.. title: PAR Class 7, Thu 2020-02-06
.. slug: class07
.. date: 2020-02-05
.. tags: class
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Docker on parallel
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#. I've installed **docker**, a popular lightweight virtualization system, on parallel, because Nvidia uses it to distribute SW.

#. Docker runs **images** that define virtual machines.

#. Docker images share resources with the host, in a controlled manner.

#. You can install private copies of images, or to see what images I've installed, do: **docker images** .

#. Run the hello-world image thus:   **docker run hello-world**

#. Here's a more complicated example:

   **docker run -it --mount type=bind,source=/parallel-class,destination=/parallel-class --mount type=bind,source=$HOME,destination=/home --gpus=all nvidia/cuda:10.1-devel**

   This interactively runs a virtual machine with

   #. Nvidia's CUDA development tools
   #. access to parallel's GPUs
   #. access to parallel's /parallel-class, mounted locally at /parallel-class .
   #. access to your home dir, mounted at /home.

#. E.g., go into /parallel-class/openmp/rpi and run some programs.

#. Copy some .cc files to your home dir, compile, and run them.

#. There are ways to make the image's contents persistent.  E.g., you can customize and save an image.

#. For simple images, which is not nvidia/cuda, starting the image is so cheap that you can do it to run one command, and encapsulate the whole process in a shell function.  More later.  However, e.g.,

   **docker run --gpus=all nvidia/cuda:10.1-devel nvidia-smi**

#. parallel has CUDA sample programs in /local/cuda/samples.  To make them available in a docker image, include **-v local/cuda/samples:/samples**



Nvidia GPU and accelated computing, ctd.
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Continuing  /parallel-class/GPU-Teaching-Kit-Accelerated-Computing  at Module 3.