My Cyberpunk Weekend - Part 3: Using Docker and GPUs
Table of Contents
I’m working on running LocalAI. But I feel like running that out of Docker.
So how to use a GPU with Docker (on Linux).
First, need the nvidia-docker2 driver. Otherwise you get an error like this:
docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].
So install that.
sudo apt install nvidia-docker2
I had a fun thing to fix in that I had added some things to the “daemon.json” so had to fix that.
$ sudo dpkg --configure -a
Setting up nvidia-docker2 (2.13.0-1) ...
Configuration file '/etc/docker/daemon.json'
==> File on system created by you or by a script.
==> File also in package provided by package maintainer.
What would you like to do about it ? Your options are:
Y or I : install the package maintainer's version
N or O : keep your currently-installed version
D : show the differences between the versions
Z : start a shell to examine the situation
The default action is to keep your current version.
*** daemon.json (Y/I/N/O/D/Z) [default=N] ? D
--- /etc/docker/daemon.json 2023-04-10 15:23:11.735382489 -0400
+++ /etc/docker/daemon.json.dpkg-new 2023-03-31 09:10:49.000000000 -0400
@@ -1,4 +1,8 @@
{
- "registry-mirrors": ["http://10.8.24.123"],
- "insecure-registries": ["https://some.registry"]
+ "runtimes": {
+ "nvidia": {
+ "path": "nvidia-container-runtime",
+ "runtimeArgs": []
+ }
+ }
}
Next, I have two NVIDIA GPUS, one old one and one newer, better one, the 3090, which is what I want to be using for LLMs.
So, locally I have two, as shown below.
$ nvidia-smi
Mon Dec 18 11:35:39 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.223.02 Driver Version: 470.223.02 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:06:00.0 Off | N/A |
| 0% 32C P8 12W / 350W | 10MiB / 24268MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 NVIDIA GeForce ... Off | 00000000:07:00.0 N/A | N/A |
| 44% 71C P0 N/A / N/A | 2574MiB / 3015MiB | N/A Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1445 G /usr/lib/xorg/Xorg 4MiB |
| 0 N/A N/A 3231 G /usr/lib/xorg/Xorg 4MiB |
+-----------------------------------------------------------------------------+
But we can specify to use “device=0” only in the container, so we should only see one GPU.
$ docker run -it --gpus "device=0" nvidia/cuda:11.4.3-base-ubuntu20.04 nvidia-smi
Mon Dec 18 16:33:29 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.223.02 Driver Version: 470.223.02 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:06:00.0 Off | N/A |
| 0% 32C P8 10W / 350W | 10MiB / 24268MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
BOOM!
One of the hard parts is figuring out what tag to use on the NVIDIA image. They are all listed here:
Examples:
11.4.3-base-ubuntu20.04 (11.4.3/ubuntu20.04/base/Dockerfile)
11.4.3-cudnn8-devel-ubuntu20.04 (11.4.3/ubuntu20.04/devel/cudnn8/Dockerfile)
11.4.3-cudnn8-runtime-ubuntu20.04 (11.4.3/ubuntu20.04/runtime/cudnn8/Dockerfile)
11.4.3-devel-ubuntu20.04 (11.4.3/ubuntu20.04/devel/Dockerfile)
11.4.3-runtime-ubuntu20.04 (11.4.3/ubuntu20.04/runtime/Dockerfile)
Note that these will change over time, of course. But if Docker reports it can’t find the tag, it’s likely because the tag is wrong, or has changed.