mindspore.profiler

This module provides Python APIs to enable the profiling of MindSpore neural networks. Users can import the mindspore.profiler.Profiler, initialize the Profiler object to start profiling, and use Profiler.analyse() to stop profiling and analyse the results. Users can visualize the results using the MindInsight tool. Now, Profiler supports AICORE operator, AICPU operator, HostCPU operator, memory, correspondence, cluster, etc data analysis.

class mindspore.profiler.Profiler(**kwargs)

MindSpore users can use this class to collect the performance of neural networks.

Parameters

• output_path (str, optional) – Output data path. Default: “./data”.

• profile_communication (bool, optional) – (Ascend only) Whether to collect communication performance data in a multi devices training,collect when True. Setting this parameter has no effect during single device training. Default: False.

• profile_memory (bool, optional) – (Ascend only) Whether to collect tensor memory data, collect when True. Default: False.

• start_profile (bool, optional) – The start_profile parameter controls whether to enable or disable performance data collection based on conditions. Default: True.

Raises

RuntimeError – When the version of CANN does not match the version of MindSpore, MindSpore cannot parse the generated ascend_job_id directory structure.

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> from mindspore import nn, context
>>> from mindspore import Model
>>> import mindspore.dataset as ds
>>> from mindspore.profiler import Profiler
>>>
>>>
>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.fc = nn.Dense(2,2)
...     def construct(self, x):
...         return self.fc(x)
>>>
>>> def generator():
...     for i in range(2):
...         yield (np.ones([2, 2]).astype(np.float32), np.ones([2]).astype(np.int32))
>>>
>>> def train(net):
...     optimizer = nn.Momentum(net.trainable_params(), 1, 0.9)
...     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
...     data = ds.GeneratorDataset(generator, ["data", "label"])
...     model = Model(net, loss, optimizer)
...     model.train(1, data)
>>>
>>> if __name__ == '__main__':
...     # If the device_target is GPU, set the device_target to "GPU"
...     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
...
...     # Init Profiler
...     # Note that the Profiler should be initialized after context.set_context and before model.train
...     # If you are running in parallel mode on Ascend, the Profiler should be initialized before HCCL
...     # initialized.
...     profiler = Profiler()
...
...     # Train Model
...     net = Net()
...     train(net)
...
...     # Profiler end
...     profiler.analyse()

analyse()

Collect and analyze training performance data, support calls during and after training. The example shows above.

start()

Used for Ascend, GPU, start profiling. Profiling can be turned on based on step and epoch.

Raises

• RuntimeError – If the profiler has already started.

• RuntimeError – If MD profiling has stopped, repeated start action is not supported.

• RuntimeError – If the start_profile parameter is not set or is set to True.

Examples

>>> class StopAtStep(Callback):
>>>     def __init__(self, start_step, stop_step):
...         super(StopAtStep, self).__init__()
...         self.start_step = start_step
...         self.stop_step = stop_step
...         self.profiler = Profiler(start_profile=False)
...
>>>     def step_begin(self, run_context):
...         cb_params = run_context.original_args()
...         step_num = cb_params.cur_step_num
...         if step_num == self.start_step:
...             self.profiler.start()
...
>>>     def step_end(self, run_context):
...         cb_params = run_context.original_args()
...         step_num = cb_params.cur_step_num
...         if step_num == self.stop_step:
...             self.profiler.stop()
...
>>>     def end(self, run_context):
...         self.profiler.analyse()

stop()

Used for Ascend, GPU, stop profiling. Profiling can be turned off based on step and epoch.

Raises

RuntimeError – If the profiler has not started, this function is disabled.

Examples

>>> class StopAtEpoch(Callback):
>>>     def __init__(self, start_epoch, stop_epoch):
...         super(StopAtEpoch, self).__init__()
...         self.start_epoch = start_epoch
...         self.stop_epoch = stop_epoch
...         self.profiler = Profiler(start_profile=False)
...
>>>     def epoch_begin(self, run_context):
...         cb_params = run_context.original_args()
...         epoch_num = cb_params.cur_epoch_num
...         if epoch_num == self.start_epoch:
...             self.profiler.start()
...
>>>     def epoch_end(self, run_context):
...         cb_params = run_context.original_args()
...         epoch_num = cb_params.cur_epoch_num
...         if epoch_num == self.stop_epoch:
...             self.profiler.stop()
...
>>>     def end(self, run_context):
...         self.profiler.analyse()