tfdbg.LocalCLIDebugHook

tfdbg.LocalCLIDebugHook

class tfdbg.LocalCLIDebugHook

Defined in tensorflow/python/debug/wrappers/hooks.py.

See the guide: TensorFlow Debugger > Session wrapper class and SessionRunHook implementations

Command-line-interface debugger hook.

Can be used as a monitor/hook for tf.train.MonitoredSessions and tf.contrib.learn's Estimators and Experiments.

Properties

graph

graph_def

sess_str

session

Methods

__init__

__init__(
    ui_type='curses',
    dump_root=None,
    thread_name_filter=None
)

Create a local debugger command-line interface (CLI) hook.

Args:

• ui_type: (str) user-interface type.
• dump_root: (str) optional path to the dump root directory. Must be a directory that does not exist or an empty directory. If the directory does not exist, it will be created by the debugger core during debug run() calls and removed afterwards.
• thread_name_filter: Regular-expression white list for threads on which the wrapper session will be active. See doc of BaseDebugWrapperSession for more details.

__enter__

__enter__()

__exit__

__exit__(
    exec_type,
    exec_value,
    exec_tb
)

add_tensor_filter

add_tensor_filter(
    filter_name,
    tensor_filter
)

Add a tensor filter.

See doc of LocalCLIDebugWrapperSession.add_tensor_filter() for details. Override default behavior to accommodate the possibility of this method being called prior to the initialization of the underlying LocalCLIDebugWrapperSession object.

Args:

• filter_name: See doc of LocalCLIDebugWrapperSession.add_tensor_filter() for details.
• tensor_filter: See doc of LocalCLIDebugWrapperSession.add_tensor_filter() for details.

after_create_session

after_create_session(
    session,
    coord
)

Called when new TensorFlow session is created.

This is called to signal the hooks that a new session has been created. This has two essential differences with the situation in which begin is called:

• When this is called, the graph is finalized and ops can no longer be added to the graph.
• This method will also be called as a result of recovering a wrapped session, not only at the beginning of the overall session.

Args:

• session: A TensorFlow Session that has been created.
• coord: A Coordinator object which keeps track of all threads.

after_run

after_run(
    run_context,
    run_values
)

as_default

as_default()

before_run

before_run(run_context)

begin

begin()

close

close()

end

end(session)

Called at the end of session.

The session argument can be used in case the hook wants to run final ops, such as saving a last checkpoint.

If session.run() raises exception other than OutOfRangeError or StopIteration then end() is not called. Note the difference between end() and after_run() behavior when session.run() raises OutOfRangeError or StopIteration. In that case end() is called but after_run() is not called.

Args:

• session: A TensorFlow Session that will be soon closed.

invoke_node_stepper

invoke_node_stepper(
    node_stepper,
    restore_variable_values_on_exit=True
)

Overrides method in base class to implement interactive node stepper.

Args:

• node_stepper: (stepper.NodeStepper) The underlying NodeStepper API object.
• 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.

on_run_end

on_run_end(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.

on_run_start

on_run_start(request)

Overrides on-run-start callback.

Invoke the CLI to let user choose what action to take: run / invoke_stepper.

Args:

• request: An instance of OnRunStartRequest.

Returns:

An instance of OnRunStartResponse.

on_session_init

on_session_init(request)

Overrides on-session-init callback.

Args:

• request: An instance of OnSessionInitRequest.

Returns:

An instance of OnSessionInitResponse.

partial_run

partial_run(
    handle,
    fetches,
    feed_dict=None
)

partial_run_setup

partial_run_setup(
    fetches,
    feeds=None
)

Sets up the feeds and fetches for partial runs in the session.

run

run(
    fetches,
    feed_dict=None,
    options=None,
    run_metadata=None
)

Wrapper around Session.run() that inserts tensor watch options.

Args:

• fetches: Same as the fetches arg to regular Session.run().
• feed_dict: Same as the feed_dict arg to regular Session.run().
• options: Same as the options arg to regular Session.run().
• run_metadata: Same as the run_metadata arg to regular Session.run().

Returns:

Simply forwards the output of the wrapped Session.run() call.

Raises:

• ValueError: On invalid OnRunStartAction value.