Skip to content

Latest commit

 

History

History
149 lines (129 loc) · 6.31 KB

README.md

File metadata and controls

149 lines (129 loc) · 6.31 KB

Model Deployment

Intro

In this module we'll learn how to deploy a model in production. We'll learn how to create a REST API that will serve our model predictions. We'll also learn how to dockerize the API. Finally we'll learn how to use locust to run some load tests.

Model Deployment lab

Goal of the lab

We want to have a REST API running that can predict trip duration for the NYC Taxi given the pickup location ID, the drop off location ID and the number of passengers.

Lab

Once the infra is running (make prepare-mlops-crashcourse and make launch-mlops-crashcourse)

  1. Go inside jupyter container (starting in /app directory)

    • either by using VSCODE's Remote Explorer extension (you can download it here) and attaching to running container
    • or simply by going to the jupyter lab on http://localhost:10000 on your browser

  2. Go to lessons/05-model-deployment:

    cd lessons/05-model-deployment/

  3. Initialize the course:

    make init_course_model_deployment
    Details on the init
    The init will do the following:
    • install the dependencies for this lesson
    • pull the data from internet
    • build a local model that is saved in `web_service/local_models/`
    • copy this model to the shared volume
    • push a model to the running MLFlow server and register it as production

  4. Go to the web_service directory where there will be a local model already pushed to web_service/local_models/

    cd web_service

  5. You can run the api locally by running the following command:

    uvicorn main:app --reload --host 0.0.0.0 --port 8000

    Doing so you'll have the api running on http://localhost:8000 since this port is forwarded to the host's port 8000 you can interact with the api from your host machine.

    1. You can go to http://localhost:8000/docs to see the documentation of the api (where you can also test the api)

    2. You can also use curl to test the api's health : curl http://localhost:8000

    3. You can send requests to the running api using python (example inside container opening another terminal):

      cd /app/lessons/05-model-deployment/ &&
python bin/post_payload.py

    4. You can also send a request to the api using curl:

      curl -X POST 'http://localhost:8000/predict'\
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "PULocationID": 264,
  "DOLocationID": 264,
  "passenger_count": 2}'
      • (optional) create payload file creating a file test_payload.json with the following content:
      {"PULocationID": 264,
 "DOLocationID": 264,
 "passenger_count": 2}
      • or using cat:
      cat << EOF > test_payload.json
{
"PULocationID": 264,
"DOLocationID": 264,
"passenger_count": 2
}
EOF
      • then you can send request using the payload file:
      curl -X POST \
  'http://localhost:8000/predict' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d @test_payload.json

  6. Now that you understood how to create an api locally, you can create a docker image for the api. Since we're working with code that is inside the running container, you you'll to run the following commands from the host machine (not inside the container):

    1. Build the docker image (you should be inside the 05-model-deployment directory):

      • first you must move the model pipeline__v0.0.1.joblib that was copied to the shared volume during the init of the course from infra/mlflow_server/local/ to web_service/local_models/: 
        mv ../../infra/mlflow_server/local/pipeline__v0.0.1.joblib web_service/local_models/
      • then you can build the docker image: 
        docker build -t prediction_server -f Dockerfile.app .

    2. Run the docker image:

      docker run -itd --rm --name prediction_server -p 8001:8001 --network mlops-crashcourse-supinfo prediction_server

    3. Check if the container is running:

      docker ps

    4. Check if the api is running:

      • go to http://localhost:8001/docs to see the documentation of the api (where you can also test the api)

    5. You can send requests to the running dockerized api using python (example inside container opening another terminal):

      • change the host in /app/lessons/05-model-deployment/bin/post_payload.py to http://prediction_server:8001

      • then just like previously you can run:

        cd /app/lessons/05-model-deployment/ &&
python bin/post_payload.py

    6. you can stop the prediction server from the host machine:

      docker stop prediction_server

    7. you can remove the prediction server image from the host machine:

      docker image rm prediction_server

  7. Locust inside the jupyter container (you should be inside the 05-model-deployment directory):

    1. Run locust targeting the api running:

      locust -f ./locust/locustfile.py --host=http://localhost:8000

    2. Go to http://localhost:8089 on your browser to see the locust dashboard.

      • You can define the target number of users (peak concurrency), the spawn rate (users started/second) and how long the test should run (you might need to expend the Advanced options item).

    3. Start the swarm

    4. You can see the results of the test on the dashboard. And save the results to a file by clicking on the Download data tab and Download Report.

Your turn!

Once you're done with this simple version, your turn to build a more complex version of the api that pulls the model from the running MLFlow server.

Note: You can find the solution to this lab in the solution.zip.