Python tensorflow.python.client.session.run() Examples
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Example #1
Source File: framework.py From lambda-packs with MIT License | 6 votes |
def __init__(self, fetches, feed_dict, run_options, run_metadata, run_call_count): """Constructor of `OnRunStartRequest`. Args: fetches: Fetch targets of the run() call. feed_dict: The feed dictionary to the run() call. run_options: RunOptions input to the run() call. run_metadata: RunMetadata input to the run() call. The above four arguments are identical to the input arguments to the run() method of a non-wrapped TensorFlow session. run_call_count: 1-based count of how many run calls (including this one) has been invoked. """ self.fetches = fetches self.feed_dict = feed_dict self.run_options = run_options self.run_metadata = run_metadata self.run_call_count = run_call_count
Example #2
Source File: framework.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
def invoke_node_stepper(self, node_stepper, restore_variable_values_on_exit=True): """Callback invoked when the client intends to step through graph nodes. Args: node_stepper: (stepper.NodeStepper) An instance of NodeStepper to be used in this stepping session. restore_variable_values_on_exit: (bool) Whether any variables whose values have been altered during this node-stepper invocation should be restored to their old values when this invocation ends. Returns: The same return values as the `Session.run()` call on the same fetches as the NodeStepper. """
Example #3
Source File: framework.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
def __init__(self, fetches, feed_dict, run_options, run_metadata, run_call_count, is_callable_runner=False): """Constructor of `OnRunStartRequest`. Args: fetches: Fetch targets of the run() call. feed_dict: The feed dictionary to the run() call. run_options: RunOptions input to the run() call. run_metadata: RunMetadata input to the run() call. The above four arguments are identical to the input arguments to the run() method of a non-wrapped TensorFlow session. run_call_count: 1-based count of how many run calls (including this one) has been invoked. is_callable_runner: (bool) whether a runner returned by Session.make_callable is being run. """ self.fetches = fetches self.feed_dict = feed_dict self.run_options = run_options self.run_metadata = run_metadata self.run_call_count = run_call_count self.is_callable_runner = is_callable_runner
Example #4
Source File: framework.py From deep_image_model with Apache License 2.0 | 6 votes |
def on_run_start(self, request): """Callback invoked on run() calls to the debug-wrapper session. This is a blocking callback. The invocation happens after the wrapper's run() call is entered, after an increment of run call counter. Args: request: (OnRunStartRequest) callback request object carrying information about the run call such as the fetches, feed dict, run options, run metadata, and how many run() calls to this wrapper session has occurred. Returns: An instance of OnRunStartResponse, carrying information to 1) direct the wrapper session to perform a specified action (e.g., run with or without debug tensor watching, invoking the stepper.) 2) debug URLs used to watch the tensors. """ pass
Example #5
Source File: framework.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
def _prepare_run_watch_config(self, fetches, feed_dict): """Get the debug_urls, and node/op whitelists for the current run() call. Args: fetches: Same as the `fetches` argument to `Session.run()`. feed_dict: Same as the `feed_dict argument` to `Session.run()`. Returns: debug_urls: (str or list of str) Debug URLs for the current run() call. Currently, the list consists of only one URL that is a file:// URL. watch_options: (WatchOptions) The return value of a watch_fn, containing options including debug_ops, and whitelists. """ debug_urls = self.prepare_run_debug_urls(fetches, feed_dict) if self._watch_fn is None: watch_options = WatchOptions() else: watch_options = self._watch_fn(fetches, feed_dict) if isinstance(watch_options, tuple): # For legacy return type (tuples). watch_options = WatchOptions(*watch_options) return debug_urls, watch_options
Example #6
Source File: profile_context.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
def __enter__(self): self.old_run = getattr(session.BaseSession, 'run', None) self.old_init = getattr(session.BaseSession, '__init__', None) if not self.old_run: raise errors.InternalError(None, None, 'BaseSession misses run method.') elif not self.old_init: raise errors.InternalError(None, None, 'BaseSession misses __init__ method.') elif getattr(session.BaseSession, '_profiler_run_internal', None): raise errors.InternalError(None, None, 'Already in context or context not cleaned.') elif getattr(session.BaseSession, '_profiler_init_internal', None): raise errors.InternalError(None, None, 'Already in context or context not cleaned.') else: setattr(session.BaseSession, 'run', _profiled_run) setattr(session.BaseSession, '__init__', _profiled_init) setattr(session.BaseSession, '_profiler_run_internal', self.old_run) setattr(session.BaseSession, '_profiler_init_internal', self.old_init) setattr(session.BaseSession, 'profile_context', self) return self
Example #7
Source File: framework.py From deep_image_model with Apache License 2.0 | 6 votes |
def __init__(self, fetches, feed_dict, run_options, run_metadata, run_call_count): """Constructor of OnRunStartRequest. Args: fetches: Fetch targets of the run() call. feed_dict: The feed dictionary to the run() call. run_options: RunOptions input to the run() call. run_metadata: RunMetadata input to the run() call. The above four arguments are identical to the input arguments to the run() method of a non-wrapped TensorFlow session. run_call_count: 1-based count of how many run calls (including this one) has been invoked. """ self.fetches = fetches self.feed_dict = feed_dict self.run_options = run_options self.run_metadata = run_metadata self.run_call_count = run_call_count
Example #8
Source File: saved_model_cli.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
def run(args): """Function triggered by run command. Args: args: A namespace parsed from command line. Raises: AttributeError: An error when neither --inputs nor --input_exprs is passed to run command. """ if not args.inputs and not args.input_exprs: raise AttributeError( 'At least one of --inputs and --input_exprs must be required') tensor_key_feed_dict = load_inputs_from_input_arg_string( args.inputs, args.input_exprs) run_saved_model_with_feed_dict(args.dir, args.tag_set, args.signature_def, tensor_key_feed_dict, args.outdir, args.overwrite, tf_debug=args.tf_debug)
Example #9
Source File: framework.py From keras-lambda with MIT License | 6 votes |
def __init__(self, fetches, feed_dict, run_options, run_metadata, run_call_count): """Constructor of `OnRunStartRequest`. Args: fetches: Fetch targets of the run() call. feed_dict: The feed dictionary to the run() call. run_options: RunOptions input to the run() call. run_metadata: RunMetadata input to the run() call. The above four arguments are identical to the input arguments to the run() method of a non-wrapped TensorFlow session. run_call_count: 1-based count of how many run calls (including this one) has been invoked. """ self.fetches = fetches self.feed_dict = feed_dict self.run_options = run_options self.run_metadata = run_metadata self.run_call_count = run_call_count
Example #10
Source File: framework.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def invoke_node_stepper(self, node_stepper, restore_variable_values_on_exit=True): """Callback invoked when the client intends to step through graph nodes. Args: node_stepper: (stepper.NodeStepper) An instance of NodeStepper to be used in this stepping session. restore_variable_values_on_exit: (bool) Whether any variables whose values have been altered during this node-stepper invocation should be restored to their old values when this invocation ends. Returns: The same return values as the `Session.run()` call on the same fetches as the NodeStepper. """
Example #11
Source File: framework.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def on_run_start(self, request): """Callback invoked on run() calls to the debug-wrapper session. This is a blocking callback. The invocation happens after the wrapper's run() call is entered, after an increment of run call counter. Args: request: (`OnRunStartRequest`) callback request object carrying information about the run call such as the fetches, feed dict, run options, run metadata, and how many `run()` calls to this wrapper session have occurred. Returns: An instance of `OnRunStartResponse`, carrying information to 1) direct the wrapper session to perform a specified action (e.g., run with or without debug tensor watching, invoking the stepper.) 2) debug URLs used to watch the tensors. """
Example #12
Source File: framework.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def __init__(self, fetches, feed_dict, run_options, run_metadata, run_call_count): """Constructor of `OnRunStartRequest`. Args: fetches: Fetch targets of the run() call. feed_dict: The feed dictionary to the run() call. run_options: RunOptions input to the run() call. run_metadata: RunMetadata input to the run() call. The above four arguments are identical to the input arguments to the run() method of a non-wrapped TensorFlow session. run_call_count: 1-based count of how many run calls (including this one) has been invoked. """ self.fetches = fetches self.feed_dict = feed_dict self.run_options = run_options self.run_metadata = run_metadata self.run_call_count = run_call_count
Example #13
Source File: saved_model_cli.py From lambda-packs with MIT License | 6 votes |
def run(args): """Function triggered by run command. Args: args: A namespace parsed from command line. Raises: AttributeError: An error when neither --inputs nor --input_exprs is passed to run command. """ if not args.inputs and not args.input_exprs: raise AttributeError( 'At least one of --inputs and --input_exprs must be required') tensor_key_feed_dict = load_inputs_from_input_arg_string( args.inputs, args.input_exprs) run_saved_model_with_feed_dict(args.dir, args.tag_set, args.signature_def, tensor_key_feed_dict, args.outdir, args.overwrite, tf_debug=args.tf_debug)
Example #14
Source File: framework.py From lambda-packs with MIT License | 6 votes |
def _prepare_run_watch_config(self, fetches, feed_dict): """Get the debug_urls, and node/op whitelists for the current run() call. Args: fetches: Same as the `fetches` argument to `Session.run()`. feed_dict: Same as the `feed_dict argument` to `Session.run()`. Returns: debug_urls: (str or list of str) Debug URLs for the current run() call. Currently, the list consists of only one URL that is a file:// URL. watch_options: (WatchOptions) The return value of a watch_fn, containing options including debug_ops, and whitelists. """ debug_urls = self.prepare_run_debug_urls(fetches, feed_dict) if self._watch_fn is None: watch_options = WatchOptions() else: watch_options = self._watch_fn(fetches, feed_dict) if isinstance(watch_options, tuple): # For legacy return type (tuples). watch_options = WatchOptions(*watch_options) return debug_urls, watch_options
Example #15
Source File: framework.py From keras-lambda with MIT License | 6 votes |
def on_run_start(self, request): """Callback invoked on run() calls to the debug-wrapper session. This is a blocking callback. The invocation happens after the wrapper's run() call is entered, after an increment of run call counter. Args: request: (`OnRunStartRequest`) callback request object carrying information about the run call such as the fetches, feed dict, run options, run metadata, and how many `run()` calls to this wrapper session have occurred. Returns: An instance of `OnRunStartResponse`, carrying information to 1) direct the wrapper session to perform a specified action (e.g., run with or without debug tensor watching, invoking the stepper.) 2) debug URLs used to watch the tensors. """
Example #16
Source File: framework.py From keras-lambda with MIT License | 6 votes |
def invoke_node_stepper(self, node_stepper, restore_variable_values_on_exit=True): """Callback invoked when the client intends to step through graph nodes. Args: node_stepper: (stepper.NodeStepper) An instance of NodeStepper to be used in this stepping session. restore_variable_values_on_exit: (bool) Whether any variables whose values have been altered during this node-stepper invocation should be restored to their old values when this invocation ends. Returns: The same return values as the `Session.run()` call on the same fetches as the NodeStepper. """
Example #17
Source File: framework.py From lambda-packs with MIT License | 6 votes |
def invoke_node_stepper(self, node_stepper, restore_variable_values_on_exit=True): """Callback invoked when the client intends to step through graph nodes. Args: node_stepper: (stepper.NodeStepper) An instance of NodeStepper to be used in this stepping session. restore_variable_values_on_exit: (bool) Whether any variables whose values have been altered during this node-stepper invocation should be restored to their old values when this invocation ends. Returns: The same return values as the `Session.run()` call on the same fetches as the NodeStepper. """
Example #18
Source File: framework.py From lambda-packs with MIT License | 6 votes |
def on_run_start(self, request): """Callback invoked on run() calls to the debug-wrapper session. This is a blocking callback. The invocation happens after the wrapper's run() call is entered, after an increment of run call counter. Args: request: (`OnRunStartRequest`) callback request object carrying information about the run call such as the fetches, feed dict, run options, run metadata, and how many `run()` calls to this wrapper session have occurred. Returns: An instance of `OnRunStartResponse`, carrying information to 1) direct the wrapper session to perform a specified action (e.g., run with or without debug tensor watching, invoking the stepper.) 2) debug URLs used to watch the tensors. """
Example #19
Source File: framework.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def on_run_end(self, request): """Callback invoked on run() calls to the debug-wrapper session. This is a blocking callback. The invocation happens right before the wrapper exits its run() call. Args: request: (`OnRunEndRequest`) callback request object carrying information such as the actual action performed by the session wrapper for the run() call. Returns: An instance of `OnRunStartResponse`. """
Example #20
Source File: framework.py From keras-lambda with MIT License | 5 votes |
def __init__(self, sess): """Constructor of `BaseDebugWrapperSession`. Args: sess: An (unwrapped) TensorFlow session instance. Raises: ValueError: On invalid `OnSessionInitAction` value. NotImplementedError: If a non-DirectSession sess object is received. """ _check_type(sess, session.BaseSession) # TODO(cais): Remove this check once tfdbg is integrated with GrpcSession. if sess.sess_str: raise NotImplementedError( "Non-DirectSession support is not available from TensorFlow " "Debugger yet (sess_str=%s)" % sess.sess_str) # The session being wrapped. self._sess = sess # Keeps track of number of run calls that have been performed on this # debug-wrapper session. self._run_call_count = 0 # Invoke on-session-init callback. response = self.on_session_init(OnSessionInitRequest(self._sess)) _check_type(response, OnSessionInitResponse) if response.action == OnSessionInitAction.PROCEED: pass elif response.action == OnSessionInitAction.REMOTE_INSTR_LOOP: # TODO(cais): Implement REMOTE_INSTR_LOOP raise NotImplementedError( "OnSessionInitAction REMOTE_INSTR_LOOP has not been " "implemented.") else: raise ValueError( "Invalid OnSessionInitAction value: %s" % response.action)
Example #21
Source File: framework.py From keras-lambda with MIT License | 5 votes |
def _prepare_run_watch_config(self, fetches, feed_dict): """Get the debug_urls, and node/op whitelists for the current run() call. Args: fetches: Same as the `fetches` argument to `Session.run()`. feed_dict: Same as the `feed_dict argument` to `Session.run()`. Returns: debug_urls: (str or list of str) Debug URLs for the current run() call. Currently, the list consists of only one URL that is a file:// URL. debug_ops: (str or list of str) Debug op(s) to be used by the debugger. node_name_regex_whitelist: (str or regex) Regular-expression whitelist for node name. Same as the same-name argument to debug_utils.watch_graph. op_type_regex_whitelist: (str or regex) Regular-expression whitelist for op type. Same as the same-name argument to debug_utils.watch_graph. """ debug_urls = self._prepare_run_debug_urls(fetches, feed_dict) debug_ops = "DebugIdentity" node_name_regex_whitelist = None op_type_regex_whitelist = None if self._watch_fn is not None: debug_ops, node_name_regex_whitelist, op_type_regex_whitelist = ( self._watch_fn(fetches, feed_dict)) return (debug_urls, debug_ops, node_name_regex_whitelist, op_type_regex_whitelist)
Example #22
Source File: framework.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def __init__(self, sess, watch_fn=None, thread_name_filter=None, pass_through_operrors=False): """Constructor of DumpingDebugWrapperSession. Args: sess: The TensorFlow `Session` object being wrapped. watch_fn: (`Callable`) A Callable that maps the fetches and feeds of a debugged `Session.run()` call to `WatchOptions.` * Args: * `fetches`: the fetches to the `Session.run()` call. * `feeds`: the feeds to the `Session.run()` call. * Returns: (`tf_debug.WatchOptions`) An object containing debug options including the debug ops to use, the node names, op types and/or tensor data types to watch, etc. See the documentation of `tf_debug.WatchOptions` for more details. thread_name_filter: Regular-expression white list for threads on which the wrapper session will be active. See doc of `BaseDebugWrapperSession` for more details. pass_through_operrors: If true, all captured OpErrors will be propagated. By default this captures all OpErrors. Raises: TypeError: If a non-None `watch_fn` is specified and it is not callable. """ BaseDebugWrapperSession.__init__( self, sess, thread_name_filter=thread_name_filter, pass_through_operrors=pass_through_operrors) self._watch_fn = None if watch_fn is not None: if not callable(watch_fn): raise TypeError("watch_fn is not callable") self._watch_fn = watch_fn
Example #23
Source File: framework.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def prepare_run_debug_urls(self, fetches, feed_dict): """Abstract method to be implemented by concrete subclasses. This method prepares the run-specific debug URL(s). Args: fetches: Same as the `fetches` argument to `Session.run()` feed_dict: Same as the `feed_dict` argument to `Session.run()` Returns: debug_urls: (`str` or `list` of `str`) Debug URLs to be used in this `Session.run()` call. """
Example #24
Source File: framework.py From keras-lambda with MIT License | 5 votes |
def _prepare_run_debug_urls(self, fetches, feed_dict): """Abstract method to be implemented by concrete subclasses. This method prepares the run-specific debug URL(s). Args: fetches: Same as the `fetches` argument to `Session.run()` feed_dict: Same as the `feed_dict` argument to `Session.run()` Returns: debug_urls: (`str` or `list` of `str`) Debug URLs to be used in this `Session.run()` call. """
Example #25
Source File: framework.py From keras-lambda with MIT License | 5 votes |
def __init__(self, sess, watch_fn=None): """Constructor of DumpingDebugWrapperSession. Args: sess: The TensorFlow `Session` object being wrapped. watch_fn: (`Callable`) A Callable of the following signature: ``` def watch_fn(fetches, feeds): # Args: # fetches: the fetches to the `Session.run()` call. # feeds: the feeds to the `Session.run()` call. # # Returns: (node_name_regex_whitelist, op_type_regex_whitelist) # debug_ops: (str or list of str) Debug op(s) to be used by the # debugger in this run() call. # node_name_regex_whitelist: Regular-expression whitelist for node # name. Same as the corresponding arg to `debug_util.watch_graph`. # op_type_regex_whiteslit: Regular-expression whitelist for op type. # Same as the corresponding arg to `debug_util.watch_graph`. # # Both or either can be None. If both are set, the two whitelists # will operate in a logical AND relation. This is consistent with # `debug_utils.watch_graph()`. ``` Raises: TypeError: If a non-None `watch_fn` is specified and it is not callable. """ BaseDebugWrapperSession.__init__(self, sess) self._watch_fn = None if watch_fn is not None: if not callable(watch_fn): raise TypeError("watch_fn is not callable") self._watch_fn = watch_fn
Example #26
Source File: profile_context.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def add_auto_profiling(self, cmd, options, profile_steps): """Traces and profiles at some session run steps. Args: cmd: The profiling commands. (i.e. scope, op, python, graph) options: The profiling options. profile_steps: A list/set of integers. The profiling command and options will be run automatically at these integer steps. Each step is a session.run. """ self._auto_profiles.append((cmd, options, profile_steps[:])) self._slow_path_steps |= set(profile_steps) self._trace_steps |= set(profile_steps)
Example #27
Source File: profile_context.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def __exit__(self, exec_type, exec_value, exec_tb): print_mdl.DeleteProfiler() setattr(session.BaseSession, 'run', self.old_run) setattr(session.BaseSession, '__init__', self.old_init) setattr(session.BaseSession, '_profiler_run_internal', None) setattr(session.BaseSession, '_profiler_init_internal', None) setattr(session.BaseSession, 'profile_context', None)
Example #28
Source File: framework.py From keras-lambda with MIT License | 5 votes |
def __init__(self, action, debug_urls, debug_ops="DebugIdentity", node_name_regex_whitelist=None, op_type_regex_whitelist=None): """Constructor of `OnRunStartResponse`. Args: action: (`OnRunStartAction`) the action actually taken by the wrapped session for the run() call. debug_urls: (`list` of `str`) debug_urls used in watching the tensors during the run() call. debug_ops: (`str` or `list` of `str`) Debug op(s) to be used by the debugger. node_name_regex_whitelist: Regular-expression whitelist for node name. op_type_regex_whitelist: Regular-expression whitelist for op type. """ _check_type(action, str) self.action = action _check_type(debug_urls, list) self.debug_urls = debug_urls self.debug_ops = debug_ops self.node_name_regex_whitelist = node_name_regex_whitelist self.op_type_regex_whitelist = op_type_regex_whitelist
Example #29
Source File: framework.py From keras-lambda with MIT License | 5 votes |
def __init__(self, performed_action, run_metadata=None, client_graph_def=None, tf_error=None): """Constructor for `OnRunEndRequest`. Args: performed_action: (`OnRunStartAction`) Actually-performed action by the debug-wrapper session. run_metadata: run_metadata output from the run() call (if any). client_graph_def: (GraphDef) GraphDef from the client side, i.e., from the python front end of TensorFlow. Can be obtained with session.graph.as_graph_def(). tf_error: (errors.OpError subtypes) TensorFlow OpError that occurred during the run (if any). """ _check_type(performed_action, str) self.performed_action = performed_action if run_metadata is not None: _check_type(run_metadata, config_pb2.RunMetadata) self.run_metadata = run_metadata self.client_graph_def = client_graph_def self.tf_error = tf_error
Example #30
Source File: framework.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def __init__(self, sess, watch_fn=None): """Constructor of DumpingDebugWrapperSession. Args: sess: The TensorFlow `Session` object being wrapped. watch_fn: (`Callable`) A Callable of the following signature: ``` def watch_fn(fetches, feeds): # Args: # fetches: the fetches to the `Session.run()` call. # feeds: the feeds to the `Session.run()` call. # # Returns: (node_name_regex_whitelist, op_type_regex_whitelist) # debug_ops: (str or list of str) Debug op(s) to be used by the # debugger in this run() call. # node_name_regex_whitelist: Regular-expression whitelist for node # name. Same as the corresponding arg to `debug_util.watch_graph`. # op_type_regex_whiteslit: Regular-expression whitelist for op type. # Same as the corresponding arg to `debug_util.watch_graph`. # # Both or either can be None. If both are set, the two whitelists # will operate in a logical AND relation. This is consistent with # `debug_utils.watch_graph()`. ``` Raises: TypeError: If a non-None `watch_fn` is specified and it is not callable. """ BaseDebugWrapperSession.__init__(self, sess) self._watch_fn = None if watch_fn is not None: if not callable(watch_fn): raise TypeError("watch_fn is not callable") self._watch_fn = watch_fn