def _ConstructDebuggerPluginWithGrpcPort(context):
try:
# pylint: disable=line-too-long,g-import-not-at-top
from tensorboard.plugins.debugger import debugger_plugin as debugger_plugin_lib
from tensorboard.plugins.debugger import interactive_debugger_plugin as interactive_debugger_plugin_lib
# pylint: enable=line-too-long,g-import-not-at-top
except ImportError as err:
(unused_type, unused_value, traceback) = sys.exc_info()
six.reraise(
ImportError,
ImportError(err.message +
'\n\nTo use the debugger plugin, you need to have '
'gRPC installed:\n pip install grpcio'), traceback)
if FLAGS.debugger_port > 0:
interactive_plugin = (
interactive_debugger_plugin_lib.InteractiveDebuggerPlugin(context))
tf.logging.info('Starting Interactive Debugger Plugin at gRPC port %d',
FLAGS.debugger_data_server_grpc_port)
interactive_plugin.listen(FLAGS.debugger_port)
return interactive_plugin
elif FLAGS.debugger_data_server_grpc_port > 0:
noninteractive_plugin = debugger_plugin_lib.DebuggerPlugin(context)
tf.logging.info(
'Starting Non-interactive Debugger Plugin at gRPC port %d',
FLAGS.debugger_data_server_grpc_port)
noninteractive_plugin.listen(FLAGS.debugger_data_server_grpc_port)
return noninteractive_plugin
return None
def _ConstructDebuggerPluginWithGrpcPort(context):
try:
# pylint: disable=line-too-long,g-import-not-at-top
from tensorboard.plugins.debugger import debugger_plugin as debugger_plugin_lib
from tensorboard.plugins.debugger import interactive_debugger_plugin as interactive_debugger_plugin_lib
# pylint: enable=line-too-long,g-import-not-at-top
except ImportError as e:
e_type, e_value, e_traceback = sys.exc_info()
message = e.msg if hasattr(e,
'msg') else e.message # Handle py2 vs py3
if 'grpc' in message:
e_value = ImportError(
message + '\n\nTo use the debugger plugin, you need to have '
'gRPC installed:\n pip install grpcio')
six.reraise(e_type, e_value, e_traceback)
if FLAGS.debugger_port > 0:
interactive_plugin = (
interactive_debugger_plugin_lib.InteractiveDebuggerPlugin(context))
tf.logging.info('Starting Interactive Debugger Plugin at gRPC port %d',
FLAGS.debugger_data_server_grpc_port)
interactive_plugin.listen(FLAGS.debugger_port)
return interactive_plugin
elif FLAGS.debugger_data_server_grpc_port > 0:
noninteractive_plugin = debugger_plugin_lib.DebuggerPlugin(context)
tf.logging.info(
'Starting Non-interactive Debugger Plugin at gRPC port %d',
FLAGS.debugger_data_server_grpc_port)
noninteractive_plugin.listen(FLAGS.debugger_data_server_grpc_port)
return noninteractive_plugin
return None
def testHealthPillsPluginIsInactive(self):
plugin = debugger_plugin.DebuggerPlugin(
base_plugin.TBContext(
logdir=self.log_dir,
multiplexer=event_multiplexer.EventMultiplexer({})))
plugin.listen(self.debugger_data_server_grpc_port)
# The multiplexer lacks sampled health pills.
self.assertFalse(plugin.is_active())
def load(self, context):
"""Returns the debugger plugin, if possible.
Args:
context: The TBContext flags including `add_arguments`.
Returns:
A DebuggerPlugin instance or None if it couldn't be loaded.
"""
flags = context.flags
if flags.debugger_data_server_grpc_port > 0 or flags.debugger_port > 0:
# Verify that the required Python packages are installed.
try:
# pylint: disable=unused-import
import tensorflow # noqa: F401
except ImportError:
raise ImportError(
"To use the debugger plugin, you need to have TensorFlow installed:\n"
" pip install tensorflow"
)
if flags.debugger_data_server_grpc_port > 0:
from tensorboard.plugins.debugger import (
debugger_plugin as debugger_plugin_lib,
)
# debugger_data_server_grpc opens the non-interactive Debugger Plugin,
# which appears as health pills in the Graph Plugin.
noninteractive_plugin = debugger_plugin_lib.DebuggerPlugin(context)
logger.info(
"Starting Non-interactive Debugger Plugin at gRPC port %d",
flags.debugger_data_server_grpc_port,
)
noninteractive_plugin.listen(flags.debugger_data_server_grpc_port)
return noninteractive_plugin
elif flags.debugger_port > 0:
from tensorboard.plugins.debugger import (
interactive_debugger_plugin as interactive_debugger_plugin_lib,
)
interactive_plugin = interactive_debugger_plugin_lib.InteractiveDebuggerPlugin(
context
)
logger.info(
"Starting Interactive Debugger Plugin at gRPC port %d",
flags.debugger_data_server_grpc_port,
)
interactive_plugin.listen(flags.debugger_port)
return interactive_plugin
else:
# If neither the debugger_data_server_grpc_port flag or the grpc_port
# flag is specified, we instantiate a dummy plugin as a placeholder for
# the frontend. The dummy plugin will display a message indicating that
# the plugin is not active. It'll also display a command snippet to
# illustrate how to activate the interactive Debugger Plugin.
return InactiveDebuggerPlugin()
def testDebuggerDataServerNotStartedWhenPortIsNone(self):
"""Tests that the plugin starts no debugger data server if port is None."""
self.mock_debugger_data_server_class.reset_mock()
# Initialize a debugger plugin with no GRPC port provided.
debugger_plugin.DebuggerPlugin(self.context).get_plugin_apps()
# No debugger data server should have been started.
# assert_not_called is not available in Python 3.4.
self.assertFalse(self.mock_debugger_data_server_class.called)
def load(self, context):
"""Returns the debugger plugin, if possible.
Args:
context: The TBContext flags including `add_arguments`.
Returns:
A DebuggerPlugin instance or None if it couldn't be loaded.
"""
if not (context.flags.debugger_data_server_grpc_port > 0
or context.flags.debugger_port > 0):
return None
flags = context.flags
try:
# pylint: disable=g-import-not-at-top,unused-import
import tensorflow
except ImportError:
raise ImportError(
'To use the debugger plugin, you need to have TensorFlow installed:\n'
' pip install tensorflow')
try:
# pylint: disable=line-too-long,g-import-not-at-top
from tensorboard.plugins.debugger import debugger_plugin as debugger_plugin_lib
from tensorboard.plugins.debugger import interactive_debugger_plugin as interactive_debugger_plugin_lib
# pylint: enable=line-too-long,g-import-not-at-top
except ImportError as e:
e_type, e_value, e_traceback = sys.exc_info()
message = e.msg if hasattr(
e, 'msg') else e.message # Handle py2 vs py3
if 'grpc' in message:
e_value = ImportError(
message +
'\n\nTo use the debugger plugin, you need to have '
'gRPC installed:\n pip install grpcio')
six.reraise(e_type, e_value, e_traceback)
if flags.debugger_port > 0:
interactive_plugin = (interactive_debugger_plugin_lib.
InteractiveDebuggerPlugin(context))
logger.info('Starting Interactive Debugger Plugin at gRPC port %d',
flags.debugger_data_server_grpc_port)
interactive_plugin.listen(flags.debugger_port)
return interactive_plugin
elif flags.debugger_data_server_grpc_port > 0:
noninteractive_plugin = debugger_plugin_lib.DebuggerPlugin(context)
logger.info(
'Starting Non-interactive Debugger Plugin at gRPC port %d',
flags.debugger_data_server_grpc_port)
noninteractive_plugin.listen(flags.debugger_data_server_grpc_port)
return noninteractive_plugin
raise AssertionError()
def _ConstructDebuggerPluginWithGrpcPort(context):
try:
# pylint: disable=line-too-long,g-import-not-at-top
from tensorboard.plugins.debugger import debugger_plugin as debugger_plugin_lib
# pylint: enable=line-too-long,g-import-not-at-top
except ImportError as err:
(unused_type, unused_value, traceback) = sys.exc_info()
six.reraise(
ImportError,
ImportError(err.message +
"\n\nTo use the debugger plugin, you need to have "
"gRPC installed:\n pip install grpcio"), traceback)
tf.logging.info("Starting Debugger Plugin at gRPC port %d",
FLAGS.debugger_data_server_grpc_port)
debugger_plugin = debugger_plugin_lib.DebuggerPlugin(context)
debugger_plugin.listen(FLAGS.debugger_data_server_grpc_port)
return debugger_plugin
def setUp(self):
super(DebuggerPluginTestBase, self).setUp()
# Importing the debugger_plugin can sometimes unfortunately produce errors.
try:
# pylint: disable=g-import-not-at-top
from tensorboard.plugins.debugger import debugger_plugin
from tensorboard.plugins.debugger import debugger_server_lib
# pylint: enable=g-import-not-at-top
except Exception as e: # pylint: disable=broad-except
raise self.skipTest(
'Skipping test because importing some modules failed: %r' % e)
self.debugger_plugin_module = debugger_plugin
# Populate the log directory with debugger event for run '.'.
self.log_dir = self.get_temp_dir()
file_prefix = tf.compat.as_bytes(
os.path.join(self.log_dir, 'events.debugger'))
writer = tf.pywrap_tensorflow.EventsWriter(file_prefix)
device_name = '/job:localhost/replica:0/task:0/cpu:0'
writer.WriteEvent(
self._CreateEventWithDebugNumericSummary(
device_name=device_name,
op_name='layers/Matmul',
output_slot=0,
wall_time=42,
step=2,
list_of_values=(list(range(12)) +
[float(tf.float32.as_datatype_enum), 1.0, 3.0])))
writer.WriteEvent(
self._CreateEventWithDebugNumericSummary(
device_name=device_name,
op_name='layers/Matmul',
output_slot=1,
wall_time=43,
step=7,
list_of_values=(
list(range(12)) +
[float(tf.float64.as_datatype_enum), 2.0, 3.0, 3.0])))
writer.WriteEvent(
self._CreateEventWithDebugNumericSummary(
device_name=device_name,
op_name='logits/Add',
output_slot=0,
wall_time=1337,
step=7,
list_of_values=(list(range(12)) +
[float(tf.int32.as_datatype_enum), 2.0, 3.0, 3.0])))
writer.WriteEvent(
self._CreateEventWithDebugNumericSummary(
device_name=device_name,
op_name='logits/Add',
output_slot=0,
wall_time=1338,
step=8,
list_of_values=(list(range(12)) +
[float(tf.int16.as_datatype_enum), 0.0])))
writer.Close()
# Populate the log directory with debugger event for run 'run_foo'.
run_foo_directory = os.path.join(self.log_dir, 'run_foo')
os.mkdir(run_foo_directory)
file_prefix = tf.compat.as_bytes(
os.path.join(run_foo_directory, 'events.debugger'))
writer = tf.pywrap_tensorflow.EventsWriter(file_prefix)
writer.WriteEvent(
self._CreateEventWithDebugNumericSummary(
device_name=device_name,
op_name='layers/Variable',
output_slot=0,
wall_time=4242,
step=42,
list_of_values=(list(range(12)) +
[float(tf.int16.as_datatype_enum), 1.0, 8.0])))
writer.Close()
# Start a server that will receive requests and respond with health pills.
self.multiplexer = event_multiplexer.EventMultiplexer({
'.': self.log_dir,
'run_foo': run_foo_directory,
})
self.debugger_data_server_grpc_port = portpicker.pick_unused_port()
# Fake threading behavior so that threads are synchronous.
tf.test.mock.patch('threading.Thread.start', threading.Thread.run).start()
self.mock_debugger_data_server = tf.test.mock.Mock(
debugger_server_lib.DebuggerDataServer)
self.mock_debugger_data_server_class = tf.test.mock.Mock(
debugger_server_lib.DebuggerDataServer,
return_value=self.mock_debugger_data_server)
tf.test.mock.patch.object(
debugger_server_lib,
'DebuggerDataServer',
self.mock_debugger_data_server_class).start()
self.context = base_plugin.TBContext(
logdir=self.log_dir, multiplexer=self.multiplexer)
self.plugin = debugger_plugin.DebuggerPlugin(self.context)
self.plugin.listen(self.debugger_data_server_grpc_port)
wsgi_app = application.TensorBoardWSGIApp(
self.log_dir, [self.plugin], self.multiplexer, reload_interval=0,
path_prefix='')
self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse)
# The debugger data server should be started at the correct port.
self.mock_debugger_data_server_class.assert_called_once_with(
self.debugger_data_server_grpc_port, self.log_dir)
mock_debugger_data_server = self.mock_debugger_data_server
start = mock_debugger_data_server.start_the_debugger_data_receiving_server
self.assertEqual(1, start.call_count)
def ConstructDebuggerPluginWithGrpcPort(context):
debugger_plugin = debugger_plugin_lib.DebuggerPlugin(context)
if FLAGS.debugger_data_server_grpc_port is not None:
debugger_plugin.listen(FLAGS.debugger_data_server_grpc_port)
return debugger_plugin
