CUDA: Difference between revisions
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# Install NVIDIA driver | # Install NVIDIA driver | ||
sudo apt-get install --no-install-recommends nvidia-driver- | sudo apt-get install --no-install-recommends nvidia-driver-450 | ||
# Reboot. Check that GPUs are visible using the command: nvidia-smi | # Reboot. Check that GPUs are visible using the command: nvidia-smi | ||
# Install development and runtime libraries (~4GB) | # Install development and runtime libraries (~4GB) | ||
sudo apt-get install --no-install-recommends \ | sudo apt-get install --no-install-recommends \ | ||
cuda-10-1 | cuda-10-1 libcudnn7 libcudnn7-dev | ||
Revision as of 12:20, 11 June 2020
Installation
Linux
- Install the latest nvidia drivers from the standard repo, e.g.
nvidia-drivers-440
- Install Cuda Toolkit separately without the drivers.
- Use one of the deb install options.
- You may also want to install the following:
- cuDnn
- TensorRT
- cuDnn
- Adapted from tensorflow
# 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-450 # 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 libcudnn7-dev # 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
For tensorflow and pytorch, you may need to add LD_LIBRARY_PATH=/usr/local/cuda/lib64
to your environment variables.
You can also do this in PyCharm.