Skip to content

Latest commit

 

History

History
46 lines (40 loc) · 2.16 KB

README.md

File metadata and controls

46 lines (40 loc) · 2.16 KB

YOLONAS ML Backend for Label Studio

Intro

Use Deci AI YOLONAS model with Label Studio.

Setup

0. Important things to note

  • This ML backend is designed to work in docker. You can run in on host but this manual does not cover that.
  • There is no easy way to run ML backend with GPU support - you get an error RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
  • Single image inference on CPU takes about a second.
  • Base docker image has a jupyter lab command. So you can comment a command in docker-compose.yml, uncomment 8888:8888 for port mapping and use it as jupyter lab with password change-me over https.
  • Main tested scenario has been for s3 cloud storage with custom endpoint url. Other storage options are not guaranteed to work.

1. Clone this repo

2. Get model weights

3. Adjust variables

Adjust these variables in .env file.

CHECKPOINT_FILE="/home/testuser/app/model.pth"
PORT=9090
YOLO_LABELS=/home/testuser/app/labels.txt
IOU_THRESHOLD=0.25
SCORE_THRESHOLD=0.4
IMG_SIZE=1280
DEVICE=cpu
ENDPOINT_URL=<specify minio address http://myminio:9000>
AWS_ACCESS_KEY_ID=minio
AWS_SECRET_ACCESS_KEY=<specify minio password>
LABEL_STUDIO_HOSTNAME=<specify label studio address with port like http://mylabelstudio.com:8080 >
YOLO_MODEL_TYPE=yolo_nas_m

YOLO_LABELS=/home/testuser/app/labels.txt file with labels - each label on new line.
Labels should be the same in labeling interface and in this file. If yolo labels differ you need to provide LABELS_FILE variable with mapping from Label studio label to yolo label like {"airplane": "Boeing"}

4. Build docker image

Run docker compose build to build an image.
Base image bodbe/yolonas is built with Dockerfile.full.

5. Run ML Backend

Run docker compose up -d

6. How to run on GPU

  • Update DEVICE variable in .env file to cuda:0
  • Uncomment deploy section in docker-compose.yml
  • Change command section in docker-compose.yml to `bash -c "python _wsgi.py"