This repository contains instructions and key files to enable Docker support on NVIDIA Tegra devices, specifically the TX-2. These instructions should work for the most part for other Tegra devices but we currently only have TX-2 to test with so any feedback on getting this to run on other devices is welcome.
We have recently been looking into TX-2s as a development platform and were very interested in using Docker to enable of our development and deployment scenarios. I attended GTC 2017 this year and was very excited about some of the nvidia-docker integration and was eager to try these out on the TX-2. However, once I got back and tried these out I quickly learned that nvidia-docker was not supported on the TX-2 or other Tegra devices and probably would not be in the short term.
Not one to be discouraged, we set out to learn what was needed to make this happen. After some frustration and trial and error we were able to successfully get Docker running on the TX-2. Not satisfied with just getting Docker to run, we also were able to get GPU programs running in a Docker container to run.
There are several challenges we had to overcome in order to get this working correctly:
- The stock kernel that is deployed when flashing the TX-2 does not have all of the kernel options necessary for containers to work on TX-2 devices. With the release of JetPack 3.2, this has been fixed. The L4T version that is shipped with JetPack 3.2 contains the necessary kernel options for Docker to run.
- The NVIDIA drivers work much differently on the TX-2 than on a normal Linux system. The nvidia-docker wrapper written by NVIDIA will not work on the TX-2. The nvidia-docker project basically wraps Docker commands and sets up the environment correctly so that Docker containers will have access to the GPU. Without this your GPU programs will not work on TX-2.
To get this working you'll need to compile a custom L4T kernel with the correct kernel configuration to allow you to use containers. Once that is flashed and running on your device you can install Docker and then will just need to pass in specific Docker parameters to allow your Docker containers to have access to the GPU.
Based on the information in this thread on the TX-2 development board, Docker on the TX2, we were able to get Docker running on the TX-2.
There are multiple ways to compile the kernel and if you have never done this before it can be intimidating, but it isn't too difficult. If you want to compile the kernel on the TX-2 device then you can follow these instructions: buildJetsonTX2Kernel. Just make sure to use this custom config file so that it will enable the Docker options in the kernel. Just note that this is for the older kernel present in JetPack 3.0, so some of the instruction below would need to be adjusted accordingly.
We did not compile on the TX-2 but rather chose to cross compile our kernel from another Linux host. NVIDIA recommends that you use Ubuntu 14.04 for this, but we were successfully able to run using Ubuntu 16.04.
**NOTE: NVIDIA has recently released JetPack 3.2 that has Docker support built into their kernel. If you are using JetPack 3.2 you do not need to do your own custom kernel compilation to get Docker to run. Just flash your TX-2 with the latest L4T release and skip to the Docker Installation section **
To compile a custom kernel for the TX-2 on a x86 Ubuntu 16.04 machine:
NOTE: Before attempting this procedure, make sure you backup any important files from your TX-2. Updating the kernel can render your system unusable and you may need to reflash the system from JetPack to get it useable again
Create a directory called
kernel_buildon your Linux machine to contain the build files. Will refer to this directory as
$BUILD_ROOT. Change into this directory to make it your working directory.
NOTE: Might be helpful to set an environment variable called $BUILD_ROOT that points to your kernel_build directory.
Download the Latest Driver Package from NVIDIA, L4T Jetson TX2 Driver Package, 28.1 and copy to
Uncompress into your
tar -jxvf Tegra186_Linux_R28.1.0_aarch64.tbz2
- Change into the
$BUILD_ROOT/Linux_for_Tegradirectory and run the
source_sync.shscript. This will download the latest kernel sources using GIT. When prompted to enter a tag use
tegra-l4t-r28.1. You will need to enter the tag five or six different times for each of the projects needed to compile the kernel.
- Download the GCC Toolchain, l4t-gcc-toolchain-64-bit-28-1 and copy to
- Uncompress the Toolchain into a directory called
mkdir $BUILD_ROOT/toolchain tar -xvf gcc-4.8.5-aarch64.solitairetheme8 -C toolchain
NOTE: It appears that the toolchain file currently downloading is called 'gcc-4.8.5-aarch64.solitairetheme8' which is probably a mistake. If/When NVIDIA fixes this, just uncompress the correct name that was downloaded.
- Set the following environment variables
export CROSS_COMPILE=$BUILD_ROOT/toolchain/install/bin/aarch64-unknown-linux-gnu- export TEGRA_KERNEL_OUT=$BUILD_ROOT/kernel-out export ARCH=arm64
- Copy the custom kernel config file (.config into the $TEGRA_KERNEL_OUT directory.
- Change into the kernel source directory
- Compile the Kernel Image
make O=$TEGRA_KERNEL_OUT zImage
- Create the Kernel Device Trees (DTB)
make O=$TEGRA_KERNEL_OUT dtbs
- Make the Kernel Modules
make O=$TEGRA_KERNEL_OUT modules make O=$TEGRA_KERNEL_OUT modules_install INSTALL_MOD_PATH=$TEGRA_KERNEL_OUT/modules
- Archive the kernel modules
cd $TEGRA_KERNEL_OUT/modules tar --owner root --group root -cjf kernel_supplements.tbz2 *
- Assuming all went well you have successfully compiled the custom kernel. Next step is to copy the kernel files to your TX-2.
Copy Kernel Files to TX-2
- You should have already flashed your TX-2 with the base image and kernel that is provided with JetPack. Your TX-2 should be booted and connected to the network so you can SCP the kernel files.
- SCP the
$TEGRA_KERNEL_OUT/arch/arm64/boot/zImagefiles to the TX-2 into the
- Replace all of the files in the
/boot/dtbdirectory on the TX-2 with the files from
$TEGRA_KERNEL_OUT/arch/arm64/boot/dtsdirectory from the host machine
- Copy the
$TEGRA_KERNEL_OUT/modules/kernel_supplements.tbz2file to the TX-2 into the root directory
- Uncompress the
/kernel_supplements.tbz2file on the TX-2:
cd / sudo tar -jxvf kernel_supplements.tbz2
- Reboot the TX-2 for the new kernel to take effect
- After the reboot, you should be able to log into the system. Check the kernel version to make sure it was updated successfully:
Should show it is running
Now that the kernel is updated you can install Docker.
- Add the following line to the bottom of your
deb [arch=arm64] https://download.docker.com/linux/ubuntu xenial stable
- Update the package lists
sudo apt-get update
- Install Docker
sudo apt-get install docker.io
This should install Docker 1.12 on the system. It is possible to install and run the latest version of Docker, 1.17, but you will need to compile from source. See install instructions from the Docker site for information on how to compile Docker from source.
At this point you should be able to run CPU based Docker containers on your TX-2. Just make sure you are using images based on arm64v8. You can test your Docker installation by running the Hello World Container:
docker run arm64v8/hello-world
If you want to run something more interesting, you can run the Ubuntu image:
docker run -it arm64v8/ubuntu /bin/bash
However, if you try to run a GPU program within a Docker container it will result in an error.
Simple GPU Docker Image
Let's build a simple image with deviceQuery so that we can test Docker's ability to run GPU programs. This requires that you installed the CUDA package on your TX-2 via JetPack. While this isn't necessary to run all CUDA programs, it is a good idea to have this installed on the base system. If you haven't already done so, use JetPack to install CUDA on your target TX-2.
- Build the deviceQuery sample which is located in /usr/local/cuda/samples/1_Utilities/deviceQuery
cd /usr/local/cuda/samples/1_Utilities/deviceQuery sudo make
This will create the deviceQuery executable. If you run this on the native machine, it will give you information about the GPU on the TX-2
./deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "GP10B" CUDA Driver Version / Runtime Version 8.5 / 8.0 CUDA Capability Major/Minor version number: 6.2 Total amount of global memory: 7852 MBytes (8233689088 bytes) ( 2) Multiprocessors, (128) CUDA Cores/MP: 256 CUDA Cores GPU Max Clock rate: 1301 MHz (1.30 GHz) Memory Clock rate: 13 Mhz Memory Bus Width: 64-bit L2 Cache Size: 524288 bytes Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384) Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total number of registers available per block: 32768 Warp size: 32 Maximum number of threads per multiprocessor: 2048 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 1 copy engine(s) Run time limit on kernels: No Integrated GPU sharing Host Memory: Yes Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Disabled Device supports Unified Addressing (UVA): Yes Device PCI Domain ID / Bus ID / location ID: 0 / 0 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.5, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = GP10B Result = PASS
- Create a directory for the image creation
- Copy the /usr/local/cuda/samples/1_Utilities/deviceQuery/deviceQuery executable to this directory
- Copy this Dockerfile to the directory
- Create the image
docker build -t device_query .
- Run the image
docker run device_query
You should see the following output:
/cudaSamples/deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) cudaGetDeviceCount returned 35 -> CUDA driver version is insufficient for CUDA runtime version Result = FAIL
This is because the CUDA driver and the GPU device are not visible to the Docker container.
Giving Docker Access to GPU
On a normal x86 or x64 machine, the nvidia-docker project would give your container access to the GPU. However, NVIDIA has made it clear that they will not currently support Tegra devices. Unfortunately, it is not as easy as compiling nvidia-docker on the TX-2 and utilizing this to give GPU access to containers. The NVIDIA driver is very different on Tegra devices than on a normal system with an external GPU. There are also libraries, such as NVML, which nvidia-docker uses but are not supported on Tegra devices.
Fortunately it is possible to give containers access to the GPU by passing in some specific libraries and giving access to the devices related to the GPU to the container.
Docker containers needs to have access to the following device files on the host:
These can be passed to the container using the
Driver Library Files
The Containers also need access to the drivers. For Tegra these are located in
/usr/lib/aarch64-linux-gnu/tegra. You should add this to the container using the
-v command line switch.
/usr/lib/aarch64-linux-gnu/tegra directory contains libraries that will be loaded dynamically by the CUDA appliations. This path should also be added to the LD_LIBRARY_PATH environment variable in your Dockerfile as well.
Will most likely also need access to other libraries depending on your GPU program. Specifically you will need access to the CUDA runtime libraries. Up to you if you want to install the CUDA libraries on the host and pass those through to the container as a volume or to build that into the image.
Running Device Query
With the above information, you can now run the device_query container we built above by passing in the correct parameters to the Docker run command:
docker run --device=/dev/nvhost-ctrl --device=/dev/nvhost-ctrl-gpu --device=/dev/nvhost-prof-gpu --device=/dev/nvmap --device=/dev/nvhost-gpu --device=/dev/nvhost-as-gpu -v /usr/lib/aarch64-linux-gnu/tegra:/usr/lib/aarch64-linux-gnu/tegra device_query
This should result in deviceQuery successfully being run inside of the Docker container.
We've created a very simple wrapper script called tx2-docker that will wrap your Docker commands with the specific command line parameters needed to give GPU access to Docker containers. This is a VERY simplified version of what the nvidia-docker project does.
To launch a Docker container that needs GPU access just run:
tx2-docker run <image_name>. If you container needs any additional libraries, just need to add the directory or library to the
NV_LIBS variable for it to be included as a volume.
Containerizing Graphic Programs
If you are trying to containerize an application that displays in a window, you will need to run your container in host mode (
--net=host) and you will have to make sure that your xserver is accepting connections from other hosts (
xhost+). As an example, follow these steps to run the Particles example from the CUDA distribution:
- Change to the particles example directory in your CUDA distribution:
% cd /usr/local/cuda/samples/5_Simulations/particles
- Compile the particles executible:
- Create a directory for the image creation
- Copy the
/usr/local/cuda/samples/5_Simulations/particles/particlesexecutable to this directory
- Copy this Dockerfile to the directory
- Create the image
% docker build -t particles .
- Allow remote x hosts
% xhost +
- Run the image
% tx2-docker run particles