Download the whole stuff from git-hub:
Build docker:
- sudo docker build -t gunicorn-ml-image ./
Run it:
- sudo docker run -d --name ML-SERVICE -p 80:80 -e GUNICORN_CONF="/app/custom_gunicorn_conf.py" gunicorn-ml-image
Test it (from tests directory invoke):
- curl -X POST -k "http://localhost:80/api/v1/analyze" -H "Content-Type:application/json" -H "Expect:" -d @ar.json
- curl -X GET -k "http://localhost:80/api/v1/healthcheck" -H "Content-Type:application/json"
Debugging Uncomment logging pertinent lines in 'main.py' and following lines in 'custom_gunicorn_conf.py' #errorlog = "gunicorn_error.log" #accesslog = "gunicorn_access.log" #access_log_format = '%(h)s %(l)s %(u)s %(t)s "%(r)s" %(s)s %(b)s "%(f)s" "%(a)s"
Send requests, then log into docker container by
- docker exec -it docker-container-id /bin/bash then view content of /app/gunicorn_error.log, /app/gunicorn_access.log
Or alternatively, invoke in somewhere
- docker logs -f docker-container-id &> docker.log & Send requests. Observer content of 'docker.log' file under folder from where 'docker logs' command has been started
Installation and configuring outside docker Tested on Ubuntu 14
- install all requirements mentioned in Dockerfile
- install 'gunicorn' and 'meinheld' wsgi servers: pip3 install -U meinheld pip3 install -U gunicorn
- from under 'app' folder run: gunicorn -k egg:meinheld#gunicorn_worker -c custom_gunicorn_conf.py main:app
Resources
All resources, needed for classification documents on the base of the pipe, created in the training and testing phase, should be placed into the folder /app/resources. Sample content of this folder is shown on the image below.
Please observe that content of this folder should manually copied from training / testing environment upon completion of training / testing process. For more details please see: https://github.com/tomerm/MLClassification
Resources collected by the process Collector, configured with the option saveResources=yes.