![]() Follow one of the installation procedures to get Intel-optimized TensorFlow. In case your anaconda channel is not the highest priority channel by default(or you are not sure), use the following command to make sure you get the right TensorFlow with Intel optimizationsīesides the install method described above, Intel Optimization for TensorFlow is distributed as wheels, docker images and conda package on Intel channel. Open Anaconda prompt and use the following instruction If you don't have conda package manager, download and install Anaconda Install the latest Intel® Optimization for TensorFlow* from Anaconda* CloudĪvailable for Linux*, Windows*, MacOS* OS Binaries Get Intel® Optimization for TensorFlow* Pre-Built Images NOTE : Users can start with pip wheel installation from Intel Channel if no preference. It would show better performance in latest release than olders. NOTE: We recommend to use latest release of OS distributions to get better performance. It supports Windows Server 2016*, Windows Server 2019*, Windows 10*, Windows 11*. It supports most Linux distributions: like Ubuntu*, Red Hat Enterprise Linux*, SUSE Linux Enterprise Server*, Fedora*, CentOS*, Debian*, Amazon Linux 2*, WSL 2, Rocky Linux*, etc. There is a comparison table between those two releases in the additional information session. Since TensorFlow v2.9, the oneAPI Deep Neural Network Library (oneDNN) optimizations are enabled by default. ![]() The feature is off by default before v2.9, users can enable those CPU optimizations by setting the the environment variable TF_ENABLE_ONEDNN_OPTS=1 for the official x86-64 TensorFlow. The oneAPI Deep Neural Network Library (oneDNN) optimizations are also now available in the official x86-64 TensorFlow after v2.5. Code samples to help get started with are available here. Download and Install to get separate conda environments optimized with Intel's latest AI accelerations. ![]() Now, Intel Optimization for Tensorflow is also available as part of Intel® AI Analytics Toolkit. This install guide features several methods to obtain Intel Optimized TensorFlow including off-the-shelf packages or building one from source that are conveniently categorized into Binaries, Docker Images, Build from Source.įor more details of those releases, users could check Release Notes of Intel Optimized TensorFlow. Starting from TensorFlow v1.9, Anaconda has and will continue to build TensorFlow using oneDNN primitives to deliver maximum performance in your CPU. For more information on the optimizations as well as performance data, see this blog post TensorFlow* Optimizations on Modern Intel® Architecture.Īnaconda* has now made it convenient for the AI community to enable high-performance-computing in TensorFlow. In order to take full advantage of Intel® architecture and to extract maximum performance, the TensorFlow framework has been optimized using oneAPI Deep Neural Network Library (oneDNN) primitives, a popular performance library for deep learning applications. TensorFlow* is a widely-used machine learning framework in the deep learning arena, demanding efficient utilization of computational resources. > hello = tf.Intel® Optimization for TensorFlow* Installation Guide The code for first program implementation is mentioned below − The command used for installation is mentioned as below −Īfter successful installation, it is important to know the sample program execution of TensorFlow.įollowing example helps us understand the basic program creation “Hello World” in TensorFlow. Step 5 − Use pip to install “Tensorflow” in the system. Step 4 − After successful environmental setup, it is important to activate TensorFlow module. It downloads the necessary packages needed for TensorFlow setup. Step 3 − Execute the following command to initialize the installation of TensorFlow −Ĭonda create -name tensorflow python = 3.5 The execution of command is displayed below − Pip is a command used for executing and installing modules in Python.īefore we install TensorFlow, we need to install Anaconda framework in our system.Īfter successful installation, check in command prompt through “conda” command. ![]() Step 2 − A user can pick up any mechanism to install TensorFlow in the system. Step 1 − Verify the python version being installed. Python version 3.4+ is considered the best to start with TensorFlow installation.Ĭonsider the following steps to install TensorFlow in Windows operating system. To install TensorFlow, it is important to have “Python” installed in your system.
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