This repository contains a CUDA-Enabled Pub/Sub Application in Docker (cepad)
- You have installed and configured Docker.
- You have installed Nvidia drivers.
- You have installed
nvidia-container-toolkit.
A way of knowing that you meet the pre-requisites is executing the following command:
sudo docker run --rm --gpus all pytorch/pytorch:1.11.0-cuda11.3-cudnn8-runtime nvidia-smi
You should see something similar to the following nvidia-smi output:
Click here to see the sample `nvidia-smi` output.
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.54 Driver Version: 510.54 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| 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 | 00000000:09:00.0 Off | N/A |
| 27% 29C P8 6W / 180W | 11MiB / 8192MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 NVIDIA GeForce ... Off | 00000000:41:00.0 Off | N/A |
| 0% 36C P8 18W / 250W | 586MiB / 11264MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1288 G 4MiB |
| 0 N/A N/A 166699 G 4MiB |
| 1 N/A N/A 1288 G 102MiB |
| 1 N/A N/A 166699 G 448MiB |
| 1 N/A N/A 166828 G 12MiB |
| 1 N/A N/A 167726 G 9MiB |
+-----------------------------------------------------------------------------+
Create a virtual environment using venv and install dependencies.
python -m venv .venv # .venv is ignored by .gitignore
source .venv/bin/activate
pip install -r requirements.txt
There are two components in this repository. One of them is a worker that subscribes to a Redis server and publishes images to it after it processes each one of them. The other component allows to send messages and save the received images locally.
To run the image processor, use this script:
python cepad/main.py
To run the image sender/receiver, use this other script:
python script/send_and_receive.py
Below, you can see an event diagram of the program flow. If the diagram does not render correctly, check the README.md raw file.
> 0. Subscribes to 'image_res' topics
> 1. User sends prompt
┌───────────────────┐
│ │ 7. Receives images through 'image_res' topic
│ Python Process │
┌──┤ (Client) ├─┐ and saves it
│ │ │ │
│ └───────────────────┘ │
│ │
│ │
┌─────┴─────┐ ┌──────┴────┐
│ │ │ │
> 3. Publish to 'jobs' │ Pub/Sub │ │ Queue │ > 2. Posts to 'jobs' queue
│ │ │ │
└─────┬─────┘ └─────┬─────┘
│ │
│ │
│ │
│ │
│ │
│ ▲ │
> 4. Sub triggers queue fetch │ │ 6. Send image as │ > 5. All nodes pop the queue to try to get
│ │ │
│ │ bytes through the │ the latest job. Jobs are served on a
│ │ │
│ │ 'image_res' topic │ first come first served basis.
│ │
┌─────┴───────────────────────┴──────┐
│ │
│ Worker Group │
│ │
│ ┌────────────────┐ │
│ │ │ │
│ │ Worker Node #1 │ │
│ │ │ │
│ └────────────────┘ │ > 0. Subscribes to 'jobs' topic
│ │
│ ┌────────────────┐ │
│ │ │ │
│ │ Worker Node #2 │ │
│ │ │ │
│ └────────────────┘ │
│ │
└────────────────────────────────────┘
sudo docker run --name redis -p 6379:6379 -d redis