GPU support

TensorFlow GPU support requires an assortment of drivers and libraries. To simplify installation and avoid library conflicts, we recommend using a TensorFlow Docker image with GPU support (Linux only). This setup only requires the NVIDIA® GPU drivers.

These install instructions are for the latest release of TensorFlow. See the tested build configurations for CUDA and cuDNN versions to use with older TensorFlow releases.

Pip package

See the pip install guide for available packages, systems requirements, and instructions. To pip install a TensorFlow package with GPU support, choose a stable or development package:

pip install tensorflow  # stable

pip install tf-nightly  # preview

Older versions of TensorFlow

For releases 1.15 and older, CPU and GPU packages are separate:

pip install tensorflow==1.15      # CPU
pip install tensorflow-gpu==1.15  # GPU

Hardware requirements

The following GPU-enabled devices are supported:

NVIDIA® GPU card with CUDA® Compute Capability 3.5 or higher. See the list of CUDA-enabled GPU cards.

Software requirements

The following NVIDIA® software must be installed on your system:

NVIDIA® GPU drivers —CUDA 10.1 requires 418.x or higher.
CUDA® Toolkit —TensorFlow supports CUDA 10.1 (TensorFlow >= 2.1.0)
CUPTI ships with the CUDA Toolkit.
cuDNN SDK (>= 7.6)
(Optional) TensorRT 6.0 to improve latency and throughput for inference on some models.

Linux setup

The apt instructions below are the easiest way to install the required NVIDIA software on Ubuntu. However, if building TensorFlow from source, manually install the software requirements listed above, and consider using a -devel TensorFlow Docker image as a base.

Install CUPTI which ships with the CUDA® Toolkit. Append its installation directory to the $LD_LIBRARY_PATH environmental variable:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64

For a GPU with CUDA Compute Capability 3.0, or different versions of the NVIDIA libraries, see the Linux build from source guide.

Install CUDA with apt

This section shows how to install CUDA 10 (TensorFlow >= 1.13.0) and CUDA 9 for Ubuntu 16.04 and 18.04. These instructions may work for other Debian-based distros.

Ubuntu 18.04 (CUDA 10.1)

# Add NVIDIA package repositories
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.1.243-1_amd64.deb
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
sudo dpkg -i cuda-repo-ubuntu1804_10.1.243-1_amd64.deb
sudo apt-get update
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
sudo apt install ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
sudo apt-get update

# Install NVIDIA driver
sudo apt-get install --no-install-recommends nvidia-driver-430
# Reboot. Check that GPUs are visible using the command: nvidia-smi

# Install development and runtime libraries (~4GB)
sudo apt-get install --no-install-recommends \
    cuda-10-1 \
    libcudnn7=7.6.4.38-1+cuda10.1  \
    libcudnn7-dev=7.6.4.38-1+cuda10.1


# Install TensorRT. Requires that libcudnn7 is installed above.
sudo apt-get install -y --no-install-recommends libnvinfer6=6.0.1-1+cuda10.1 \
    libnvinfer-dev=6.0.1-1+cuda10.1 \
    libnvinfer-plugin6=6.0.1-1+cuda10.1

Ubuntu 16.04 (CUDA 10.1)

# Add NVIDIA package repositories
# Add HTTPS support for apt-key
sudo apt-get install gnupg-curl
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_10.1.243-1_amd64.deb
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo dpkg -i cuda-repo-ubuntu1604_10.1.243-1_amd64.deb
sudo apt-get update
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
sudo apt install ./nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
sudo apt-get update

# Install NVIDIA driver
# Issue with driver install requires creating /usr/lib/nvidia
sudo mkdir /usr/lib/nvidia
sudo apt-get install --no-install-recommends nvidia-418
# Reboot. Check that GPUs are visible using the command: nvidia-smi

# Install development and runtime libraries (~4GB)
sudo apt-get install --no-install-recommends \
    cuda-10-1 \
    libcudnn7=7.6.4.38-1+cuda10.1  \
    libcudnn7-dev=7.6.4.38-1+cuda10.1


# Install TensorRT. Requires that libcudnn7 is installed above.
sudo apt-get install -y --no-install-recommends \
    libnvinfer6=6.0.1-1+cuda10.1 \
    libnvinfer-dev=6.0.1-1+cuda10.1 \
    libnvinfer-plugin6=6.0.1-1+cuda10.1

Ubuntu 16.04 (CUDA 9.0 for TensorFlow < 1.13.0)

# Add NVIDIA package repository
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
sudo apt install ./cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
sudo apt install ./nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
sudo apt update

# Install the NVIDIA driver
# Issue with driver install requires creating /usr/lib/nvidia
sudo mkdir /usr/lib/nvidia
sudo apt-get install --no-install-recommends nvidia-410
# Reboot. Check that GPUs are visible using the command: nvidia-smi

# Install CUDA and tools. Include optional NCCL 2.x
sudo apt install cuda9.0 cuda-cublas-9-0 cuda-cufft-9-0 cuda-curand-9-0 \
    cuda-cusolver-9-0 cuda-cusparse-9-0 libcudnn7=7.2.1.38-1+cuda9.0 \
    libnccl2=2.2.13-1+cuda9.0 cuda-command-line-tools-9-0

# Optional: Install the TensorRT runtime (must be after CUDA install)
sudo apt update
sudo apt install libnvinfer4=4.1.2-1+cuda9.0

Windows setup

See the hardware requirements and software requirements listed above. Read the CUDA® install guide for Windows.

Make sure the installed NVIDIA software packages match the versions listed above. In particular, TensorFlow will not load without the cuDNN64_7.dll file. To use a different version, see the Windows build from source guide.

Add the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environmental variable. For example, if the CUDA Toolkit is installed to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1 and cuDNN to C:\tools\cuda, update your %PATH% to match:

SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin;%PATH%
SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\extras\CUPTI\libx64;%PATH%
SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\include;%PATH%
SET PATH=C:\tools\cuda\bin;%PATH%