mlflow-ecs-sagemaker

MLflow on ECS Fargate with Amazon SageMaker

This is a MLflow project on Amazon ECS for CDK development with Python.

The cdk.json file tells the CDK Toolkit how to execute your app.

This project is set up like a standard Python project. The initialization process also creates a virtualenv within this project, stored under the .venv directory. To create the virtualenv it assumes that there is a python3 (or python for Windows) executable in your path with access to the venv package. If for any reason the automatic creation of the virtualenv fails, you can create the virtualenv manually.

To manually create a virtualenv on MacOS and Linux:

$ python3 -m venv .venv

After the init process completes and the virtualenv is created, you can use the following step to activate your virtualenv.

$ source .venv/bin/activate

If you are a Windows platform, you would activate the virtualenv like this:

% .venv\Scripts\activate.bat

Once the virtualenv is activated, you can install the required dependencies.

(.venv) $ pip install -r requirements.txt

Before synthesizing the CloudFormation, you should set approperly the cdk context configuration file, cdk.context.json.

For example:

{
  "ecs": {
    "cluster_name": "mlflow",
    "service_name": "mlflow"
  },
  "vpc_name": "default"
}

At this point you can now synthesize the CloudFormation template for this code.

(.venv) $ export CDK_DEFAULT_ACCOUNT=$(aws sts get-caller-identity --query Account --output text)
(.venv) $ export CDK_DEFAULT_REGION=$(aws configure get region)
(.venv) $ cdk synth --all

Use cdk deploy command to create the stack shown above,

(.venv) $ export CDK_DEFAULT_ACCOUNT=$(aws sts get-caller-identity --query Account --output text)
(.venv) $ export CDK_DEFAULT_REGION=$(aws configure get region)
(.venv) $ cdk deploy --require-approval never --all

To add additional dependencies, for example other CDK libraries, just add them to your setup.py file and rerun the pip install -r requirements.txt command.

Running Test

1. Open the jupyter notebook, training_job_on_premise.ipynb in example/sklearn_diabetes_regression directory on your jupyter lab.
2. Replace MLFLOW_TRACKING_URI with the mlflow server deployed on Amazon ECS cluster.
3. Run all cells in training_job_on_premise.ipynb.
4. Open the mlflow web in your browser and you can see the screen like this:
5. Launch Amazon SageMaker Studio
6. Upload deploy_mlflow_model_to_sagemaker.ipynb file in example/sklearn_diabetes_regression directory to the SageMaker Studio.
7. Set MLFLOW_TRACKING_URI
8. Run all cells in deploy_mlflow_model_to_sagemaker.ipynb.

Clean Up

Delete the CloudFormation stack by running the below command.

(.venv) $ cdk destroy --force --all

Useful commands

• cdk ls list all stacks in the app
• cdk synth emits the synthesized CloudFormation template
• cdk deploy deploy this stack to your default AWS account/region
• cdk diff compare deployed stack with current state
• cdk docs open CDK documentation

Enjoy!

References

• (AWS Blog) Managing your machine learning lifecycle with MLflow and Amazon SageMaker (2021-01-28)
  • (GitHub Repo) amazon-sagemaker-mlflow-fargate
• MLflow Documentation
• Dockerfile reference
• Use the Docker command line
• Connect using the EC2 Instance Connect CLI

  $ sudo pip install ec2instanceconnectcli
  $ mssh ubuntu@i-001234a4bf70dec41EXAMPLE # ec2-instance-id with ubuntu user