mindspore.profiler.DynamicProfilerMonitor

查看源文件
class mindspore.profiler.DynamicProfilerMonitor(cfg_path, output_path='./dyn_profile_data', poll_interval=2, **kwargs)[源代码]

该类用于动态采集MindSpore神经网络性能数据。

参数:
  • cfg_path (str) - 动态profile的json配置文件文件夹路径。要求该路径是能够被所有节点访问到的共享目录。

  • output_path (str, 可选) - 动态profile的输出文件路径。默认值:"./dyn_profile_data"

  • poll_interval (int, 可选) - 监控进程的轮询周期,单位为秒。默认值:2

异常:
  • RuntimeError - 创建监控进程失败次数超过最大限制。

支持平台:

Ascend GPU

样例:

>>> 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])