def _prep_cli_for_run_end(self, debug_dump, tf_error, passed_filter):
"""Prepare (but not launch) CLI for run-end, with debug dump from the run.
Args:
debug_dump: (debug_data.DebugDumpDir) The debug dump directory from this
run.
tf_error: (None or OpError) OpError that happened during the run() call
(if any).
passed_filter: (None or str) Name of the tensor filter that just passed
and caused the preparation of this run-end CLI (if any).
"""
if tf_error:
help_intro = cli_shared.get_error_intro(tf_error)
self._init_command = "help"
self._title_color = "red_on_white"
else:
help_intro = None
self._init_command = "lt"
self._title_color = "black_on_white"
if passed_filter is not None:
# Some dumped tensor(s) from this run passed the filter.
self._init_command = "lt -f %s" % passed_filter
self._title_color = "red_on_white"
self._run_cli = analyzer_cli.create_analyzer_ui(
debug_dump,
self._tensor_filters,
ui_type=self._ui_type,
on_ui_exit=self._remove_dump_root)
# Get names of all dumped tensors.
dumped_tensor_names = []
for datum in debug_dump.dumped_tensor_data:
dumped_tensor_names.append("%s:%d" %
(datum.node_name, datum.output_slot))
# Tab completions for command "print_tensors".
self._run_cli.register_tab_comp_context(["print_tensor", "pt"],
dumped_tensor_names)
# Tab completion for commands "node_info", "list_inputs" and
# "list_outputs". The list comprehension is used below because nodes()
# output can be unicodes and they need to be converted to strs.
self._run_cli.register_tab_comp_context(
["node_info", "ni", "list_inputs", "li", "list_outputs", "lo"],
[str(node_name) for node_name in debug_dump.nodes()])
# TODO(cais): Reduce API surface area for aliases vis-a-vis tab
# completion contexts and registered command handlers.
self._title = "run-end: " + self._run_description
if help_intro:
self._run_cli.set_help_intro(help_intro)
def _prep_cli_for_run_end(self, debug_dump, tf_error, passed_filter):
"""Prepare (but not launch) CLI for run-end, with debug dump from the run.
Args:
debug_dump: (debug_data.DebugDumpDir) The debug dump directory from this
run.
tf_error: (None or OpError) OpError that happened during the run() call
(if any).
passed_filter: (None or str) Name of the tensor filter that just passed
and caused the preparation of this run-end CLI (if any).
"""
if tf_error:
help_intro = cli_shared.get_error_intro(tf_error)
self._init_command = "help"
self._title_color = "red_on_white"
else:
help_intro = None
self._init_command = "lt"
self._title_color = "black_on_white"
if passed_filter is not None:
# Some dumped tensor(s) from this run passed the filter.
self._init_command = "lt -f %s" % passed_filter
self._title_color = "red_on_white"
self._run_cli = analyzer_cli.create_analyzer_ui(
debug_dump, self._tensor_filters, ui_type=self._ui_type,
on_ui_exit=self._remove_dump_root)
# Get names of all dumped tensors.
dumped_tensor_names = []
for datum in debug_dump.dumped_tensor_data:
dumped_tensor_names.append("%s:%d" %
(datum.node_name, datum.output_slot))
# Tab completions for command "print_tensors".
self._run_cli.register_tab_comp_context(["print_tensor", "pt"],
dumped_tensor_names)
# Tab completion for commands "node_info", "list_inputs" and
# "list_outputs". The list comprehension is used below because nodes()
# output can be unicodes and they need to be converted to strs.
self._run_cli.register_tab_comp_context(
["node_info", "ni", "list_inputs", "li", "list_outputs", "lo"],
[str(node_name) for node_name in debug_dump.nodes()])
# TODO(cais): Reduce API surface area for aliases vis-a-vis tab
# completion contexts and registered command handlers.
self._title = "run-end: " + self._run_description
if help_intro:
self._run_cli.set_help_intro(help_intro)
def main(_):
if FLAGS.log_usage:
pass # No logging for open-source.
if not FLAGS.dump_dir:
print("ERROR: dump_dir flag is empty.", file=sys.stderr)
sys.exit(1)
print("tfdbg offline: FLAGS.dump_dir = %s" % FLAGS.dump_dir)
debug_dump = debug_data.DebugDumpDir(
FLAGS.dump_dir, validate=FLAGS.validate_graph)
cli = analyzer_cli.create_analyzer_ui(
debug_dump,
tensor_filters={"has_inf_or_nan": debug_data.has_inf_or_nan},
ui_type=FLAGS.ui_type)
title = "tfdbg offline @ %s" % FLAGS.dump_dir
cli.run_ui(title=title, title_color="black_on_white", init_command="lt")
def main(_):
if FLAGS.log_usage:
pass # No logging for open-source.
if not FLAGS.dump_dir:
print("ERROR: dump_dir flag is empty.", file=sys.stderr)
sys.exit(1)
print("tfdbg offline: FLAGS.dump_dir = %s" % FLAGS.dump_dir)
debug_dump = debug_data.DebugDumpDir(FLAGS.dump_dir,
validate=FLAGS.validate_graph)
cli = analyzer_cli.create_analyzer_ui(
debug_dump,
tensor_filters={"has_inf_or_nan": debug_data.has_inf_or_nan},
ui_type=FLAGS.ui_type)
title = "tfdbg offline @ %s" % FLAGS.dump_dir
cli.run_ui(title=title, title_color="black_on_white", init_command="lt")
