mindspore.profiler.DynamicProfilerMonitor

class mindspore.profiler.DynamicProfilerMonitor(cfg_path, output_path='./dyn_profile_data', poll_interval=2, **kwargs)

This class to enable the dynamic profile monitoring of MindSpore neural networks.

Parameters

• cfg_path (str) – Dynamic profile json config file directory. The requirement is a shared path that can be accessed by all nodes.

• output_path (str, optional) – Output data path. Default: "./dyn_profile_data" .

• poll_interval (int, optional) – The polling period of the monitoring process, in seconds. Default value: 2.

Raises

RuntimeError – When create shared memory times exceeds max times.

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import nn
>>> import mindspore.dataset as ds
>>> from mindspore.profiler import DynamicProfilerMonitor
>>>
>>> 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"])
...     dynprof_cb = DynamicProfilerMonitor(cfg_path="./dyn_cfg", output_path="./dyn_prof_data")
...     model = ms.train.Model(net, loss, optimizer)
...     # register DynamicProfilerMonitor to model.train()
...     model.train(10, data, callbacks=[dynprof_cb])