def test_int_gauge(self):
gauge = monitoring.IntGauge('test/gauge', 'test gauge')
gauge.get_cell().set(1)
self.assertEqual(gauge.get_cell().value(), 1)
gauge.get_cell().set(5)
self.assertEqual(gauge.get_cell().value(), 5)
gauge1 = monitoring.IntGauge('test/gauge1', 'test gauge1', 'label1')
gauge1.get_cell('foo').set(2)
self.assertEqual(gauge1.get_cell('foo').value(), 2)
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Helper functions for the Keras implementations of models."""
import multiprocessing
import os
import time
import tensorflow as tf
from absl import logging
from tensorflow.python.eager import monitoring
global_batch_size_gauge = monitoring.IntGauge(
'/tensorflow/training/global_batch_size', 'TF training global batch size')
first_batch_time_gauge = monitoring.IntGauge(
'/tensorflow/training/first_batch',
'TF training start/end time for first batch (unix epoch time in us.',
'type')
first_batch_start_time = first_batch_time_gauge.get_cell('start')
first_batch_end_time = first_batch_time_gauge.get_cell('end')
class BatchTimestamp(object):
"""A structure to store batch time stamp."""
def __init__(self, batch_index, timestamp):
self.batch_index = batch_index
self.timestamp = timestamp
"""Helper functions for the Keras implementations of models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import multiprocessing
import os
import time
from absl import logging
import tensorflow as tf
from tensorflow.python.eager import monitoring
global_batch_size_gauge = monitoring.IntGauge(
'/tensorflow/training/global_batch_size', 'TF training global batch size')
class BatchTimestamp(object):
"""A structure to store batch time stamp."""
def __init__(self, batch_index, timestamp):
self.batch_index = batch_index
self.timestamp = timestamp
def __repr__(self):
return "'BatchTimestamp<batch_index: {}, timestamp: {}>'".format(
self.batch_index, self.timestamp)
class TimeHistory(tf.keras.callbacks.Callback):
"""Callback for Keras models."""
'Counter for number of conversion attempts.')
_counter_conversion_success = monitoring.Counter(
'/tensorflow/lite/convert/success',
'Counter for number of successful conversions.')
_gauge_conversion_params = monitoring.StringGauge(
'/tensorflow/lite/convert/params',
'Gauge for keeping conversion parameters.', 'name')
_gauge_conversion_errors = monitoring.StringGauge(
'/tensorflow/lite/convert/errors',
'Gauge for collecting conversion errors. The value represents the error '
'message.', 'component', 'subcomponent', 'op_name', 'error_code')
_gauge_conversion_latency = monitoring.IntGauge(
'/tensorflow/lite/convert/latency', 'Conversion latency in ms.')
class TFLiteMetrics(metrics_interface.TFLiteMetricsInterface):
"""TFLite metrics helper for prod (borg) environment.
Attributes:
model_hash: A string containing the hash of the model binary.
model_path: A string containing the path of the model for debugging
purposes.
"""
def __init__(self,
model_hash: Optional[Text] = None,
model_path: Optional[Text] = None) -> None:
del self # Temporarily removing self until parameter logic is implemented.
if model_hash and not model_path or not model_hash and model_path:
"""Helper functions for the Keras implementations of models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import multiprocessing
import os
import time
from absl import logging
import tensorflow as tf
from tensorflow.python.eager import monitoring
global_batch_size_gauge = monitoring.IntGauge(
'/tensorflow/training/global_batch_size', 'TF training global batch size')
first_batch_start_time = monitoring.IntGauge(
'/tensorflow/training/first_batch_start',
'TF training start time (unix epoch time in us.')
class BatchTimestamp(object):
"""A structure to store batch time stamp."""
def __init__(self, batch_index, timestamp):
self.batch_index = batch_index
self.timestamp = timestamp
def __repr__(self):
return "'BatchTimestamp<batch_index: {}, timestamp: {}>'".format(
self.batch_index, self.timestamp)
