Deploying a Batch Job¶
This tutorial refers to files within a Bodywork template project hosted on GitHub - check it out here. If you want to execute the examples, you will need to have setup access to a Kubernetes cluster and installed bodywork on your local machine.
We strongly recommend that you find five minutes to read about the key concepts that Bodywork is built upon, before beginning to work-through the examples below.
Working with private repositories
If you've cloned the example project into a private repository and intend to use it for this tutorial, then you will need to follow the necessary configuration steps detailed here.
What am I going to Learn?¶
- How to take a Python module defining a ML task (or job) and deploy it to Kubernetes.
- How to test the deployment.
- How to run the job on a schedule, without the manual intervention of an ML engineer.
Scoring a Dataset with a Model¶
The example task that we want to deploy as a batch job, is to load a pre-trained model and use it to score a dataset. The latest dataset will be downloaded from cloud storage (AWS S3) and the pre-trained model will be bundled with the executable Python module defining the job - this is for convenience (not as a best practice).
The project for this single-stage workflow is packaged as a GitHub repository and is structured as follows,
root/ |-- score_data/ |-- score.py |-- classification_model.joblib |-- bodywork.yaml
Configuring the Job¶
All of the configuration for this deployment is held within the
bodywork.yaml file, whose contents are reproduced below.
version: "1.0" project: name: bodywork-batch-job-project docker_image: bodyworkml/bodywork-core:latest DAG: score_data stages: score_data: executable_module_path: score_data/score.py requirements: - boto3==1.16.15 - joblib==0.17.0 - pandas==1.1.4 - scikit-learn==0.23.2 cpu_request: 0.5 memory_request_mb: 100 batch: max_completion_time_seconds: 30 retries: 2 logging: log_level: INFO
stages.score_data.executable_module_path parameter points to the executable Python module -
score.py - that defines what will happen when the
score_data (batch) stage is executed, within a pre-built Bodywork container. This module contains the code required to:
- download the new dataset from cloud storage (AWS S3);
- load the pre-trained model
- score the dataset; and,
- save the results back to cloud storage (AWS S3).
It can be summarised as,
from urllib.request import urlopen # other imports # ... DATA_URL = ('http://bodywork-batch-job-project.s3.eu-west-2.amazonaws.com' '/data/iris_classification_data.csv') # other constants # ... def main() -> None: """Main script to be executed.""" data = download_dataset(DATA_URL) model = load(TRAINED_MODEL_FILENAME) scored_data = score_data(data, model) upload_results(scored_data) # other functions definitions used in main() # ... if __name__ == '__main__': main()
We recommend that you spend five minutes familiarising yourself with the full contents of score.py. When Bodywork runs the stage, it will do so in the same way as if you were to run,
$ python score.py
And so everything defined in
main() will be executed.
stages.score_data.requirements parameter in the
bodywork.yaml file lists the 3rd party Python packages that will be Pip-installed on the pre-built Bodywork container, as required to run the
score.py module. In this example we have,
boto3==1.16.15 joblib==0.17.0 pandas==1.1.4 scikit-learn==0.23.2
boto3- for interacting with AWS;
joblib- for persisting models;
pandas- for manipulating the raw data; and,
scikit-learn- for training the model.
Finally, the remaining parameters in
stages.score_data section of the
bodywork.yaml file allow us to configure the remaining key parameters for the stage,
stages: score_data: executable_module_path: score_data/score.py requirements: - boto3==1.16.15 - joblib==0.17.0 - pandas==1.1.4 - scikit-learn==0.23.2 cpu_request: 0.5 memory_request_mb: 100 batch: max_completion_time_seconds: 30 retries: 2
From which it is clear to see that we have specified that this stage is a batch stage (as opposed to a service stage), together with an estimate of the CPU and memory resources to request from the Kubernetes cluster, how long to wait and how many times to retry, etc.
Configuring the Workflow¶
project section of the
bodywork.yaml file contains the configuration for the whole workflow, which in this case consists of a single stage as defined in the
stages.scoring_service section of
project: name: bodywork-batch-job-project docker_image: bodyworkml/bodywork-core:latest DAG: score_data
The most important element is the specification of the workflow DAG, which in this instance is simple and will instruct the Bodywork workflow-controller to run the
Testing the Workflow¶
Firstly, make sure that the bodywork package has been Pip-installed into a local Python environment that is active. Then, make sure that there is a namespace setup for use by Bodywork projects - e.g.
bodywork-batch-jobs - by running the following at the command line,
$ bodywork setup-namespace bodywork-batch-jobs
Which should result in the following output,
creating namespace=bodywork-batch-job creating service-account=bodywork-workflow-controller in namespace=bodywork-batch-jobs creating cluster-role-binding=bodywork-workflow-controller--bodywork-batch-jobs creating service-account=bodywork-jobs-and-deployments in namespace=bodywork-batch-jobs
Then, the workflow can be tested by running the workflow-controller locally (to orchestrate remote containers on k8s), using,
$ bodywork deployment create \ --namespace=bodywork-batch-jobs \ --name=test-deployment \ --git-repo-url=https://github.com/bodywork-ml/bodywork-batch-job-project \ --git-repo-branch=master \ --local-workflow-controller
Which will run the workflow defined in the
master branch of the project's remote Git repository, all within the
bodywork-batch-jobs namespace. The logs from the workflow-controller and from the container running the stage, will be streamed to the command-line to inform you on the precise state of the workflow, but you can also keep track of the current state of all Kubernetes resources created by the workflow-controller in the
bodywork-batch-jobs namespace, by using the Kubectl CLI tool - e.g.,
$ kubectl -n bodywork-batch-jobs get all
Scheduling the Workflow¶
If you're happy with the test results, you can schedule the workflow-controller to operate remotely on the cluster, on a pre-defined schedule. For example, to setup the the workflow to run every hour, use the following command,
$ bodywork cronjob create \ --namespace=bodywork-batch-jobs \ --name=score-data \ --schedule="0 * * * *" \ --git-repo-url=https://github.com/bodywork-ml/bodywork-batch-job-project \ --git-repo-branch=master \ --retries=2
Each scheduled workflow will attempt to re-run the batch-job, as defined by the state of this repository's
master branch at the time of execution.
To get the execution history for all
score-data jobs use,
$ bodywork cronjob history \ --namespace=bodywork-batch-jobs \ --name=score-data
Which should return output along the lines of,
JOB_NAME START_TIME COMPLETION_TIME ACTIVE SUCCEEDED FAILED score-data-1605214260 2020-11-12 20:51:04+00:00 2020-11-12 20:52:34+00:00 0 1 0
Then to stream the logs from any given cronjob run (e.g. to debug and/or monitor for errors), use,
$ bodywork cronjob logs \ --namespace=bodywork-batch-jobs \ --name=score-data-1605214260
To clean-up the deployment in its entirety, delete the namespace using kubectl - e.g. by running,
$ kubectl delete ns bodywork-batch-jobs