def on_run_end(self, request):
"""Overrides on-run-end callback.
Actions taken:
1) Load the debug dump.
2) Bring up the Analyzer CLI.
Args:
request: An instance of OnSessionInitRequest.
Returns:
An instance of OnSessionInitResponse.
"""
self._is_run_start = False
if request.performed_action == framework.OnRunStartAction.DEBUG_RUN:
partition_graphs = None
if request.run_metadata and request.run_metadata.partition_graphs:
partition_graphs = request.run_metadata.partition_graphs
elif request.client_graph_def:
partition_graphs = [request.client_graph_def]
if request.tf_error and not os.path.isdir(self._dump_root):
# It is possible that the dump root may not exist due to errors that
# have occurred prior to graph execution (e.g., invalid device
# assignments), in which case we will just raise the exception as the
# unwrapped Session does.
raise request.tf_error
debug_dump = debug_data.DebugDumpDir(
self._dump_root, partition_graphs=partition_graphs)
debug_dump.set_python_graph(self._sess.graph)
passed_filter = None
if self._active_tensor_filter:
if not debug_dump.find(
self._tensor_filters[self._active_tensor_filter], first_n=1):
# No dumped tensor passes the filter in this run. Clean up the dump
# directory and move on.
self._remove_dump_root()
return framework.OnRunEndResponse()
else:
# Some dumped tensor(s) from this run passed the filter.
passed_filter = self._active_tensor_filter
self._active_tensor_filter = None
self._prep_cli_for_run_end(debug_dump, request.tf_error, passed_filter)
self._run_start_response = self._launch_cli()
# Clean up the dump generated by this run.
self._remove_dump_root()
else:
# No debug information to show following a non-debug run() call.
self._run_start_response = None
# Return placeholder response that currently holds no additional
# information.
return framework.OnRunEndResponse()
def on_run_end(self, request):
"""Overrides on-run-end callback.
Actions taken:
1) Load the debug dump.
2) Bring up the Analyzer CLI.
Args:
request: An instance of OnSessionInitRequest.
Returns:
An instance of OnSessionInitResponse.
"""
self._is_run_start = False
if request.performed_action == framework.OnRunStartAction.DEBUG_RUN:
partition_graphs = None
if request.run_metadata and request.run_metadata.partition_graphs:
partition_graphs = request.run_metadata.partition_graphs
elif request.client_graph_def:
partition_graphs = [request.client_graph_def]
debug_dump = debug_data.DebugDumpDir(
self._dump_root, partition_graphs=partition_graphs)
debug_dump.set_python_graph(self._sess.graph)
passed_filter = None
if self._active_tensor_filter:
if not debug_dump.find(
self._tensor_filters[self._active_tensor_filter],
first_n=1):
# No dumped tensor passes the filter in this run. Clean up the dump
# directory and move on.
self._remove_dump_root()
return framework.OnRunEndResponse()
else:
# Some dumped tensor(s) from this run passed the filter.
passed_filter = self._active_tensor_filter
self._active_tensor_filter = None
self._prep_cli_for_run_end(debug_dump, request.tf_error,
passed_filter)
self._run_start_response = self._launch_cli()
# Clean up the dump generated by this run.
self._remove_dump_root()
else:
# No debug information to show following a non-debug run() call.
self._run_start_response = None
# Return placeholder response that currently holds no additional
# information.
return framework.OnRunEndResponse()
def on_run_end(self, request):
"""Override abstract on-run-end callback method."""
self._obs["on_run_end_count"] += 1
self._obs["performed_action"] = request.performed_action
return framework.OnRunEndResponse()
def on_run_end(self, request):
return framework.OnRunEndResponse()
def on_run_end(self, request):
"""See doc of BaseDebugWrapperSession.on_run_end."""
return framework.OnRunEndResponse()
def on_run_end(self, request):
"""Overrides on-run-end callback.
Actions taken:
1) Load the debug dump.
2) Bring up the Analyzer CLI.
Args:
request: An instance of OnSessionInitRequest.
Returns:
An instance of OnSessionInitResponse.
"""
if request.performed_action == framework.OnRunStartAction.DEBUG_RUN:
partition_graphs = None
if request.run_metadata and request.run_metadata.partition_graphs:
partition_graphs = request.run_metadata.partition_graphs
elif request.client_graph_def:
partition_graphs = [request.client_graph_def]
debug_dump = debug_data.DebugDumpDir(
self._dump_root, partition_graphs=partition_graphs)
if request.tf_error:
help_intro = cli_shared.get_error_intro(request.tf_error)
init_command = "help"
title_color = "red_on_white"
else:
help_intro = None
init_command = "lt"
title_color = "black_on_white"
if self._run_till_filter_pass:
if not debug_dump.find(
self._tensor_filters[self._run_till_filter_pass],
first_n=1):
# No dumped tensor passes the filter in this run. Clean up the dump
# directory and move on.
shutil.rmtree(self._dump_root)
return framework.OnRunEndResponse()
else:
# Some dumped tensor(s) from this run passed the filter.
init_command = "lt -f %s" % self._run_till_filter_pass
title_color = "red_on_white"
self._run_till_filter_pass = None
analyzer = analyzer_cli.DebugAnalyzer(debug_dump)
# Supply all the available tensor filters.
for filter_name in self._tensor_filters:
analyzer.add_tensor_filter(filter_name,
self._tensor_filters[filter_name])
run_end_cli = curses_ui.CursesUI()
run_end_cli.register_command_handler(
"list_tensors",
analyzer.list_tensors,
analyzer.get_help("list_tensors"),
prefix_aliases=["lt"])
run_end_cli.register_command_handler(
"node_info",
analyzer.node_info,
analyzer.get_help("node_info"),
prefix_aliases=["ni"])
run_end_cli.register_command_handler(
"list_inputs",
analyzer.list_inputs,
analyzer.get_help("list_inputs"),
prefix_aliases=["li"])
run_end_cli.register_command_handler(
"list_outputs",
analyzer.list_outputs,
analyzer.get_help("list_outputs"),
prefix_aliases=["lo"])
run_end_cli.register_command_handler(
"print_tensor",
analyzer.print_tensor,
analyzer.get_help("print_tensor"),
prefix_aliases=["pt"])
run_end_cli.register_command_handler(
"run",
self._run_end_run_command_handler,
"Helper command for incorrectly entered run command at the run-end "
"prompt.",
prefix_aliases=["r"])
# 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".
run_end_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.
run_end_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.
title = "run-end: " + self._run_description
if help_intro:
run_end_cli.set_help_intro(help_intro)
run_end_cli.run_ui(init_command=init_command,
title=title,
title_color=title_color)
# Clean up the dump directory.
shutil.rmtree(self._dump_root)
else:
print(
"No debug information to show following a non-debug run() call."
)
# Return placeholder response that currently holds no additional
# information.
return framework.OnRunEndResponse()
