def __init__(self,
subgraph='all',
is_detail=True,
is_show_op_path=False,
output_path='./data',
optypes_to_deal='',
optypes_not_deal='Variable',
job_id=""):
# get device_id and device_target
self._get_devid_and_devtarget()
self._output_path = validate_and_normalize_path(output_path)
self._output_path = os.path.join(self._output_path, "profiler")
if not os.path.exists(self._output_path):
os.makedirs(self._output_path, exist_ok=True)
else:
logger.warning(
"The target dir already exists. "
"There may be some old profiling data, and they will be rewrote in the end."
)
if self._device_target and self._device_target == "GPU":
from mindspore._c_expression import GPUProfiler
self._gpu_profiler = GPUProfiler.get_instance()
self._gpu_profiler.init(self._output_path)
self._gpu_profiler.step_profiling_enable(True)
elif self._device_target and (self._device_target == "Ascend"
or self._device_target != "Davinci"):
self._container_path = os.path.join(
self._base_profiling_container_path, self._dev_id)
data_path = os.path.join(self._container_path, "data")
if not os.path.exists(data_path):
os.makedirs(data_path, exist_ok=True)
os.environ['PROFILING_MODE'] = 'true'
os.environ['PROFILING_OPTIONS'] = 'training_trace:task_trace'
os.environ['MINDDATA_PROFILING_DIR'] = self._output_path
os.environ['DEVICE_ID'] = self._dev_id
os.environ['AICPU_PROFILING_MODE'] = 'true'
os.environ['PROFILING_DIR'] = str(self._container_path)
# use context interface to open profiling, for the new mindspore version(after 2020.5.21)
context.set_context(enable_profiling=True,
profiling_options="training_trace:task_trace")
self._subgraph = check_subgraph(subgraph)
self._valid_optype_name = optypes_to_deal.split(
",") if optypes_to_deal else []
self._filt_optype_names = optypes_not_deal.split(
",") if optypes_not_deal else []
self._detail = check_bool(is_detail, 'is_detail')
self._withfullpath = check_bool(is_show_op_path, 'is_show_op_path')
self._profiling_job_id = job_id
# add job id env through user input later
self._job_id_env = 0
self._start_time = int(time.time() * 10000000)
logger.info("Profiling: profiling start time: %d",
self._start_time)
def __init__(self,
subgraph='all',
is_detail=True,
is_show_op_path=False,
output_path='./data',
optypes_to_deal='',
optypes_not_deal='Variable',
job_id=""):
# get device_id and device_target
self._get_devid_and_devtarget()
self._container_path = os.path.join(
self._base_profiling_container_path, self._dev_id)
data_path = os.path.join(self._container_path, "data")
if not os.path.exists(data_path):
os.makedirs(data_path, exist_ok=True)
self._output_path = validate_and_normalize_path(output_path)
self._output_path = os.path.join(self._output_path, "profiler")
if not os.path.exists(self._output_path):
os.makedirs(self._output_path, exist_ok=True)
os.environ['PROFILING_MODE'] = 'true'
os.environ['PROFILING_OPTIONS'] = 'training_trace:task_trace'
os.environ['MINDDATA_PROFILING_DIR'] = self._output_path
os.environ['DEVICE_ID'] = self._dev_id
os.environ['AICPU_PROFILING_MODE'] = 'true'
os.environ['PROFILING_DIR'] = str(self._container_path)
# use context interface to open profiling, for the new mindspore version(after 2020.5.21)
context.set_context(enable_profiling=True,
profiling_options="training_trace:task_trace")
self._subgraph = check_subgraph(subgraph)
self._valid_optype_name = optypes_to_deal.split(
",") if optypes_to_deal else []
self._filt_optype_names = optypes_not_deal.split(
",") if optypes_not_deal else []
self._detail = check_bool(is_detail, 'is_detail')
self._withfullpath = check_bool(is_show_op_path, 'is_show_op_path')
self._profiling_job_id = job_id
# add job id env through user input later
self._job_id_env = 0
self._start_time = int(time.time() * 10000000)
logger.info("Profiling: profiling start time: %d", self._start_time)