TensorFlow is an open source machine learning framework that can be used to build and train various deep learning models. In the answer below, I will show you how to install and download TensorFlow on Windows, Linux and Mac OS systems.
Install TensorFlow on Windows
Install Python
First, you need to have Python installed on your Windows system. It is recommended to use the official Python distribution, namely Anaconda, because it comes with many scientific computing libraries, such as numpy and scipy, which are also used in TensorFlow. You can download and install Anaconda at: https://www.anaconda.com/products/individual#windows.
Create a virtual environment
Next, you need to create a virtual environment for TensorFlow. This is because TensorFlow may require specific versions of libraries that may conflict with other applications on your system. Using a virtual environment isolates libraries between TensorFlow and other applications, avoiding conflicts.
In the Anaconda Prompt terminal, run the following command to create a virtual environment named "tf_env":
conda create --name tf_env
Activate the virtual environment
After creating a virtual environment, you need to activate it. In the Anaconda Prompt terminal, run the following command:
conda activate tf_env
Install TensorFlow
After activating the virtual environment, you can install TensorFlow using the pip command. In the Anaconda Prompt terminal, run the following command to install TensorFlow:
pip install tensorflow
If you have an NVIDIA GPU installed on your computer, you can use the TensorFlow GPU version by installing tensorflow-gpu:
pip install tensorflow-gpu
test installation
After the installation is complete, you can run the following Python code to test whether TensorFlow is installed correctly:
import tensorflow as tf print(tf.__version__)
If the version number is output, TensorFlow has been successfully installed. If you get any errors, make sure you have properly installed Python and TensorFlow following the steps above and check for any error messages.
Install TensorFlow on a Linux system
Install Python
If Python is not installed in your Linux system, you can install it with the following command:
sudo apt-get update sudo apt-get install python3-dev python3-pip
Create a virtual environment
On Linux systems, you can use virtualenv to create virtual environments. First, you need to install virtualenv:
sudo apt-get install python3-virtualenv
You can then create a virtual environment called "tf_env" with the following command:
virtualenv --system-site-packages -p python3 ./tf_env
Activate the virtual environment
After creating the virtual environment, you need to activate the virtual environment.
Of course, the following are the detailed steps of TensorFlow installation and download:
Determine system requirements
First, you need to make sure your computer system meets the requirements for TensorFlow. The requirements for TensorFlow are as follows:
- OS: 64-bit Windows 7 (or higher) or 64-bit Ubuntu 16.04 (or higher).
- Graphics card: If you use GPU acceleration, you need an NVIDIA graphics card that supports CUDA computing capability.
- Python version: TensorFlow supports Python 3.6-3.8 versions.
Install Python
If Python is not installed on your system, you need to install Python first. You can download the Python installer from the Python official website , and then run the program to install it.
Create a Python virtual environment (optional)
To avoid installing TensorFlow and other Python packages at the system level, you can create Python virtual environments. Python virtual environments allow you to install and use different versions of Python packages without interfering with other Python environments.
You can use Python's own venvmodules or third-party tools such as conda to create Python virtual environments. Here are venvthe steps to create a Python virtual environment with modules:
-
Open a terminal (command prompt or PowerShell for Windows, terminal for Linux and MacOS).
-
Create a Python virtual environment called "myenv" by entering the following command in Terminal:
python -m venv myenv -
Activate the virtual environment. Under Windows, you need to execute the following command:
myenv\Scripts\activate.batUnder Linux and MacOS, you need to execute the following commands:
source myenv/bin/activateAfter activating the virtual environment, your terminal prompt should be prefixed with "(myenv)".
Install TensorFlow
There are two ways to install TensorFlow: install using pip or compile and install from source.
install using pip
Enter the following command in a terminal to install the latest version of TensorFlow using pip:
pip install tensorflow
If your computer has an NVIDIA graphics card that supports CUDA compute capabilities, and you want to use GPU acceleration, you can install the TensorFlow GPU version with the following command:
pip install tensorflow-gpu
When installing TensorFlow, pip will automatically download and install other Python packages required by TensorFlow, such as numpy and protobuf.
Source code compilation and installation
If you want to compile and install TensorFlow from source code, you need to download the TensorFlow source code first.
You can download TensorFlow's source code from TensorFlow's GitHub page . You need to select the version you want and download the source code tarball.
The following is the detailed process of TensorFlow installation and download:
1. Install Python and pip
Before installing TensorFlow, you need to install Python and pip first. TensorFlow supports Python 3.5 to 3.8, and it is recommended to use Python 3.7 or 3.8. If Python is not installed on your computer, please download and install Python first.
After installing Python, you can check whether pip is installed with the following command:
pip --version
If it prompts that pip does not exist, you can use the following command to install pip:
python -m ensurepip --default-pip
2. Install TensorFlow
2.1 Install the CPU version
To install the CPU version of TensorFlow, you can use the following command:
pip install tensorflow
This will download and install the latest version of TensorFlow. If you want to install a specific version of TensorFlow, you can specify the version number, for example:
pip install tensorflow==2.6.0
2.2 Install the GPU version
If you have a CUDA-capable NVIDIA graphics card on your computer and have CUDA and cuDNN installed, you can install the GPU version of TensorFlow to run faster deep learning models on the GPU. The following are the detailed steps to install the TensorFlow GPU version:
2.2.1 Install CUDA
First you need to install CUDA. You can download the CUDA version suitable for your computer from the NVIDIA official website, and install it according to the official documentation after downloading.
2.2.2 Install cuDNN
After installing CUDA, you need to install cuDNN, which is a deep learning library that provides efficient convolution operations and other deep learning calculations. You can download the cuDNN version suitable for your CUDA version from the NVIDIA official website, and install it according to the official documentation after downloading.
2.2.3 Install TensorFlow GPU version
After installing CUDA and cuDNN, you can install the TensorFlow GPU version. The latest version of TensorFlow GPU version can be installed with the following command:
pip install tensorflow-gpu
If you want to install a specific version of TensorFlow GPU version, you can specify the version number, for example:
pip install tensorflow-gpu==2.6.0
3. Verify TensorFlow installation
After the installation is complete, you can verify that TensorFlow is installed correctly with the following code:
import tensorflow as tf print(tf.__version__) hello = tf.constant('Hello, TensorFlow!') sess = tf.Session() print(sess.run(hello))
If the version number of TensorFlow and "Hello, TensorFlow!" are output, it means that TensorFlow has been successfully installed and can run normally.
Hope this process of installing and downloading TensorFlow helps you. If you have problems installing or using TensorFlow, you can check the official TensorFlow documentation or ask for help in communities such as Stack Overflow.