def is_mkl_enabled(self):
major_version = int(tf.__version__.split(".")[0])
if major_version >= 2:
from tensorflow.python import _pywrap_util_port
return _pywrap_util_port.IsMklEnabled()
else:
return tf.pywrap_tensorflow.IsMklEnabled()
def get_mkl_enabled_flag():
mkl_enabled = False
major_version = int(tf.__version__.split(".")[0])
minor_version = int(tf.__version__.split(".")[1])
if major_version >= 2:
if minor_version < 5:
from tensorflow.python import _pywrap_util_port
else:
from tensorflow.python.util import _pywrap_util_port
onednn_enabled = int(os.environ.get('TF_ENABLE_ONEDNN_OPTS', '0'))
mkl_enabled = _pywrap_util_port.IsMklEnabled() or (onednn_enabled == 1)
else:
mkl_enabled = tf.pywrap_tensorflow.IsMklEnabled()
return mkl_enabled
def test_intel_tensorflow():
"""
Check if Intel version of TensorFlow is installed
"""
import tensorflow as tf
print("We are using Tensorflow version {}".format(tf.__version__))
major_version = int(tf.__version__.split(".")[0])
if major_version >= 2:
from tensorflow.python import _pywrap_util_port
print("Intel-optimizations (DNNL) enabled:",
_pywrap_util_port.IsMklEnabled())
else:
print("Intel-optimizations (DNNL) enabled:",
tf.pywrap_tensorflow.IsMklEnabled())
def get_mkl_enabled_flag():
""" Checks if the TensorFlow optimizations for Intel CPU architectures
are enabled. The source for this sanity check comes from:
https://www.intel.com/content/www/us/en/developer/articles/guide/optimization-for-tensorflow-installation-guide.html
Returns:
bool: true if Intel optimizations are enabled
"""
mkl_enabled = False
major_version = int(tf.__version__.split(".")[0])
minor_version = int(tf.__version__.split(".")[1])
if major_version >= 2:
if minor_version < 5:
from tensorflow.python import _pywrap_util_port
else:
from tensorflow.python.util import _pywrap_util_port
onednn_enabled = int(os.environ.get('TF_ENABLE_ONEDNN_OPTS', '0'))
mkl_enabled = _pywrap_util_port.IsMklEnabled() or (onednn_enabled == 1)
else:
mkl_enabled = tf.pywrap_tensorflow.IsMklEnabled()
return mkl_enabled
import os
import sys
import tensorflow as tf
os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices'
print("TensorFlow version: ", tf.__version__)
print("Num GPU Available: ",
len(tf.config.experimental.list_physical_devices('GPU')))
from tensorflow.python import _pywrap_util_port
print("MKL enabled:", _pywrap_util_port.IsMklEnabled())
tf.debugging.set_log_device_placement(False)
logdir = './logs/func2'
writer = tf.summary.create_file_writer(logdir)
@tf.function
def my_func(ax, bx):
return tf.matmul(ax, bx)
A = tf.constant([[1, 2], [3, 4]], dtype=tf.float32, name='A')
x = tf.constant([[5, 6], [7, 8]], dtype=tf.float32, name='x')
# Bracket the function call with
# tf.summary.trace_on() and tf.summary.trace_export()
tf.summary.trace_on(graph=True, profiler=False)
z = my_func(A, x)
