akerbp-mlops 0.20210208134108

MLOps framework

MLOps Framework

This is a framework for MLOps that deploys models as functions in Cognite Data Fusion or api's in Google Cloud Run.

User Guide

Getting Started:

Follow these steps:

• Install package: pip install akerbp.mlops
• Define the following environmental variables (e.g. in .bashrc):

  export ENV=dev
  export COGNITE_API_KEY_PERSONAL=xxx
  export COGNITE_API_KEY_FUNCTIONS=$COGNITE_API_KEY_PERSONAL
  export COGNITE_API_KEY_DATA=$COGNITE_API_KEY_PERSONAL
  export COGNITE_API_KEY_FILES=$COGNITE_API_KEY_PERSONAL
  export GOOGLE_PROJECT_ID=xxx # If deploying to Google Cloud Run
  

• Set up pipeline file in your repo's root folder:

  from akerbp.mlops.core.setup import setup_pipeline
  setup_pipeline()
  

• Become familiar with the model template (see folder model_code) and make sure your model follows the same interface and file structure (described later)
• Copy config file mlops_settings.py from MLOps repo to your repo's root folder and fill in user settings
• Commit the pipeline and settings files to your repo
• Follow or request the Bitbucket setup (described later)

A this point every git push in master branch will trigger a deployment in the test environment. More information about the deployments pipelines is provided later.

General Guidelines

Users should consider the following general guidelines:

• Model artifacts should not be committed to the repo. Folder model_artifact does store model artifacts for the model defined in model_code, but it is just to help users understand the framework
• Follow the recommended file and folder structure (described later)
• There can be several models in your repo: they need to be registered in the settings, and then they need to have their own model and test files
• Follow the import guidelines (described later)
• Make sure the prediction service gets access to model artifacts (described later)

Files and Folders Structure

All the model code and files should be under a single folder, e.g. model_code. Required files in this folder:

• model.py: implements the standard model interface
• test_model.py: tests to verify that the model code is correct and to verify correct deployment
• requirements.model: libraries needed (with specific version numbers), can't be called requirements.txt. Note that you need to add the MLOps framework.

The following structure is recommended for projects with multiple models:

• model_code/model1/
• model_code/model2/
• model_code/common_code/

This is because when deploying a model, e.g. model1, the top folder in the path (model_code in the example above) is copied and deployed, i.e. common_code folder (assumed to be needed by model1) is included. Note that model2 folder would also be deployed (this is assumed to be unnecessary but harmless).

Import Guidelines

The repo's root folder is the base folder when importing. For example, assume you have these files in the folder with model code:

• model_code/model.py
• model_code/helper.py
• model_code/data.csv

If model.py needs to import helper.py, use: import model_code.helper. If model.py needs to read data.csv, the right path is os.path.join('model_code', 'data.csv').

It's of course possible to import from the Mlops package, e.g. its logger:

from akerbp.mlops.core import logger 
logging=logger.get_logger()
logging.debug("This is a debug log")

Deployment Platform

Model services (described below) can be deployed to either CDF or GCR, independently.

Services

We consider two types of services: prediction and training.

Deployed services can be called with

from akerbp.mlops.xx.helpers import call_function
output = call_function(function_name, data)

Where function_name follows the structure model-service-env:

• model: model name given by the user (settings file)
• service: either training or prediction
• env: either dev, test or prod (depending on the deployment environment)

The output has a status field (ok or error). If they are 'ok', they have also a prediction or training field (depending on the type of service). The former is determined by the predict method of the model, while the latter combines artifact metadata and model metadata produced by the train function. Prediction services have also a model_id field to keep track of which model was used to predict.

Model Artifacts for the Prediction Service

Prediction services are deployed with model artifacts so that they are available at prediction time (downloading would require waiting time, and files written during run time consume ram memory).

Model artifacts are segregated by environment (e.g. only production models can be deployed to production). Model artifacts are versioned and stored in CDF Files together with user-defined metadata. Uploading a new model increases the version count by 1 for that model and environment. It's important not to delete model files manually, since that would mess with the model manager. When deploying a model service, the latest model version is chosen (however, we can discuss the possibility of deploying specific versions or filtering by metadata).

The general rule is that model artifacts have to be uploaded manually before deployment. If there are multiple models, you need to do this one at at time. Code example:

from akerbp.mlops.cdf.helpers import set_up_cdf_client
from akerbp.mlops.cdf.helpers import upload_new_model_version 

set_up_cdf_client()
metadata = train(model_dir, secrets) # or define it directly

folder_info = upload_new_model_version(
  model_name, 
  env,
  folder_path, 
  metadata
)

Note that model_name corresponds to one of the elements in model_names defined in mlops_settings.py, env is the target environment (where the model should be available), folder_path is the local model artifact folder and metadata is a dictionary with artifact metadata, e.g. performance, git commit, etc. Each model update adds a new version (environment dependent) and note that updating a model doesn't modify the models used in existing prediction services.

Recommended process to update a model:

1. New model features implemented in a feature branch
2. New artifact generated and uploaded to test environment
3. Feature branch merged with master
4. Test deployment is triggered automatically: prediction service is deployed to test with the latest artifacts
5. Prediction service in test is verified, and if things go well
6. New artifact uploaded to prod environment
7. Production deployment is triggered manually: prediction service is deployed to prod with the latest artifacts

However, in projects with a training service, you can rely on it to upload a first version of the model. The first prediction service deployment will fail, but you can deploy again after the training service has produced a model.

Another exception is that, when you deploy from the development environment (covered later in this document), the model artifacts in the settings file can point to existing local folders. These will then be used for the deployment. Version is then fixed to model_name/dev/1. Note that these artifacts are not uploaded to CDF Files.

Local Testing and Deployment

It's possible to tests the functions locally, which can help you debug errors quickly. This is recommended before a deployment. From your repo's root folder:

• python -m pytest model_code (replace model_code by your model code folder name)
• bash deploy_prediction_service.sh
• bash deploy_training_service.sh (if there's a training service)

The first one will run your model tests. The last two run model tests but also the service tests implemented in the framework and simulate deployment.

If you really want to deploy from your development environment, you can run this: LOCAL_DEPLOYMENT=True bash deploy_prediction_service.sh

Automated Deployments from Bitbucket

Deployments to the test environment are triggered by commits (you need to push them). Deployments to the production environment are enabled manually from the Bitbucket pipeline dashboard. Branches that match 'deploy/*' behave as master.

It is assumed that most projects won't include a training service. A branch that matches 'mlops/*' deploys both prediction and training services. If a project includes both services, the pipeline file could instead be edited so that master deployed both services.

It is possible to schedule the training service in CDF, and then it can make sense to schedule the deployment pipeline of the model service (as often as new models are trained)

Bitbucket Setup

The following environments need to be defined in repository settings > deployments:

• test deployments: test-prediction and test-training, each with ENV=test
• production deployments: production-prediction and production-training, each with ENV=prod

The following need to be defined in respository settings > repository variables: COGNITE_API_KEY_DATA, COGNITE_API_KEY_FUNCTIONS, COGNITE_API_KEY_FILES (these should be CDF keys with access to data, functions and files). If deployment to GCR is needed, you need in addition: ENABLE_GC_DEPLOYMENT (set to True), GOOGLE_SERVICE_ACCOUNT_FILE (content of the service account id file) and GOOGLE_PROJECT_ID (name of the project)

The pipeline needs to be enabled.

Developer/Admin Guide

MLOps Files and Folders

These are the files and folders from the MLOps framework:

• mlops_settings.py contains the user settings
• Folder model_code is a model template included to show the model interface. It is not needed by the framework, but it is recommended to become familiar with it.
• model_artifact stores the artifacts for the model shown in model_code. This is to help to test the model and learn the framework.
• mlops contains deployment code
• bitbucket-pipelines.yml describes the deployment pipeline in Bitbucket

Build and Upload Package

Edit setup.py file and note the following:

• Register dependencies
• Bash scripts will be installed in a bin folder in the PATH. Create an account in pypi, then create a token and a $HOME/.pypirc file.

The pipeline is setup to build the library from Bitbucket, but it's possible to build and upload the library from the development environment as well:

bash build.sh

This should usually be done before LOCAL_DEPLOYMENT=True bash deploy_xxx_service.sh. The exception is if local changes affect only the deployment part of the library, and the library has been installed in developer mode with:

pip install -e .

In this mode, the installed package links to the source code, so that it can be modified without the need to reinstall).

Bitbucket Setup

In addition to the user setup, the following is needed to build the package:

• test-pypi: ENV=test, TWINE_USERNAME=__token__ and TWINE_PASSWORD (token generated from pypi)
• prod-pypi: ENV=prod, TWINE_USERNAME=__token__ and TWINE_PASSWORD (token generated from pypi, can be the same as above)

Google Cloud Setup

In order to deploy to Google Cloud Run, you need to create a service account with the following rights:

• Cloud Build Service Account
• Service Account Admin
• Service Account User
• Cloud Run Admin
• Viewer

You also need to create the CDF secret mlops-cdf-keys. It's a string that can be evaluated in python to get a dictionary (same used in the cdf helpers file). This is because:

• It needs to be passed to prediction and training services
• Model registry uses CDF Files in its

Calling FastApi services

Bash: install httpie, then:

http -v POST http://127.0.0.1:8000/train data='{"x": [1,-1],"y":[1,0]}'

Python: challenging when posting nested json with requests. This works:

import requests, json
data = {"x":[1,-1], "y":[1,0]}
requests.post(model_api, json={'data': json.dumps(data)})