mindspore.train

SummaryRecord.

User can use SummaryRecord to dump the summary data, the summary is a series of operationsto 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)[source]
  • 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

  1. Copy>>> summary_record = SummaryRecord(log_dir="/opt/log", queue_max_size=50, flush_time=6,
  2. >>>                                file_prefix="xxx_", file_suffix="_yyy")
  • close()[source]
  • Flush all events and close summary records.

Examples

  1. Copy>>> summary_record = SummaryRecord(log_dir="/opt/log", queue_max_size=50, flush_time=6,
  2. >>>                                file_prefix="xxx_", file_suffix="_yyy")
  3. >>> summary_record.close()
  • flush()[source]
  • Flush the event file to disk.

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

Examples

  1. Copy>>> summary_record = SummaryRecord(log_dir="/opt/log", queue_max_size=50, flush_time=6,
  2. >>>                                file_prefix="xxx_", file_suffix="_yyy")
  3. >>> summary_record.flush()
  • property log_dir
  • Get the full path of the log file.

Examples

  1. Copy>>> summary_record = SummaryRecord(log_dir="/opt/log", queue_max_size=50, flush_time=6,
  2. >>>                                file_prefix="xxx_", file_suffix="_yyy")
  3. >>> print(summary_record.log_dir)
  1. - Returns
  2. -

String, the full path of log file.

  • record(step, train_network=None)[source]

  • Record the summary.

    • Parameters

      • step (int) – Represents training step number.

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

Examples

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

bool, whether the record process is successful or not.

Callback related classes and functions.

  • class mindspore.train.callback.Callback[source]
  • Abstract base class used to build a callback function.

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

Examples

  1. Copy>>> class Print_info(Callback):
  2. >>>     def step_end(self, run_context):
  3. >>>         cb_params = run_context.original_args()
  4. >>>         print(cb_params.cur_epoch_num)
  5. >>>         print(cb_params.cur_step_num)
  6. >>>
  7. >>> print_cb = Print_info()
  8. >>> model.train(epoch, dataset, callback=print_cb)
  • begin(run_context)[source]

  • Called once before the network executing.

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

  • end(run_context)[source]

  • Called once after network training.

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

  • epochbegin(_run_context)[source]

  • Called before each epoch beginning.

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

  • epochend(_run_context)[source]

  • Called after each epoch finished.

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

  • stepbegin(_run_context)[source]

  • Called before each epoch beginning.

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

  • stepend(_run_context)[source]

  • Called after each step finished.

    • Parameters
    • run_context (RunContext) – Include some information of the model.
  • class mindspore.train.callback.LossMonitor(per_print_times=1)[source]
  • 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)[source]
  • 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)[source]

  • 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.

  • stepend(_run_context)[source]

  • Save the checkpoint at the end of step.

    • Parameters
    • run_context (RunContext) – Context of the train running.
  • class mindspore.train.callback.SummaryStep(summary, flush_step=10)[source]

  • The summary callback class.

    • Parameters

      • summary (Object) – Summary recode object.

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

    • stepend(_run_context)[source]

    • Save summary.

      • Parameters
      • run_context (RunContext) – Context of the train running.
  • class mindspore.train.callback.CheckpointConfig(save_checkpoint_steps=1, save_checkpoint_seconds=0, keep_checkpoint_max=5, keep_checkpoint_per_n_minutes=0)[source]

  • 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

  1. Copy>>> config = CheckpointConfig()
  2. >>> ckpoint_cb = ModelCheckpoint(prefix="ck_prefix", directory='./', config=config)
  3. >>> model.train(10, dataset, callbacks=ckpoint_cb)
  • get_checkpoint_policy()[source]

  • 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.RunContext(original_args)[source]
  • 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()[source]

  • Returns whether a stop is requested or not.

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

  • original_args()[source]

  • Get the _original_args object.

    • Returns
    • _InternalCallbackParam, a object holding the original arguments of model.

  • request_stop()[source]

  • Sets stop requested during training.

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

Model and parameters serialization.

  • mindspore.train.serialization.savecheckpoint(_parameter_list, ckpoint_file_name)[source]

  • Saves checkpoint info to a specified file.

    • Parameters

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

      • ckpoint_file_name (str) – Checkpoint file name.

    • Raises

    • RuntimeError – Failed to save the Checkpoint file.
  • mindspore.train.serialization.loadcheckpoint(_ckpoint_file_name, net=None)[source]

  • 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.loadparam_into_net(_net, parameter_dict)[source]

  • 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.export(net, *inputs, file_name, file_format='GEIR')[source]

  • 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.

  1.   - 
  2.     - GEIR: Graph Engine Intermidiate Representation. An intermidiate representation format of
  3.     -

Ascend model.

  1.   -

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

  1.   -

LITE: Huawei model format for mobile.

Auto mixed precision.

  • mindspore.train.amp.buildtrain_network(_network, optimizer, loss_fn=None, level='O0', **kwargs)[source]

  • Build the mixed precision training cell automatically.

    • Parameters

      • network (Cell) – Definition of the network.

      • loss_fn (Union__[None, Cell]) – Definition of the lossfn. 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”.

  1.   -

O0: Do not change.

  1.   -

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

  1. -

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.

  1. -

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

  1. -

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

Loss scale manager abstract class.

  • class mindspore.train.loss_scale_manager.LossScaleManager[source]

  • Loss scale manager abstract class.

    • get_loss_scale()[source]

    • Get loss scale value.

    • get_update_cell()[source]

    • Get the loss scaling update logic cell.

    • updateloss_scale(_overflow)[source]

    • Update loss scale value.

      • Parameters
      • overflow (bool) – Whether it overflows.
  • class mindspore.train.lossscale_manager.FixedLossScaleManager(_loss_scale=128.0, drop_overflow_update=True)[source]

  • 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

  1. Copy>>> loss_scale_manager = FixedLossScaleManager()
  2. >>> model = Model(net, loss_scale_manager=loss_scale_manager)
  • get_drop_overflow_update()[source]

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

  • get_loss_scale()[source]

  • Get loss scale value.

  • get_update_cell()[source]

  • Returns the cell for TrainOneStepWithLossScaleCell

  • updateloss_scale(_overflow)[source]

  • Update loss scale value.

    • Parameters
    • overflow (bool) – Whether it overflows.
  • class mindspore.train.lossscale_manager.DynamicLossScaleManager(_init_loss_scale=16777216, scale_factor=2, scale_window=2000)[source]

  • 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

  1. Copy>>> loss_scale_manager = DynamicLossScaleManager()
  2. >>> model = Model(net, loss_scale_manager=loss_scale_manager)
  • get_drop_overflow_update()[source]

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

  • get_loss_scale()[source]

  • Get loss scale value.

  • get_update_cell()[source]

  • Returns the cell for TrainOneStepWithLossScaleCell

  • updateloss_scale(_overflow)[source]

  • Update loss scale value.

    • Parameters
    • overflow – Boolean. Whether it overflows.