A hook that streams debugger-related events to any grpc_debug_server.
For example, the debugger data server is a grpc_debug_server. The debugger data server writes debugger-related events it receives via GRPC to logdir. This enables debugging features in Tensorboard such as health pills.
When the arguments of debug_utils.watch_graph changes, strongly consider changing arguments here too so that features are available to tflearn users.
Can be used as a monitor/hook for
__init__( grpc_debug_server_addresses, watch_fn=None, thread_name_filter=None, log_usage=True )
Constructs a GrpcDebugHook.
str) A list of the gRPC debug server addresses, in the format of
, with or without the "grpc://" prefix. For example: ["localhost:7000", "192.168.0.2:8000"]
watch_fn: A function that allows for customizing which ops to watch at which specific steps. See doc of
thread_name_filter: Regular-expression white list for threads on which the wrapper session will be active. See doc of
BaseDebugWrapperSessionfor more details.
log_usage: (bool) Whether usage is to be logged.
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.
session: A TensorFlow Session that has been created.
coord: A Coordinator object which keeps track of all threads.
after_run( run_context, run_values )
Called after each call to run().
run_values argument contains results of requested ops/tensors by
run_context argument is the same one send to
run_context.request_stop() can be called to stop the iteration.
session.run() raises any exceptions then
after_run() is not called.
run_values: A SessionRunValues object.
Called right before a session is run.
run_context: A session_run_hook.SessionRunContext. Encapsulates information on the run.
A session_run_hook.SessionRunArgs object.
Called once before using the session.
When called, the default graph is the one that will be launched in the
session. The hook can modify the graph by adding new operations to it.
begin() call the graph will be finalized and the other callbacks
can not modify the graph anymore. Second call of
begin() on the same
graph, should not change the graph.
Called at the end of session.
session argument can be used in case the hook wants to run final ops,
such as saving a last checkpoint.
session.run() raises exception other than OutOfRangeError or
end() is not called.
Note the difference between
after_run() behavior when
session.run() raises OutOfRangeError or StopIteration. In that case
end() is called but
after_run() is not called.
session: A TensorFlow Session that will be soon closed.