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Cloud deployment


Core Module

We are now returning to using the cloud. In this module you should have gone through the steps of having your code in your github repository to automatically build into a docker container, store that, store data and pull it all together to make a training run. After the training is completed you should hopefully have a file stored in the cloud with your trained model weights.

Todays exercises will be about serving those model weights to an end user. We focus on two different ways of deploying our model, Google cloud functions and Google Vertex AI endpoints.

Cloud Functions

Cloud functions are the easiest way to get started with deployment because they are what is called serverless. For serverless deployment we still need a server to do the actual workload, however the core concept is that you do you have to manage the server. Everything is magically taken care of behind the scene.

❔ Exercises

  1. Go to the start page of Cloud Functions. Can be found in the sidebar on the homepage or you can just search for it. Activate the service if not already active.

  2. Click the Create Function button which should take you to a screen like the image below. Give it a name, set the server region to somewhere close by and change the authentication policy to Allow unauthenticated invocations so we can access it directly from a browser. Remember to note down the URL of the service somewhere.

    Image

  3. On the next page, for Runtime pick the Python 3.9 option. This will make the inline editor show both a main.py and requirements.py file. Look over them. Click the Deploy button in the lower left corner.

  4. Afterwards you should see a green check mark beside your function meaning that it is deployed. Click the Test function button which will take you to the testing page.

    Image

  5. If you know what the application does, it should come as no surprise that it can run without any input. We therefore just send an empty request by clicking the Test The Function button. Does the function return the output you expected? Wait for the logs to show up. What do they show?

    1. What should the Triggering event look like in the testing prompt for the program to respond with

      Good day to you sir!
      

      Try it out.

    2. Click on the metrics tab. Identify what each panel is showing.

    3. Go to the trigger tab and go to the url for the application.

    4. Checkout the logs tab. You should see that your application have already been invoked multiple times. Also try to execute this command in a terminal:

      gcloud functions logs read
      
  6. Next, we are going to create an application that actually takes some input so we can try to send it requests. We provide a very simple sklearn_cloud_function.py script to get started.

    1. Figure out what the script does and run the script. This should create a file with trained model.

    2. Next create a storage bucket and upload the model file to the bucket. You can either do this through the webpage or run the following commands:

      gsutil mb gs://<bucket-name>  # mb stands for make bucket
      gsutil cp <file-name> gs://<bucket-name>  # cp stands for copy
      

      check that the file is in the bucket.

    3. Create a new cloud function with the same initial settings as the first one. Choose also the Python 3.9 but this time change code to something that can actually use the model we just uploaded. Here is a code snippet to help you:

      from google.cloud import storage
      import pickle
      
      BUCKET_NAME = ...
      MODEL_FILE = ...
      
      client = storage.Client()
      bucket = client.get_bucket(BUCKET_NAME)
      blob = bucket.get_blob(MODEL_FILE)
      my_model = pickle.loads(blob.download_as_string())
      
      def knn_classifier(request):
          """ will to stuff to your request """
          request_json = request.get_json()
          if request_json and 'input_data' in request_json:
              data = request_json['input_data']
              input_data = list(map(int, data.split(',')))
              prediction = my_model.predict([input_data])
              return f'Belongs to class: {prediction}'
          else:
              return 'No input data received'
      

      Some notes: * For locally testing the above code you will need to install the google-cloud-storage python package * Remember to change the Entry point * Remember to also fill out the requirements.txt file. You need at least two packages to run the application with google-cloud-storage being one of them. * If you deployment fails, try to go to the Logs Explorer page in gcp which can help you identify why.

    4. When you have successfully deployed the model, try to make predictions with it.

  7. You can finally try to redo the exercises deploying a Pytorch application. You will essentially need to go through the same steps as the sklearn example, including uploading a trained model to a storage, write a cloud function that loads it and return some output. You are free to choose whatever Pytorch model you want.

Cloud Run

Cloud functions are great for simple deployments, that can be encapsulated in a single script with only simple requirements. However, they do not really scale with more advance applications that may depend on multiple programming languages. We are already familiar with how we can deal with this through containers and Cloud Run is the corresponding service in GCP for deploying containers.

❔ Exercises

  1. We are going to start locally by developing a small app that we can deploy. We provide two small examples to choose from: first a small FastAPI app consisting of this .py file and this dockerfile . Secondly a small streamlit application consisting of just this dockerfile . You are free to choose which application to work with.

    1. Start by going over the files belonging to your choice app and understand what it does.

    2. Next build the docker image belonging to the app

      docker build -f <dockerfile> . -t gcp_test_app:latest
      
    3. Next tag and push the image to your container registry

      docker tag gcp_test_app gcr.io/<project-id>/gcp_test_app
      docker push gcr.io/<project-id>/gcp_test_app
      

      afterwards check you container registry to check that you have successfully pushed the image.

  2. Next go to Cloud Run in the cloud console an enable the service

  3. Click the Create Service button which should bring you to a page similar to the one below

    Image

    Do the following: * Click the select button, which will bring up all build containers and pick the one you want to deploy. In the future you probably want to choose the Continuously deploy new revision from a source repository such that a new version is always deployed when a new container is build. * Hereafter, give the service a name and select the region. We recommend do choose a region close to you, however it does not really matter that much for our use case * Set the authentication method to Allow unauthenticated invocations such that we can call it without providing credentials. In the future you may only set that authenticated invocations are allowed. * Expand the Container, Connections, Security tab and edit the port such that it matches the port exposed in your chosen application.

    Finally, click the create button and wait for the service to be deployed (may take some time).

    Common problems

    If you get an error saying The user-provided container failed to start and listen on the port defined by the PORT environment variable. there are two common reasons for this:

    1. You need to add an EXPOSE statement in your docker container:

      EXPOSE 8080
      CMD exec uvicorn my_application:app --port 8080 --workers 1 main:app
      

      and make sure that your application is also listening on that port. If you hard code the port in your application (as in above code) it is best to set it 8080 which is the default port for cloud run. Alternatively, a better approach is to set it to the $PORT environment variable which is set by cloud run and can be accessed in your application:

      EXPOSE $PORT
      CMD exec uvicorn my_application:app --port $PORT --workers 1 main:app
      

      If you do this, and then want to run locally you can run it as:

      docker run -p 8080:8080 -e PORT=8080 <image-name>:<image-tag>
      
    2. If you are serving a large machine learning model, it may also be that your deployed container is running out of memory. You can try to increase the memory of the container by going to the Edit container and the Resources tab and increase the memory.

  4. If you manage to deploy the service you should see a image like this:

    Image

    You can now access you application by clicking url. This will access the root of your application, so you may need to add / or /<path> to the url depending on how the app works.

  5. Everything we just did to deploy an container can be reproduced using the following command:

    gcloud run deploy $APP --image $TAG --platform managed --region $REGION --allow-unauthenticated
    

    and checked using these two commands

    gcloud run services list
    gcloud run services describe $APP --region $REGION
    

    feel free to experiment doing the deployment from the command line.

  6. Instead of deploying our docker container using the UI or command line, which is a manual operation, we can do it in a continues manner by using cloudbuild.yaml file we learned about in the previous section. We just need to add a new step to the file. We provide an example

    steps:
    # Build the container image
    - name: 'gcr.io/cloud-builders/docker'
      args: ['build', '-t', 'gcr.io/$PROJECT_ID/<container-name>:latest', '.'] #(1)!
    # Push the container image to Container Registry
    - name: 'gcr.io/cloud-builders/docker'
      args: ['push', 'gcr.io/$PROJECT_ID/<container-name>:latest']
    # Deploy container image to Cloud Run
    - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
      entrypoint: gcloud
      args:
      - 'run'
      - 'deploy'
      - '<service-name>'
      - '--image'
      - 'gcr.io/$PROJECT_ID/<container-name>:latest'
      - '--region'
      - '<region>'
    
    1. This line assume you are standing in the root of your repository and is trying to build the docker image specified in a file called Dockerfile and tag it with the name gcr.io/$PROJECT_ID/my_deployment:latest. Therefore if you want to point to another dockerfile you need to add -f option to the command. For example if you want to point to a my_app/my_serving_app.dockerfile you need to change the line to

      args: ['build', '-f', 'my_app/my_serving_app.dockerfile', '-t', 'gcr.io/$PROJECT_ID/my_deployment:lates', '.']
      

    where you need to replace <container-name> with the name of your container, <service-name> with the name of the service you want to deploy and <region> with the region you want to deploy to. Afterwards you need to setup a trigger (or reuse the one you already have) to build the container and deploy it to cloud run. Confirm that this works by making a change to your application and pushing it to github and see if the application is updated continuously. For help you can look here for help. If you succeeded, congratulations you have now setup a continues deployment pipeline.

That ends the exercises on deployment. The exercises above is just a small taste of what deployment has to offer. In both sections we have explicitly chosen to work with serverless deployments. But what if you wanted to do the opposite e.g. being the one in charge of the management of the cluster that handles the deployed services? If you are really interested in taking deployment to the next level should get started on kubernetes which is the de-facto open-source container orchestration platform that is being used in production environments. If you want to deep dive we recommend starting here which describes how to make pipelines that are a necessary component before you start to create your own kubernetes cluster.