This guide is for the latest stable version of TensorFlow. For the
preview build (nightly), use the pip package named
tf-nightly. Refer to these tables for
older TensorFlow version requirements. For the CPU-only build use the pip
package named tensorflow-cpu.
Here are the quick versions of the install commands. Scroll down for the step-by-step instructions.
Linux
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
python3 -m pip install tensorflow
# Verify install:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
MacOS
# There is currently no official GPU support for MacOS.
python3 -m pip install tensorflow
# Verify install:
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
Windows Native
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
python3 -m pip install tensorflow
# Verify install:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
Windows WSL2
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
python3 -m pip install tensorflow
# Verify install:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
CPU
python3 -m pip install tensorflow
# Verify install:
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
Nightly
python3 -m pip install tf-nightly
# Verify install:
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
Hardware requirements
The following GPU-enabled devices are supported:
- NVIDIA® GPU card with CUDA® architectures 3.5, 5.0, 6.0, 7.0, 7.5, 8.0 and higher. See the list of CUDA®-enabled GPU cards.
- For GPUs with unsupported CUDA® architectures, or to avoid JIT compilation from PTX, or to use different versions of the NVIDIA® libraries, see the Linux build from source guide.
- Packages do not contain PTX code except for the latest supported CUDA®
architecture; therefore, TensorFlow fails to load on older GPUs when
CUDA_FORCE_PTX_JIT=1is set. (See Application Compatibility for details.)
System requirements
- Ubuntu 16.04 or higher (64-bit)
- macOS 10.12.6 (Sierra) or higher (64-bit) (no GPU support)
- Windows Native - Windows 7 or higher (64-bit)
- Windows WSL2 - Windows 10 19044 or higher (64-bit)
Software requirements
- Python 3.7–3.10
- pip version 19.0 or higher for Linux (requires
manylinux2010support) and Windows. pip version 20.3 or higher for macOS. - Windows Native Requires Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019
The following NVIDIA® software are only required for GPU support.
- NVIDIA® GPU drivers version 450.80.02 or higher.
- CUDA® Toolkit 11.2.
- cuDNN SDK 8.1.0.
- (Optional) TensorRT to improve latency and throughput for inference.
Step-by-step instructions
Linux
1. System requirements
- Ubuntu 16.04 or higher (64-bit)
TensorFlow only officially support Ubuntu. However, the following instructions may also work for other Linux distros.
2. Install Miniconda
Miniconda is the recommended approach for installing TensorFlow with GPU support. It creates a separate environment to avoid changing any installed software in your system. This is also the easiest way to install the required software especially for the GPU setup.
You can use the following command to install Miniconda. During installation, you may need to press enter and type "yes".
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
You may need to restart your terminal or source ~/.bashrc to enable the
conda command. Use conda -V to test if it is installed successfully.
3. Create a conda environment
Create a new conda environment named tf with the following command.
conda create --name tf python=3.9
You can deactivate and activate it with the following commands.
conda deactivate
conda activate tf
Make sure it is activated for the rest of the installation.
4. GPU setup
You can skip this section if you only run TensorFlow on the CPU.
First install the NVIDIA GPU driver if you have not. You can use the following command to verify it is installed.
nvidia-smi
Then install CUDA and cuDNN with conda.
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
Configure the system paths. You can do it with following command everytime your start a new terminal after activating your conda environment.
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
For your convenience it is recommended that you automate it with the following commands. The system paths will be automatically configured when you activate this conda environment.
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
5. Install TensorFlow
TensorFlow requires a recent version of pip, so upgrade your pip installation to be sure you're running the latest version.
pip install --upgrade pip
Then, install TensorFlow with pip.
pip install tensorflow
6. Verify install
Verify the CPU setup:
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
If a tensor is returned, you've installed TensorFlow successfully.
Verify the GPU setup:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
If a list of GPU devices is returned, you've installed TensorFlow successfully.
MacOS
1. System requirements
- macOS 10.12.6 (Sierra) or higher (64-bit)
Currently there is no official GPU support for running TensorFlow on MacOS. The following is instructions are for running on CPU.
2. Check Python version
Check if your Python environment is already configured:
python3 --version
python3 -m pip --version
2. Install Miniconda
Miniconda is the recommended approach for installing TensorFlow with GPU support. It creates a separate environment to avoid changing any installed software in your system. This is also the easiest way to install the required software especially for the GPU setup.
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o Miniconda3-latest-MacOSX-x86_64.sh
bash Miniconda3-latest-MacOSX-x86_64.sh
You may need to restart your terminal or source ~/.bashrc to enable the
conda command. Use conda -V to test if it is installed successfully.
4. Create a conda environment
Create a new conda environment named tf with the following command.
conda create --name tf python=3.9
You can deactivate and activate it with the following commands.
conda deactivate
conda activate tf
Make sure it is activated for the rest of the installation.
5. Install TensorFlow
TensorFlow requires a recent version of pip, so upgrade your pip installation to be sure you're running the latest version.
pip install --upgrade pip
Then, install TensorFlow with pip.
pip install tensorflow
6. Verify install
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
If a tensor is returned, you've installed TensorFlow successfully.
Windows Native
1. System requirements
- Windows 7 or higher (64-bit)
2. Install Microsoft Visual C++ Redistributable
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.
3. Install Miniconda
Miniconda is the recommended approach for installing TensorFlow with GPU support. It creates a separate environment to avoid changing any installed software in your system. This is also the easiest way to install the required software especially for the GPU setup.
Download the Miniconda Windows Installer. Double-click the downloaded file and follow the instructions on the screen.
4. Create a conda environment
Create a new conda environment named tf with the following command.
conda create --name tf python=3.9
You can deactivate and activate it with the following commands.
conda deactivate
conda activate tf
Make sure it is activated for the rest of the installation.
5. GPU setup
You can skip this section if you only run TensorFlow on CPU.
First install NVIDIA GPU driver if you have not.
Then install the CUDA, cuDNN with conda.
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
6. Install TensorFlow
TensorFlow requires a recent version of pip, so upgrade your pip installation to be sure you're running the latest version.
pip install --upgrade pip
Then, install TensorFlow with pip.
pip install tensorflow
7. Verify install
Verify the CPU setup:
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
If a tensor is returned, you've installed TensorFlow successfully.
Verify the GPU setup:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
If a list of GPU devices is returned, you've installed TensorFlow successfully.
Windows WSL2
1. System requirements
- Windows 10 19044 or higher (64-bit). This corresponds to Windows 10 version 21H2, the November 2021 update.
See the following documents to:
2. Install Miniconda
Miniconda is the recommended approach for installing TensorFlow with GPU support. It creates a separate environment to avoid changing any installed software in your system. This is also the easiest way to install the required software especially for the GPU setup.
You can use the following command to install Miniconda. During installation, you may need to press enter and type "yes".
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
You may need to restart your terminal or source ~/.bashrc to enable the
conda command. Use conda -V to test if it is installed successfully.
3. Create a conda environment
Create a new conda environment named tf with the following command.
conda create --name tf python=3.9
You can deactivate and activate it with the following commands.
conda deactivate
conda activate tf
Make sure it is activated for the rest of the installation.
4. GPU setup
You can skip this section if you only run TensorFlow on the CPU.
First install the NVIDIA GPU driver if you have not. You can use the following command to verify it is installed.
nvidia-smi
Then install CUDA and cuDNN with conda.
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
Configure the system paths. You can do it with following command everytime your start a new terminal after activating your conda environment.
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
For your convenience it is recommended that you automate it with the following commands. The system paths will be automatically configured when you activate this conda environment.
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
5. Install TensorFlow
TensorFlow requires a recent version of pip, so upgrade your pip installation to be sure you're running the latest version.
pip install --upgrade pip
Then, install TensorFlow with pip.
pip install tensorflow
6. Verify install
Verify the CPU setup:
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
If a tensor is returned, you've installed TensorFlow successfully.
Verify the GPU setup:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
If a list of GPU devices is returned, you've installed TensorFlow successfully.
Package location
A few installation mechanisms require the URL of the TensorFlow Python package. The value you specify depends on your Python version.