

Get loss scale value.

get_update_cell()

Get the loss scaling update logic cell.

update_loss_scale(overflow)

Update loss scale value.

Parameters

overflow (bool) – Whether it overflows.