def begin(self, run_context):
cb_params = run_context.original_args()
self._check_callbacks(cb_params)
if cb_params.mode not in ModeEnum.to_list():
raise ValueError(
'Only support `train` (model.train) and `eval` (model.eval) mode, '
'but got `{cb_params.mode}` mode.')
self._record.set_mode(cb_params.mode)
if cb_params.mode == ModeEnum.TRAIN.value:
# Note: if model.init is not executed then the computed graph will not be obtained here
# The purpose of recording the graph here was to collect_freq if it was set to a large size,
# but also want to see the graph as soon after compilation.
self._collect_graphs(cb_params)
self._collect_dataset_graph(cb_params)
if self._custom_lineage_data and not self._has_saved_custom_data:
packaged_custom_data = self._package_custom_lineage_data(
self._custom_lineage_data)
self._record.add_value('custom_lineage_data',
'custom_lineage_data', packaged_custom_data)
self._has_saved_custom_data = True
# There's nothing special about setting step to 0 here, just to satisfy the interface call
self._record.record(step=0)
def begin(self, run_context):
cb_params = run_context.original_args()
self._check_callbacks(cb_params)
if cb_params.mode not in ModeEnum.to_list():
raise ValueError(
'Only support `train` (model.train) and `eval` (model.eval) mode, '
'but got `{cb_params.mode}` mode.')
self._record.set_mode(cb_params.mode)
def begin(self, run_context):
cb_params = run_context.original_args()
self._check_callbacks(cb_params)
if cb_params.mode not in ModeEnum.to_list():
raise ValueError(
'Only support `train` (model.train) and `eval` (model.eval) mode, '
'but got `{cb_params.mode}` mode.')
self._record.set_mode(cb_params.mode)
if cb_params.mode == ModeEnum.TRAIN.value:
if self._collect_tensor_freq is None:
default_tensor_summary_limit = 20
total_step = cb_params.epoch_num * cb_params.batch_num
self._collect_tensor_freq = max(
self._collect_freq,
total_step // default_tensor_summary_limit)