mindspore.train
mindspore.train.summary
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)[source]
SummaryRecord is used to record the summary value.
Note
The API will create an event file in a given directory and add summaries and events to it. It writes the event log to a file by executing the record method. In addition, if the SummaryRecord object is created and the summary operator is used in the network, even if the record method is not called, the event in the cache will be written to the file at the end of execution or when the summary is closed.
- 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()[source]
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()[source]
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)[source]
Record the summary.
- Parameters
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.
mindspore.train.callback
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
>>> 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)[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.
- epoch_begin(run_context)[source]
Called before each epoch beginning.
- Parameters
run_context (RunContext) – Include some information of the model.
- epoch_end(run_context)[source]
Called after each epoch finished.
- Parameters
run_context (RunContext) – Include some information of the model.
- step_begin(run_context)[source]
Called before each epoch beginning.
- Parameters
run_context (RunContext) – Include some information of the model.
- step_end(run_context)[source]
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, integrated_save=True)[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.
integrated_save (bool) – Whether to intergrated save in automatic model parall scene. Default: True. Integrated save function is only supported in automatic parall scene, not supported in manual parallel.
- 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)
- property integrated_save
Get the value of _integrated_save.
- 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)[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.
- step_end(run_context)[source]
Save the checkpoint at the end of step.
- Parameters
run_context (RunContext) – Context of the train running.
- 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.
- 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.
- step_end(run_context)[source]
Save summary.
- Parameters
run_context (RunContext) – Context of the train running.
mindspore.train.serialization
Model and parameters serialization.
- 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) – Inputs of the net.
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. A lite model only for the MindSpore Lite
- mindspore.train.serialization.load_checkpoint(ckpoint_file_name, net=None)[source]
Loads checkpoint info from a specified file.
- Parameters
- 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)[source]
Loads parameters into network.
- mindspore.train.serialization.save_checkpoint(parameter_list, ckpoint_file_name)[source]
Saves checkpoint info to a specified file.
- Parameters
- Raises
RuntimeError – Failed to save the Checkpoint file.
mindspore.train.amp
Auto mixed precision.
- mindspore.train.amp.build_train_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 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.
mindspore.train.loss_scale_manager
Loss scale manager abstract class.
- class mindspore.train.loss_scale_manager.DynamicLossScaleManager(init_loss_scale=16777216, scale_factor=2, scale_window=2000)[source]
Dynamic loss-scale manager.
- Parameters
Examples
>>> loss_scale_manager = DynamicLossScaleManager() >>> model = Model(net, loss_scale_manager=loss_scale_manager)
- class mindspore.train.loss_scale_manager.FixedLossScaleManager(loss_scale=128.0, drop_overflow_update=True)[source]
Fixed loss-scale manager.
- Parameters
Examples
>>> loss_scale_manager = FixedLossScaleManager() >>> model = Model(net, loss_scale_manager=loss_scale_manager)