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!4846 Add gpu usage to Profiler docstring. 

Merge pull request !4846 from yuximiao/gpu_comments
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mindspore-ci-bot 2020-08-21 09:26:31 +08:00 committed by Gitee
parent 4ee0bf22c3 6e4798e69b
commit bbb2a34495
1 changed files with 10 additions and 7 deletions
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17
mindspore/profiler/profiling.py
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@ -46,17 +46,20 @@ class Profiler:
Performance profiling API.
Enable MindSpore users to profile the performance of neural network.
Profiler support Ascend and GPU, both of them are used in the same way,
but only output_path in args works on GPU.
Args:
subgraph (str): Define which subgraph to monitor and analyse, can be 'all', 'Default', 'Gradients'.
is_detail (bool): Whether to show profiling data for op_instance level, only show optype level if False.
is_show_op_path (bool): Whether to save the full path for each op instance.
subgraph (str): (Ascend only)Define which subgraph to monitor and analyse, can be 'all', 'Default', 'Gradients'.
is_detail (bool): (Ascend only)Whether to show profiling data for op_instance level,
only show optype level if False.
is_show_op_path (bool): (Ascend only)Whether to save the full path for each op instance.
output_path (str): Output data path.
optypes_to_deal (str): Op type names, the data of which optype should be collected and analysed,
optypes_to_deal (str): (Ascend only)Op type names, the data of which optype should be collected and analysed,
will deal with all op if null; Different op types should be seperated by comma.
optypes_not_deal (str): Op type names, the data of which optype will not be collected and analysed;
optypes_not_deal (str): (Ascend only)Op type names, the data of which optype will not be collected and analysed;
Different op types should be seperated by comma.
job_id (str): The directory where the parsed profiling files are located;
job_id (str): (Ascend only)The directory where the parsed profiling files are located;
This parameter is used to support offline parsing.
Examples:
@ -64,7 +67,7 @@ class Profiler:
>>> import mindspore.context
>>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
>>> device_id=int(os.environ["DEVICE_ID"]))
>>> profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False, output_path='./data')
>>> profiler = Profiler()
>>> model = Model()
>>> model.train()
>>> profiler.analyse()