TensorFlow Installation

The most up-to-date installation method can be acquired through the official website (https://tensorflow.google.cn/install). TensorFlow supports multiple programming languages like Python, Java and Go and a variety of operating systems like Windows, OSX and Linux. We prefer Python in this handbook.

We provide both simple and full installation methods for readers with different requirements.

Simple Installation

You can follow these steps if you only want to install TensorFlow on your personal computer without GPU or you do not want to spend too much effort configuring the environment:

  • Download and install Python distribution Anaconda (with Python Ver 3.6).
  • Download and install Python IDE PyCharm (Community version. Students can apply for licenses of Professional version for free).
  • Run Anaconda Prompt in the Start Menu (Windows), enter and execute pip install tensorflow.
  • Start PyCharm, create a project with default python interpreter, and create a python file in the project.

And done.

Full Installation

This part includes more details of installation (e.g. building a conda environment) and guidance for the GPU version of TensorFlow environment installation.

Environment configuration before installation

Before installing TensorFlow, we need to set up a proper environment with the following steps:

  1. Check if your computer has an NVIDIA graphics card and install the GPU version of TensorFlow in order to take advantages of its powerful capability of computation acceleration [1] , or, just install CPU version if not so. To be more specific, the CUDA Computing Capability of your graphics card that you can check on NVIDIA official website should not be less than 3.0.
  2. Install the Python environment. Anaconda is recommended. It is an open-source release version of Python that provides a full environment for scientific computation including common libraries such as NumPy and SciPy, or you can choose your favorite ones of course. Note that TensorFlow only supports Python Ver 3.X under Windows when we write this handbook.
    • You can choose to add the directory of Anaconda into the PATH (though not recommended by the installation wizard). It enables you to call all Anaconda commands under command line or Powershell directly. You can always call them under the Anaconda Prompt started in the Start Menu.
  3. (For GPU version installation) Install the NVIDIA graphics driver, CUDA Toolkit and cuDNN. You should note that:
    • We recommend you install it through the following order: 1) latest NVIDIA graphics driver 2) CUDA (without selecting the built-in driver when installing since the built-in ones may be out-of-date) 3) cuDNN;
    • There is a quite simple way to install drivers in Ubuntu. First click “Software & Updates” in “System Setting”, then toggle on “Using NVIDIA binary driver” option in “Additional Drivers” and click “Apply Changes” for system to install NVIDIA drivers automatically, otherwise, it won’t be peaceful for NVIDIA installation on Linux. You should disable the built-in graphics driver Nouveau and Secure Boot function of the motherboard. You can seek a more detailed guidance here;
    • The version of CUDA Toolkit and cuDNN must agree with the requirements on TensorFlow official website which does not always require the latest version.
    • You have to copy the downloaded files of cuDNN to the installation directory of CUDA to complete cuDNN installation.

Install

These are the following steps of TensorFlow installation under Anaconda (taking Windows as example):

  1. Create a conda environment named tensorflow
conda create -n tensorflow python=X.X # Substitute "X.X" with your own Python version, e.g. "3.6".
  1. Activate the environment
activate tensorflow
  1. Use pip to install TensorFlow

Install the CPU version

pip install tensorflow

Or, install the GPU version

pip install tensorflow-gpu

You can also choose to install the Nightly version of TensorFlow if you want. This version may include some latest features compared to the official version (e.g. the Eager Execution mode in this handbook was only supported in the Nightly version before TensorFlow Ver 1.8) yet with some instability. You can do so by running pip install tf-nightly (CPU version) or pip install tf-nightly-gpu (GPU version) in a new virtual environment. If you are going to install the GPU version, it may require higher versions of CUDA and cuDNN. Fortunately different versions of CUDA and cuDNN can coexist.

If it is slow to install through pip command in China, you may want to try TensorFlow mirror on TUNA.

First Program

We write a piece of code to verify the installation.

Enter activate tensorflow under command line to enter the previously built conda environment with TensorFlow. Then enter python to enter Python environment. Input the following codes line by line:

import tensorflow as tf
tf.enable_eager_execution()

A = tf.constant([[1, 2], [3, 4]])
B = tf.constant([[5, 6], [7, 8]])
C = tf.matmul(A, B)

print(C)

If the output is:

tf.Tensor(
[[19 22]
[43 50]], shape=(2, 2), dtype=int32)

We can draw conclusions that TensorFlow was successfully installed. It’s normal for the program to output some prompt messages when running.

Here we use Python. You can get Python tutorials on https://docs.python.org/3/tutorial/. From now on we assume that readers are familiar with the basics of Python. Relax, Python is easy to handle and advanced features of Python will be barely involved in TensorFlow. We recommend you to use PyCharm as your Python IDE. If you are a student with an email address ended with .edu, you can apply for a free license here. You can always download PyCharm Community version whose main functions do not differ that much from the former if you do not meet the aforementioned criteria.

[1]The effect of acceleration is relative to the GPU performance. It won’t be satisfactory if you have a high performance CPU and a beginner level GPU where the acceleration rate will be like 1-2. However, the acceleration rate may reach 10 or even higher under specific models if you have a powerful GPU (e.g. NVIDIA GeForce GTX 1080 Ti or NVIDIA GeForce TITAN Series are powerful graphics card types when this handbook was being written). Meanwhile, the acceleration rate is also influenced by the running task itself. The beginner level models of TensorFlow do not require too much performance as the CPU version is adequate. You may determine if you will purchase a higher level graphics card to get faster training speed after you master the basics of TensorFlow.

Upgrade to A New Version

TensorFlow is updated frequently. If you want to upgrade TensorFlow, please enter the conda environment with TensorFlow installed and type the following command

pip install tensorflow --upgrade

If you want to install a specific version of TensorFlow, please type

pip install tensorflow==1.8.0      # here 1.8.0 is the specified version

Upgrade of TensorFlow can be risky, and there may be errors whe you import TensorFlow after the upgrade. The simpler way is to delete the current conda environment and reinstall it. The following conda commands may be useful:

conda list                                             # List all packages and versions in the current conda environment
conda env list                                         # List all conda environments
conda create --name new_env_name --clone old_env_name  # Backup the current conda environment `old_env_name` to `new_env_name`
conda env remove -n tensorflow                         # Delete the conda environment named `tensorflow`