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GPU data processing inside LXD

1. Overview

Hugely parallelised GPU data processing, using either CUDA or OpenCL, is changing the shape of data science. It even has its own snappy acronym - GPGPU - General-purpose computing on graphics processing units.

It’s no surprise, then, that flexible, scalable access to these GPU resources is becoming a key requirement in many cloud deployments (see the Canonical Distribution of Kubernetes for a good example). But it’s not always easy, nor cheap, to get started. Unless you use LXD.

LXD’s unrivalled density in real-world cloud deployments, and its ability to run locally, make it a game-changing tool for experimenting with cloud-like GPU data processing.

It enables you to create local scalable deployments using nothing more than a PC with a GPU or two. As we’ll now demonstrate.

What you’ll learn

  • How to replace default NVIDIA drivers with the latest ones
  • How to install the CUDA toolkit
  • How to configure LXD to use NVIDIA GPUs and CUDA

What you’ll need

Our configuration is going to be based on the following: - 1 or more NVIDIA GPUs - Ubuntu 18.04 LTS (Bionic Beaver) - LXD version 3.0 or higher

LXD versioning is incremental, which means version 3.1 is more recent than version 3.0.1.

We’ll be using NVIDIA hardware alongside NVIDIA’s proprietary CUDA, as these currently constitute the most widely used GPGPU platform.

However, LXD’s hardware passthrough enables any GPU to appear natively to any deployment, which means that using different GPUs or drivers with OpenCL should be possible.

As both NVIDIA’s drivers and CUDA are constantly in a rapid state of development, we’re going to install and use the latest versions we can get hold of. This will mean using packages separate from those supplied by the distribution, which we’ll cover in the next step.

Originally authored by Graham Morrison

2. Remove NVIDIA drivers

With either a new or old Ubuntu 18.04 installation, it’s likely that you’ll have NVIDIA drivers of one sort or another on your system. We need to make sure these are fully removed before attempting to install a new set.

When working with graphics drivers, it’s best to quit from the graphical environment and work on the command line. This can be done by entering the following into a terminal:

sudo systemctl isolate

To remove your current NVIDIA or open source Nouveau drivers, enter the following:

sudo apt remove --purge nvidia*

It’s safer to reboot your machine at this point, although this isn’t strictly necessary.

Remove Nouveau drivers

The nouveau driver, installed by default when you elect not to add NVIDIA’s proprietary drivers, may refuse to remove itself. You can check with the following command:

lsmod | grep nouveau

If the output includes the nouveau module, you will need to blacklist this module to stop it loading in future. You can do this by adding the following to /etc/modprobe.d/blacklist.conf as root with your favourite text editor:

blacklist amd76x_edac #this might not be required for x86 32 bit users.
blacklist vga16fb
blacklist nouveau
blacklist rivafb
blacklist nvidiafb
blacklist rivatv

3. Install proprietary NVIDIA drivers

With LXD, the host machine handles the drivers and passes the resultant device nodes to the container. But CUDA still expects a local driver installation, and this means we need to have identical versions of both the drivers and CUDA on the host and any LXD containers we deploy.

Thankfully LXD 3.0 has a way to keep the two in sync without requiring installation of all the NVIDIA packages in both host and container.

You can now use apt to install the latest version of the drivers, for example:

apt install nvidia-390

You’ll need to reboot your machine after installing the NVIDIA drivers, after which you can use the nvidia-smi command to check that the driver is installed and operating correctly.

The output will include details similar to the following:

Thu May 25 13:32:33 2017
| NVIDIA-SMI 390.77                 Driver Version: 390.77                    |
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|   0  GeForce GTX 1080    Off  | 0000:01:00.0      On |                  N/A |
| 43%   52C    P8    18W / 240W |    981MiB /  8110MiB |      1%      Default |

4. Install the CUDA toolkit

With the drivers installed, the next step is to grab CUDA itself. While there are Ubuntu packages available, we’ve found the most reliable option is to download the latest version of CUDA directly from NVIDIA’s developer website:

To get to the download, select ‘Linux’, select your architecture, choose ‘Ubuntu’, ‘18.04’ and finally the ‘deb (network)’ installer type. You will then be able to download the base installer. In our example, this file is called cuda-repo-ubuntu1804_10.0.130-1_amd64.deb.

This package then needs to be installed with:

sudo apt install ./cuda-repo-ubuntu1804_10.0.130-1_amd64.deb
sudo apt-key adv --fetch-keys
sudo apt update
sudo apt install cuda nvidia-cuda-toolkit

And then you’ll need to reboot your system again as installing cuda will most likely bring a different set of drivers for your system.

You can then check CUDA is installed correctly by running /usr/local/cuda-10.0/bin/nvcc -V, which produces output similar to the following:

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:01_CDT_2018
Cuda compilation tools, release 10.0, V10.0.130

Being correctly installed doesn’t necessarily mean CUDA is correctly linked to the NVIDIA driver. To make sure this is working, use the bandwidthTest utility from CUDA’s demo_suite folder, usually found in ‘/usr/local/cuda-10.0/extras/demo_suite/’.

If everything is working correctly, you should see Result = PASS at the end of the output.

5. Launch LXD

With the host now correctly configured and ready to go, it’s time to launch LXD.

If this is the first time you’ve used LXD, see our setting up tutorial for a few pointers.

In particular, you will need to have a network and a storage pool defined. The lxd init command will step you through the process if you’ve not done this already.

You can then launch a fresh deployment of Ubuntu 18.04 with the following command:

lxc launch ubuntu:18.04 cuda -c nvidia.runtime=true

We’ve given this new instance the name of ‘cuda’. If this is the first time you’ve deployed an LXD instance with ‘ubuntu:18.04’, its image will be retrieved as part of the creation process.

Creating cuda
Retrieving image: rootfs: 21% (5.98MB/s)
Starting cuda

The nvidia.runtime=true has LXD setup passthrough for the NVIDIA libraries, driver utilities and CUDA library. This ensures the container and host always run the same version of the NVIDIA drivers and greatly reduces the amount of duplicated binaries between host and container.

6. Add your GPU to the container

Now let’s make all GPUs available to the container with:

lxc config device add cuda gpu gpu

At which point you can run nvidia-smi inside your container with:

lxc exec cuda -- nvidia-smi

And should get an output matching that from before.

7. Add CUDA to LXD

The nvidia.runtime property in LXD exposes both the NVIDIA utilities like nvidia-smi but also the various libraries needed to run CUDA binaries.

It however doesn’t expose the compiler, C headers or any of the other bits of the CUDA SDK.

If you want to build CUDA code inside the container, you should follow the CUDA installation instructions again, this time running them in the container.

8. Test CUDA within LXD

Whether you chose to install the CUDA SDK in the container or not, you should be able to run CUDA binaries.

So let’s transfer the bandwidthTest binary from earlier and run it inside the container.

lxc file push /usr/local/cuda-10.0/extras/demo_suite/bandwidthTest cuda/root/
lxc exec cuda -- /root/bandwidthTest

This should produce output like this:

 Bandwidth Test] - Starting...
Running on...

 Device 0: GeForce GTX 1080
 Quick Mode

 Host to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(MB/s)
   33554432                     12845.8

 Device to Host Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(MB/s)
   33554432                     12887.7

 Device to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)        Bandwidth(MB/s)
   33554432                     226363.4

Result = PASS

NOTE: The CUDA Samples are not meant for performance measurements. Results may
vary when GPU Boost is enabled.

If you see output similar to the above - congratulations! You can now start using CUDA at scale with your LXD deployment.

9. Further reading

If you’re looking for inspiration on how to take your CUDA and LXD configuration further, we’d recommend starting with NVIDIA’s CUDA Toolkit Documentation and the LXD documentation.

If your requirements outgrow your local LXD deployment, take a look using GPUs with Kubernetes for deep learning - a solution that uses some of Canonical’s best technologies.

Finding help

Finally, if you get stuck, the Ubuntu community is always willing to help, even when dealing with complex issues like CUDA.