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()