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1. #INSTALL LATEST NVIDIA DRIVERS GPU ON UBUNTU 16.04 INSTALL#
2. #INSTALL LATEST NVIDIA DRIVERS GPU ON UBUNTU 16.04 UPDATE#

Test running docker with GPU acceleration provided by NVIDIA runtime $ sudo docker run -runtime=nvidia -rm nvidia/cuda nvidia-smiĪlternatively, we can replace docker command with nvidia-docker $ nvidia-docker run -rm nvidia/cuda nvidia-smi

#INSTALL LATEST NVIDIA DRIVERS GPU ON UBUNTU 16.04 INSTALL#

Install nvidia-docker2 and reload the docker daemon $ sudo apt-get install -y nvidia-docker2ĥ.

#INSTALL LATEST NVIDIA DRIVERS GPU ON UBUNTU 16.04 UPDATE#

Update the apt package index $ sudo apt-get updateĤ. Add the repository source list $ curl -s -L | sudo tee /etc/apt//nvidia-docker.listģ. Add the repository GPG key $ curl -s -L | sudo apt-key add -Ģ. If all prerequisites are satisfied, we can now proceed to the installationġ. To obtain the NVIDIA driver version, we can invoke nvidia-smi command again: $ nvidia-smi | grep "Version" Verify that the NVIDIA driver version >= 361.93 If the product is not in the list, there is high chance that nvidia-docker cannot be installed.Ĥ. $ nvidia-smi -q | grep "Product Name"Īfter obtaining the product name, if you are using desktop GPUs you can check against the table provided in this article. To check if the current GPU can be used with nvidia-docker, we can use nvidia-smi to obtain the GPU product name. The architecture types that are newer than Fermi: Verify that NVIDIA GPU architecture is newer than Fermi (the CUDA compute capability > 2.1) If you haven’t installed Docker, you can refer to the installation guide provided in this article.ģ.

The Docker version info can be obtained by invoking this command: $ docker -v Verify that the Docker version is at least 1.12 The kernel version info can be obtained by invoking this command: $ uname -rĢ. Verify that the GNU/Linux kernel version is bigger than 3.10 By specifying this runtime when invoking a command in a (new) Docker container, the command execution will be accelerated with the GPUs.Īs outlined in the official documentation, these prerequisites should be satisfied prior to installation.ġ. Under the hood, nvidia-docker will add a new Docker runtime called nvidia during the installation. The utility enables creation of Docker containers that leverage CUDA GPU computing when being run. NVIDIA provides a utility called nvidia-docker. A possible route to achieving this is by creating several containers and perform distinct task in each container. But life will be much easier if the tasks can be done in parallel. We can proceed with the sequential operation, doing the tasks one by one. We may need a task to calculate the precision and recall of a certain model while at the same time we are in need for training another model. Suppose that we are evaluating different models. When performing deep learning tasks especially on a single physical machine, there can be a moment where we need to execute tasks in parallel.