Guide to TensorFlow* Runtime optimizations for CPU

Published: 07/30/2020  

Last Updated: 11/29/2022

Overview

Runtime settings can greatly affect the performance of TensorFlow* workloads running on CPUs, particularly regarding threading, data layout.  

OpenMP* and TensorFlow both have settings that should be considered for their effect on performance. The Intel® oneAPI Deep Neural Network Library (oneDNN) within the Intel® Optimization for TensorFlow* uses OpenMP settings as environment variables to affect performance on Intel CPUs. TensorFlow has a class (ConfigProto or config depending on the version) with settings that affect performance.

Most of the recommendations work on both official x86-64 TensorFlow and  Intel® Optimization for TensorFlow. Some recommendations such as OpenMP tuning only apply to Intel® Optimization for TensorFlow

This guide will describe how to set the running variables to optimize Tensorflow* for CPU. 

OpenMP* settings descriptions

  • OMP_NUM_THREADS
    • Maximum number of threads to use for OpenMP parallel regions if no other value is specified in the application.
    • Recommend: start with the number of physical cores/sockets on the test system, and try increasing and decreasing.
  • KMP_BLOCKTIME
    • Time, in milliseconds, that a thread should wait, after completing the execution of a parallel region, before sleeping.
    • Recommend: start with 1 and try increasing.
  • KMP_AFFINITY
    • Restricts execution of certain threads to a subset of the physical processing units in a multiprocessor computer. Only valid if Hyperthreading is enabled.
    • Recommend: granularity=fine,verbose,compact,1,0
  • KMP_SETTINGS
    • Enables (TRUE) or disables (FALSE) printing of OpenMP run-time library environment variables during execution.
    • Recommend: Start with TRUE to ensure settings are being utilized, then use as needed.

How to apply OpenMP settings

These settings are applied as environment variables by two methods: 

  • Shell
    • Example:
export OMP_NUM_THREADS=16
  • Python code
    • Example:
import os
os.environ["OMP_NUM_THREADS"] = “16”

TensorFlow* settings

  • intra_op_parallelism_threads
    • Number of threads used within an individual op for parallelism.
    • Recommend: start with the number of cores/sockets on the test system, and try increasing and decreasing.
  • inter_op_parallelism_threads
    • Number of threads used for parallelism between independent operations.
    • Recommend: start with the number of physical cores on the test system, and try increasing and decreasing.
  • device_count
    • Maximum number of devices (CPUs in this case) to use.
    • Recommend: start with the number of cores/sockets on the test system, and try increasing and decreasing.
  • allow_soft_placement
    • Set to True/enabled to facilitate operations to be placed on CPU instead of GPU.

How to apply TensorFlow settings

These settings are applied in Python* code using Config Proto or config

  • Example in TensorFlow version 1.X:
import tensorflow as tf
config = tf.ConfigProto(intra_op_parallelism_threads=16, inter_op_parallelism_threads=2, allow_soft_placement=True, device_count = {'CPU': 16})
session = tf.Session(config=config)
  • Example in TensorFlow 2.X:
import tensorflow as tf
tf.config.threading.set_inter_op_parallelism_threads() 
tf.config.threading.set_intra_op_parallelism_threads()
tf.config.set_soft_device_placement(enabled)

Intel® oneDNN enabling settings

TensorFlow* is highly optimized with Intel® oneAPI Deep Neural Network Library (oneDNN) on CPU. The oneDNN optimizations are now available both in the official x86-64 TensorFlow binary and Intel® Optimization for TensorFlow* since v2.5.

  • Users can enable oneDNN optimizations by setting the environment variable TF_ENABLE_ONEDNN_OPTS=1 for the official x86-64 TensorFlow for v2.5-v2.8. Since v2.9,  no environment setting is needed as oneDNN is default DNN library in official x86-64 TensorFlow.
export TF_ENABLE_ONEDNN_OPTS=1

 

  • Users could enable/disable usage of oneDNN blocked data format in Tensorflow by TF_ENABLE_MKL_NATIVE_FORMAT environment variable. By exporting TF_ENABLE_MKL_NATIVE_FORMAT=0, TensorFlow will use oneDNN blocked data format instead. Please check oneDNN memory format for more information about oneDNN blocked data format.

We recommend users to enable NATIVE_FORMAT by below command to achieve good out-of-box performance.
export TF_ENABLE_MKL_NATIVE_FORMAT=1 (or 0)

export TF_ENABLE_MKL_NATIVE_FORMAT=1

References

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Product and Performance Information

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Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.