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.
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.
The following NVIDIA® software must be installed on your system:
- NVIDIA® GPU drivers —CUDA 9.0 requires 384.x or higher.
- CUDA® Toolkit —TensorFlow supports CUDA 9.0.
- CUPTI ships with the CUDA Toolkit.
- cuDNN SDK (>= 7.2)
- (Optional) NCCL 2.2 for multiple GPU support.
- (Optional) TensorRT 4.0 to improve latency and throughput for inference on some models.
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
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
For Ubuntu 16.04—and possibly other Debian-based Linux distros—add the
NVIDIA package repository and use
apt to install CUDA.
# Add NVIDIA package repository
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt install ./cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
sudo apt install ./nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
sudo apt update# 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=184.108.40.206-1+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
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
environmental variable. For example, if the CUDA Toolkit is installed to
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0 and cuDNN to
C:\tools\cuda, update your
%PATH% to match:
SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin;%PATH%
SET PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\extras\CUPTI\libx64;%PATH%