As of December 2023, the cheapest instance with an Nvidia GPU is g4nd.xlarge, so let's try that out. In that instance, lspci contains:TODO meaning of "nd"? "n" presumably means Nvidia, but what is the "d"?
00:1e.0 3D controller: NVIDIA Corporation TU104GL [Tesla T4] (rev a1)Be careful not to confuse it with g4ad.xlarge, which has an AMD GPU instead. TODO meaning of "ad"? "a" presumably means AMD, but what is the "d"?
Some documentation on which GPU is in each instance can seen at: docs.aws.amazon.com/dlami/latest/devguide/gpu.html (archive) with a list of which GPUs they have at that random point in time. Can the GPU ever change for a given instance name? Likely not. Also as of December 2023 the list is already outdated, e.g. P5 is now shown, though it is mentioned at: aws.amazon.com/ec2/instance-types/p5/
When selecting the instance to launch, the GPU does not show anywhere apparently on the instance information page, it is so bad!
Also note that this instance has 4 vCPUs, so on a new account you must first make a customer support request to Amazon to increase your limit from the default of 0 to 4, see also: stackoverflow.com/questions/68347900/you-have-requested-more-vcpu-capacity-than-your-current-vcpu-limit-of-0, otherwise instance launch will fail with:
You have requested more vCPU capacity than your current vCPU limit of 0 allows for the instance bucket that the specified instance type belongs to. Please visit aws.amazon.com/contact-us/ec2-request to request an adjustment to this limit.
When starting up the instance, also select:Once you finally managed to SSH into the instance, first we have to install drivers and reboot:and now running:shows something like:
- image: Ubuntu 22.04
- storage size: 30 GB (maximum free tier allowance)
sudo apt update
sudo apt install nvidia-driver-510 nvidia-utils-510 nvidia-cuda-toolkit
sudo rebootnvidia-smi+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.147.05 Driver Version: 525.147.05 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| 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 Tesla T4 Off | 00000000:00:1E.0 Off | 0 |
| N/A 25C P8 12W / 70W | 2MiB / 15360MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+If we start from the raw Ubuntu 22.04, first we have to install drivers:
- docs.aws.amazon.com/AWSEC2/latest/UserGuide/install-nvidia-driver.html official docs
- stackoverflow.com/questions/63689325/how-to-activate-the-use-of-a-gpu-on-aws-ec2-instance
- askubuntu.com/questions/1109662/how-do-i-install-cuda-on-an-ec2-ubuntu-18-04-instance
- askubuntu.com/questions/1397934/how-to-install-nvidia-cuda-driver-on-aws-ec2-instance
From basically everything should just work as normal. E.g. we were able to run a CUDA hello world just fine along:
nvcc inc.cu
./a.outOne issue with this setup, besides the time it takes to setup, is that you might also have to pay some network charges as it downloads a bunch of stuff into the instance. We should try out some of the pre-built images. But it is also good to know this pristine setup just in case.
Some stuff we then managed to run:which gave:so way faster than on my local desktop CPU, hurray.
curl https://ollama.ai/install.sh | sh
/bin/time ollama run llama2 'What is quantum field theory?'0.07user 0.05system 0:16.91elapsed 0%CPU (0avgtext+0avgdata 16896maxresident)k
0inputs+0outputs (0major+1960minor)pagefaults 0swapsAfter setup from: askubuntu.com/a/1309774/52975 we were able to run:which gave:so only marginally better than on P14s. It would be fun to see how much faster we could make things on a more powerful GPU.
head -n1000 pap.txt | ARGOS_DEVICE_TYPE=cuda time argos-translate --from-lang en --to-lang fr > pap-fr.txt77.95user 2.87system 0:39.93elapsed 202%CPU (0avgtext+0avgdata 4345988maxresident)k
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