Google BigQuery alternative.
Let's get SSH access, instal a package, and run a server.
As of December 2023 on a t2.micro instance, the only one part of free tier at the time with advertised 1 vCPU, 1 GiB RAM, 8 GiB disk for the first 12 months, on Ubuntu 22.04:
$ free -h
               total        used        free      shared  buff/cache   available
Mem:           949Mi       149Mi       210Mi       0.0Ki       590Mi       641Mi
Swap:             0B          0B          0B
$ nproc
$ df -h /
Filesystem      Size  Used Avail Use% Mounted on
/dev/root       7.6G  1.8G  5.8G  24% /
To install software:
sudo apt update
sudo apt install cowsay
cowsay asdf
Once HTTP inbound traffic is enabled on security rules for port 80, you can:
while true; do printf "HTTP/1.1 200 OK\r\n\r\n`date`: hello from AWS" | sudo nc -Nl 80; done
and then you are able to curl from your local computer and get the response.
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:
00:1e.0 3D controller: NVIDIA Corporation TU104GL [Tesla T4] (rev a1)
TODO meaning of "nd"? "n" presumably means Nvidia, but what is the "d"?
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: (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 Also as of December 2023 the list is already outdated, e.g. P5 is now shown, though it is mentioned at:
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:, 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 to request an adjustment to this limit.
When starting up the instance, also select:
  • image: Ubuntu 22.04
  • storage size: 30 GB (maximum free tier allowance)
Once you finally managed to SSH into the instance, first we have to install drivers and reboot:
sudo apt update
sudo apt install nvidia-driver-510 nvidia-utils-510 nvidia-cuda-toolkit
sudo reboot
and now running:
shows something like:
| 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                                                 |
From basically everything should just work as normal. E.g. we were able to run a CUDA hello world just fine along:
One 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:
curl | sh
/bin/time ollama run llama2 'What is quantum field theory?'
which gave:
0.07user 0.05system 0:16.91elapsed 0%CPU (0avgtext+0avgdata 16896maxresident)k
0inputs+0outputs (0major+1960minor)pagefaults 0swaps
so way faster than on my local desktop CPU, hurray.
After setup from: we were able to run:
head -n1000 pap.txt | ARGOS_DEVICE_TYPE=cuda time argos-translate --from-lang en --to-lang fr > pap-fr.txt
which gave:
77.95user 2.87system 0:39.93elapsed 202%CPU (0avgtext+0avgdata 4345988maxresident)k
0inputs+88outputs (0major+910748minor)pagefaults 0swaps
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.
These come with pre-installed drivers, so e.g. nvidia-smi just works on them out of the box, tested on g5.xlarge which has an Nvidia A10G GPU. Good choice as a starting point for deep learning experiments.
The hot and more expensive sotorage for Amazon EC2, where e.g. your Ubuntu filesystem will lie.
The cheaper and slower alternative is to use Amazon S3.
Large but ephemeral storage for EC2 instances. Predetermined by the EC2 instance type. Stays in the local server disk. Not automatically mounted.

Articles by others on the same topic (0)

There are currently no matching articles.