TensorFlow 2 packages are available
-
tensorflow
—Latest stable release with CPU and GPU support (Ubuntu and Windows) -
tf-nightly
—Preview build (unstable) . Ubuntu and Windows include GPU support .
Older versions of TensorFlow
For TensorFlow 1.x, CPU and GPU packages are separate:
-
tensorflow==1.15
—Release for CPU-only -
tensorflow-gpu==1.15
—Release with GPU support (Ubuntu and Windows)
System requirements
-
Python 3.6–3.8
- Python 3.8 support requires TensorFlow 2.2 or later.
-
pip 19.0 or later (requires
manylinux2010
support) - Ubuntu 16.04 or later (64-bit)
-
macOS 10.12.6 (Sierra) or later (64-bit)
(no GPU support)
- macOS requires pip 20.3 or later
- Windows 7 or later (64-bit)
- Raspbian 9.0 or later
- GPU support requires a CUDA®-enabled card (Ubuntu and Windows)
Hardware requirements
- Starting with TensorFlow 1.6, binaries use AVX instructions which may not run on older CPUs.
- Read the GPU support guide to set up a CUDA®-enabled GPU card on Ubuntu or Windows.
1. Install the Python development environment on your system
Check if your Python environment is already configured:
python3 --version
pip3 --version
If these packages are already installed, skip to the next step.
Otherwise, install
Python
, the
pip package manager
,
and
venv
:
Ubuntu
sudo apt update
sudo apt install python3-dev python3-pip python3-venv
macOS
Install using the Homebrew package manager:
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
export PATH="/usr/local/opt/python/libexec/bin:$PATH"
# if you are on macOS 10.12 (Sierra) use `export PATH="/usr/local/bin:/usr/local/sbin:$PATH"`
brew update
brew install python # Python 3
Windows
Install the
Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017,
and 2019
. Starting with the TensorFlow 2.1.0 version, the
msvcp140_1.dll
file is required from this package (which may not be provided from older redistributable packages).
The redistributable comes with
Visual Studio 2019
but can be installed separately:
- Go to the Microsoft Visual C++ downloads ,
- Scroll down the page to the Visual Studio 2015, 2017 and 2019 section.
- Download and install the Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019 for your platform.
Make sure long paths are enabled on Windows.
Install the
64-bit
Python 3 release for Windows
(select
pip
as an optional feature).
Raspberry Pi
Requirements for the Raspbian operating system:
sudo apt update
sudo apt install python3-dev python3-pip python3-venv
sudo apt install libatlas-base-dev # required for numpy
Other
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python get-pip.py
2. Create a virtual environment (recommended)
Python virtual environments are used to isolate package installation from the system.
Ubuntu / macOS
Create a new virtual environment by choosing a Python interpreter and making a
./venv
directory to hold it:
python3 -m venv --system-site-packages ./venv
Activate the virtual environment using a shell-specific command:
source ./venv/bin/activate # sh, bash, or zsh
. ./venv/bin/activate.fish # fish
source ./venv/bin/activate.csh # csh or tcsh
When the virtual environment is active, your shell prompt is prefixed with
(venv)
.
Install packages within a virtual environment without affecting the host system
setup. Start by upgrading
pip
:
pip install --upgrade pip
pip list # show packages installed within the virtual environment
And to exit the virtual environment later:
deactivate # don't exit until you're done using TensorFlow
Windows
Create a new virtual environment by choosing a Python interpreter and making a
.\venv
directory to hold it:
python -m venv --system-site-packages .\venv
Activate the virtual environment:
.\venv\Scripts\activate
Install packages within a virtual environment without affecting the host system
setup. Start by upgrading
pip
:
pip install --upgrade pip
pip list # show packages installed within the virtual environment
And to exit the virtual environment later:
deactivate # don't exit until you're done using TensorFlow
Conda
While the TensorFlow provided pip package is recommended, a community-supported Anaconda package is available. To install, read the Anaconda TensorFlow guide .
3. Install the TensorFlow pip package
Choose one of the following TensorFlow packages to install from PyPI :
-
tensorflow
—Latest stable release with CPU and GPU support (Ubuntu and Windows) . -
tf-nightly
—Preview build (unstable) . Ubuntu and Windows include GPU support . -
tensorflow==1.15
—The final version of TensorFlow 1.x.
Virtual environment install
pip install --upgrade tensorflow
Verify the install:
python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
System install
pip3 install --user --upgrade tensorflow # install in $HOME
Verify the install:
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
Package location
A few installation mechanisms require the URL of the TensorFlow Python package. The value you specify depends on your Python version.
Version | URL |
---|---|
Linux | |
Python 3.6 GPU support | https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-2.4.0-cp36-cp36m-manylinux2010_x86_64.whl |
Python 3.6 CPU-only | https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.4.0-cp36-cp36m-manylinux2010_x86_64.whl |
Python 3.7 GPU support | https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-2.4.0-cp37-cp37m-manylinux2010_x86_64.whl |
Python 3.7 CPU-only | https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.4.0-cp37-cp37m-manylinux2010_x86_64.whl |
Python 3.8 GPU support | https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-2.4.0-cp38-cp38-manylinux2010_x86_64.whl |
Python 3.8 CPU-only | https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.4.0-cp38-cp38-manylinux2010_x86_64.whl |
macOS (CPU-only) | |
Python 3.6 | https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.4.0-cp36-cp36m-macosx_10_9_x86_64.whl |
Python 3.7 | https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.4.0-cp37-cp37m-macosx_10_9_x86_64.whl |
Python 3.8 | https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.4.0-cp38-cp38-macosx_10_14_x86_64.whl |
Windows | |
Python 3.6 GPU support | https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-2.4.0-cp36-cp36m-win_amd64.whl |
Python 3.6 CPU-only | https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.4.0-cp36-cp36m-win_amd64.whl |
Python 3.7 GPU support | https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-2.4.0-cp37-cp37m-win_amd64.whl |
Python 3.7 CPU-only | https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.4.0-cp37-cp37m-win_amd64.whl |
Python 3.8 GPU support | https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-2.4.0-cp38-cp38-win_amd64.whl |
Python 3.8 CPU-only | https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.4.0-cp38-cp38-win_amd64.whl |
Raspberry PI (CPU-only) | |
Python 3, Pi0 or Pi1 | https://storage.googleapis.com/tensorflow/raspberrypi/tensorflow-2.3.0rc2-cp35-none-linux_armv6l.whl |
Python 3, Pi2 or Pi3 | https://storage.googleapis.com/tensorflow/raspberrypi/tensorflow-2.3.0rc2-cp35-none-linux_armv6l.whl |