Lab 8: Web Deployment

Goal of the lab

• Run our LinePredictor as a web app, and send it some requests
• Dockerize our web app
• Deploy our web app to production

Follow along

git pull
cd lab8/

This lab has quite a few new files, mostly in the new api/ directory.

Serving predictions from a web server

First, we will get a Flask web server up and running and serving predictions.

python api/app.py

Open up another terminal tab (click on the '+' button under 'File' to open the launcher). In this terminal, we'll send some test image to the web server we're running in the first terminal.

Make sure to cd into the lab8 directory in this new terminal.

export API_URL=http://0.0.0.0:8000
curl -X POST "${API_URL}/v1/predict" -H 'Content-Type: application/json' --data '{ "image": "data:image/png;base64,'$(base64 -w0 -i text_recognizer/tests/support/emnist_lines/or\ if\ used\ the\ results.png)'" }'

If you want to look at the image you just sent, you can navigate to lab8/text_recognizer/tests/support/emnist_lines in the file browser on the left, and open the image.

We can also send a request specifying a URL to an image:

curl "${API_URL}/v1/predict?image_url=http://s3-us-west-2.amazonaws.com/fsdl-public-assets/emnist_lines/or%2Bif%2Bused%2Bthe%2Bresults.png"

You can shut down your flask server now.

Adding web server tests

The web server code should have a unit test just like the rest of our code.

Let's check it out: the tests are in api/tests/test_app.py. You can run them with

tasks/test_api.sh

Running web server in Docker

Now, we'll build a docker image with our application. The Dockerfile in api/Dockerfile defines how we're building the docker image.

Still in the lab8 directory, run:

tasks/build_api_docker.sh

This should take a couple of minutes to complete.

When it's finished, you can run the server with tasks/run_api_docker.sh

You can run the same curl commands as you did when you ran the flask server earlier, and see that you're getting the same results.

curl -X POST "${API_URL}/v1/predict" -H 'Content-Type: application/json' --data '{ "image": "data:image/png;base64,'$(base64 -w0 -i text_recognizer/tests/support/emnist_lines/or\ if\ used\ the\ results.png)'" }'

curl "${API_URL}/v1/predict?image_url=http://s3-us-west-2.amazonaws.com/fsdl-public-assets/emnist_lines/or%2Bif%2Bused%2Bthe%2Bresults.png"

If needed, you can connect to your running docker container by running:

docker exec -it api bash

You can shut down your docker container now.

We could deploy this container to a number of platforms. In this lab, we will deploy the app as a Docker container using https://render.com

Web deployment

TODO: render.com

As before, we can test out our API by running a few curl commands (from the lab8 directory). We need to change the API_URL first though to point it at Lambda:

export API_URL="https://REPLACE_THIS.execute-api.us-west-2.amazonaws.com/dev/"
curl -X POST "${API_URL}/v1/predict" -H 'Content-Type: application/json' --data '{ "image": "data:image/png;base64,'$(base64 -w0 -i text_recognizer/tests/support/emnist_lines/or\ if\ used\ the\ results.png)'" }'
curl "${API_URL}/v1/predict?image_url=http://s3-us-west-2.amazonaws.com/fsdl-public-assets/emnist_lines/or%2Bif%2Bused%2Bthe%2Bresults.png"

If the POST request fails, it's probably because you are in api and not in the top-level lab8 directory.

You'll want to run the curl commands a couple of times -- the first execution may time out, because the function has to "warm up." After the first request, it will stay warm for 10-60 minutes.