{"id":14006,"date":"2018-05-03T10:22:48","date_gmt":"2018-05-03T10:22:48","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=14006"},"modified":"2021-05-14T11:00:27","modified_gmt":"2021-05-14T05:30:27","slug":"install-tensorflow","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/install-tensorflow\/","title":{"rendered":"How to Install TensorFlow Quickly and Easily"},"content":{"rendered":"

In our last TensorFlow Tutorial<\/strong>, we discussed Tensorflow API<\/strong>. Today we will see how to install TensorFlow.\u00a0 While looking at the TensorFlow installation in your system we will also learn\u00a0how to install Tensorflow with Anaconda<\/strong>.<\/p>\n

Moreover, we will see the process\u00a0to install TensorFlow Virtual environment, Pip, Docker. Also, we will discuss TensorFlow installation process with GPU & CPU.<\/p>\n

So let\u2019s understand how to install TensorFlow.<\/p>\n

TensorFlow Installation With CPU and GPU<\/h2>\n

Before getting started with Tensorflow Installation it is important to note that TensorFLow has been tested in 64-bit versions and with Ubuntu 16.04 or higher.<\/p>\n

Before you install TensorFlow in your system, it is important to know which kind of installation is best suited for your need. You should choose between the following types:<\/p>\n

Install Tensorflow With CPU support only:<\/strong> This usually takes less time than a GPU<\/strong> supported version and this type is recommended if you do not have an NVIDIA GPU in your computer system<\/p>\n

Install Tensorflow With GPU support:<\/strong> It takes a little more time but the processing is faster than a CPU, and the requirements should be met as listed below, the following programs must be installed on your system before you install Tensorflow with GPU Support:<\/p>\n

    \n
  • CUDA\u00ae Toolkit 9.0<\/li>\n
  • The NVIDIA drivers associated with CUDA Toolkit 9.0.<\/li>\n
  • cuDNN v7.0<\/li>\n
  • GPU card with CUDA Compute Capability 3.0 or higher.<\/li>\n<\/ul>\n

    Methods to Install TensorFlow<\/h2>\n

    In Ubuntu, it is recommended to first install pip in your system if not already installed, as it is one of the things that\u2019ll be required quite often while working with Python. For installing pip, use the following commands:<\/strong><\/p>\n

    # for Python 2.7<\/strong><\/p>\n

    $ sudo apt-get install python-pip python-dev<\/pre>\n
    # for Python 3.n<\/strong><\/p>\n
    $ sudo apt-get install python3-pip python3-dev<\/pre>\n
    You can then proceed with Tensorflow installation using the following methods:<\/p>\n
      \n
    • Install in Virtual Environment like Virtual Box.<\/li>\n
    • Using python package installer pip.<\/li>\n
    • Using container software like Docker.<\/li>\n
    • Using Anaconda<\/li>\n
    • Installing from other sources.<\/li>\n<\/ul>\n
      a. Using Virtual Environment<\/h3>\n
      This is a suggested method since it is incapable of interfering with or affecting the other Python programs on the same machine. You need to enable a virtual environment in your BIOS for using it.<\/p>\n
      Create a virtual environment for TensorFlow Installation:
      \n# for Python 2.7<\/strong><\/p>\n
      $ virtualenv --system-site-packages targetDirectory<\/pre>\n
      # for Python 3.n<\/strong><\/p>\n
      $ virtualenv --system-site-packages -p python3 targetDirectory<\/pre>\n
      where targetDirectory specifies the top of the Virtualenv tree. Our instructions assume that targetDirectory is ~\/TensorFlow, but you may choose any directory. Then activate the environment using.<\/p>\n
      $ source ~\/tensorflow\/bin\/activate # bash, sh, ksh, or zsh\r\n$ source ~\/tensorflow\/bin\/activate.csh  # csh or tcsh<\/pre>\n
      The source \u00a0command now changes to :(tensorflow)$
      \nNow issue one of the following commands in the active virtual environment :
      \n# for Python 2.7<\/strong><\/p>\n
      (tensorflow)$ pip install --upgrade tensorflow<\/pre>\n
      # for Python 3.n<\/strong><\/p>\n
      (tensorflow)$ pip3 install --upgrade tensorflow<\/pre>\n
      # for Python 2.7 and GPU<\/strong><\/p>\n
      (tensorflow)$ pip install --upgrade tensorflow-gpu<\/pre>\n
      # for Python 3.n and GPU<\/strong><\/p>\n
      (tensorflow)$ pip3 install --upgrade tensorflow-gpu<\/pre>\n
      b. Using pip<\/h3>\n
      The native pip install TensorFlow directly into your system, without going through any container system. Plus, it is an easy installation. Use the following commands for the installation:<\/p>\n
      $ pip install tensorflow      # Python 2.7; CPU support (no GPU support)\r\n$ pip3 install tensorflow     # Python 3.n; CPU support (no GPU support)\r\n$ pip install tensorflow-gpu  # Python 2.7;  GPU support\r\n$ pip3 install tensorflow-gpu # Python 3.n; GPU support<\/pre>\n
      If the above step fails, try upgrading the binary files using :<\/p>\n
      $ sudo pip  install --upgrade tfBinaryURL   # Python 2.7\r\n$ sudo pip3 install --upgrade tfBinaryURL   # Python 3.n<\/pre>\n
      c.\u00a0 Install TensorFlow With Docker<\/h3>\n
      It isolates the library installation from pre-existing packages on your machine. The only downside is that the Docker image can be a bit large approx to 100s of Mbs. You have to first install Docker and initialize the container.<\/p>\n
      To install a version of TensorFlow that supports GPUs, you must first install Nvidia-docker, which is stored in GitHub, then, launch a Docker container that contains one of the TensorFlow binary images. With CPU support only do :<\/p>\n
      $ docker run -it -p hostPort:containerPort TensorFlowCPUImage<\/pre>\n
      Where -p hostPort:containerPort is optional.
      \nTensorFlowCPUImage is required. Specify one of the following values:<\/p>\n
        \n
      • tensorflow\/tensorflow, which is the TensorFlow CPU binary image.<\/li>\n
      • tensorflow\/tensorflow:latest-devel, which is the latest TensorFlow CPU Binary image plus source code.<\/li>\n
      • tensorflow\/tensorflow:version, which is the specified version (for example, 1.1.0rc1) of TensorFlow CPU binary image.<\/li>\n
      • tensorflow\/tensorflow:version-devel, which is the specified version (for example, 1.1.0rc1) of the TensorFlow GPU binary image plus source code.<\/li>\n<\/ul>\n
        For example,<\/p>\n
        $ docker run -it tensorflow\/tensorflow bash<\/pre>\n
        You can Jupyter Notebook too,<\/p>\n
        $ docker run -it -p 8888:8888 tensorflow\/tensorflow<\/pre>\n
        d. Installing TensorFlow Using Anaconda<\/h3>\n
        You may use conda to create a virtual environment, and then using either conda install or pip install for installing TensorFlow. First, you need to download and install Anaconda in your system.<\/p>\n
        $ conda create -n tensorflow pip python=2.7 # or python=3.3, etc.<\/pre>\n
        Creates a conda environment named tensorflow to run a version of Python. Activating the conda environment using,<\/p>\n
        $ source activate tensorflow\r\n(tensorflow)$<\/pre>\n
        # Your prompt should change
        \nThen, install TensorFlow using :<\/p>\n
        (tensorflow)$ pip install --ignore-installed --upgrade tfBinaryURL<\/pre>\n
        For example, the following command installs the CPU-only version of TensorFlow for Python 3.4:<\/p>\n
        (tensorflow)$ pip install --ignore-installed --upgrade \\\r\nhttps:\/\/storage.googleapis.com\/tensorflow\/linux\/cpu\/tensorflow-1.7.0-cp34-cp34m-linux_x86_64.whl<\/pre>\n
        Now test your installation by typing out the following commands :
        \n# Python<\/strong><\/p>\n
        import tensorflow as tf\r\nhello = tf.constant('Hello, TensorFlow!')\r\nsess = tf.Session()\r\nprint(sess.run(hello))<\/pre>\n
        You should see the following output if everything is done correctly,<\/p>\n
        Hello, TensorFlow!
        \nInstalling Tensorflow on Windows,<\/p>\n
          \n
        • Using \u2018native\u2019 pip<\/li>\n
        • Using Anaconda<\/li>\n<\/ul>\n
          e. Using Pip to Install TensorFlow<\/h3>\n
          The above methods described earlier are the same for windows as well with a few minor changes.
          \nFirst, start a terminal, then type :<\/p>\n
          C:\\> pip3 install --upgrade tensorflow<\/pre>\n
          And for GPU support add \u201c-gpu\u201d suffix with tensorflow. That\u2019s it.<\/p>\n
          f. Again Using Anaconda<\/h3>\n
          Again, create a conda environment as follows:<\/p>\n
          C:> conda create -n tensorflow pip python=3.5<\/pre>\n
          Then, activate the environment using:<\/p>\n
          C:> activate tensorflow\r\n(tensorflow)C:> \u00a0# Your prompt should change<\/pre>\n
          Now, issue the command for TensorFlow installation as described below:<\/p>\n
          (tensorflow)C:> pip install --ignore-installed --upgrade tensorflow<\/pre>\n
          Now, it\u2019s time to test the installation using :
          \nFirst start python in the terminal, then, type the following,<\/p>\n
          >>> import tensorflow as tf\r\n>>> hello = tf.constant('Hello, TensorFlow!')\r\n>>> sess = tf.Session()\r\n>>> print(sess.run(hello))<\/pre>\n
          If the output is as given below, then, congratulations on your first TensorFlow code!
          \nHello, TensorFlow!<\/p>\n
          Install TensorFlow in MacOS<\/h2>\n
          The methods are followed in the installation of TensorFlow:<\/p>\n
            \n
          • Virtualenv<\/li>\n
          • “native” pip<\/li>\n
          • Docker<\/li>\n<\/ul>\n
            The TensorFlow installation with docker and Virtualenv remain the same as described previously.
            \nUsing pip, first install it :<\/p>\n
            $ sudo easy_install --upgrade pip\r\n$ sudo easy_install --upgrade six<\/pre>\n
            Now, to install TensorFlow
            \n# Python 2.7; CPU support<\/p>\n
            $ pip install tensorflow<\/pre>\n
            # Python 3.n; CPU support<\/p>\n
            $ pip3 install tensorflow<\/pre>\n
            That\u2019s it, pretty easy, right!
            \nNow, validate your installation by using the same previous command,
            \n# Python<\/strong><\/p>\n
            import tensorflow as tf\r\nhello = tf.constant('Hello, TensorFlow!')\r\nsess = tf.Session()\r\nprint(sess.run(hello))<\/pre>\n
            And you should see the following output:
            \nHello, TensorFlow!
            \nSo, this was all in how to Install TensorFlow. Hope you like our explanation of Installing TensorFlow.<\/p>\n
            Conclusion<\/h2>\n
            Hence, we saw how to install Tensorflow by importing the libraries and dependencies using various methods on different systems. Moreover, we discussed the TensorFlow installation with GPU & CPU.<\/p>\n
            Also, we saw install TensorFlow using Pip, Anaconda & Virtual environment. Next up, we\u2019ll be making clusters in Tensorflow<\/strong>. Still, if any doubt, ask in the comment tab.<\/p>\n","protected":false},"excerpt":{"rendered":"
            In our last TensorFlow Tutorial, we discussed Tensorflow API. Today we will see how to install TensorFlow.\u00a0 While looking at the TensorFlow installation in your system we will also learn\u00a0how to install Tensorflow with...<\/p>\n","protected":false},"author":6,"featured_media":21446,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[73],"tags":[6234,6802,6803,6804,6805,6806,6824,6825,14557],"class_list":["post-14006","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tensorflow","tag-how-to-install-tensorflow","tag-install-tensorflow","tag-install-tensorflow-on-macos","tag-install-tensorflow-on-ubuntu","tag-install-tensorflow-on-windows","tag-install-tensorflow-with-docker","tag-installing-tensorflow-with-cpu","tag-installing-tensorflow-with-gpu","tag-tensorflow-installation-process"],"yoast_head":"\nHow to Install TensorFlow Quickly and Easily - DataFlair<\/title>\n<meta name=\"description\" content=\"Install Tensorflow tutorial:Tensorflow installation on windows,Ubuntu and MacOS, install TensorFlow using Anaconda on Ubuntu,PIP, Docker&Virtual environment\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/install-tensorflow\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Install TensorFlow Quickly and Easily - 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