This article explains how to run inference on a YOLOv8 object detection model using Docker and create a REST API to orchestrate the process. It includes code implementation and a detailed README in the author's GitHub repository for running the API via REST with Docker.
This is a hands-on guide with Python example code that walks through the deployment of an ML-based search API using a simple 3-step approach. The article provides a deployment strategy applicable to most machine learning solutions, and the example code is available on GitHub.
tensorflow jupyter):
Download the training zip file from drive, extract it
docker run --rm -it -e JUPYTER_ENABLE_LAB=yes -p 8888:8888 -v /Users/foo/Learn/python/training:/home/jovyan/ jupyter/tensorflow-notebook:latest
Download the training zip file from drive, extract it
docker run --rm -it -e JUPYTER_ENABLE_LAB=yes -p 8888:8888 -v /Users/foo/Learn/python/training:/home/jovyan/ jupyter/tensorflow-notebook:latest
Download the Docker image and show the help text to make sure it works.
docker run --rm -v `pwd`:/ne/input -it alexjc/neural-enhance --help
docker run --rm -v `pwd`:/ne/input -it alexjc/neural-enhance --help
