Deploying Machine Learning Models to Production as REST API

MLflow

General Deployment

Generating Machine Learning Models

hashicorp-aws deploys an instance running Machine Learning Model NOT Managed by MLflow as REST API in Docker Container and exposes that API to public.

Please follow this dedicated guide to generate the Machine Learning model in a directory ready for use next. We will call this directory /abs/path/to/mlflow_models/ from now on

Support custom module import

Suppose our ML model depends on a custom module called my_module:

from my_module import my_function

class MyModel(mlflow.pyfunc.PythonModel):
    ...

How do we make my_module available at runtime? It's very easy. Simply put the custom module under /abs/path/to/mlflow_models/ and import that module as is. hashicorp-aws will bind /abs/path/to/mlflow_models/ to PYTHONPATH at runtime so that my_module will be available as long as both MyModel and my_module are under /abs/path/to/mlflow_models/ directory.

After model is generated, add a PORT file containing a port number only. For eample,

PORT

8080

After model is generated...

• If the container runs multiple models at different ports, please make sure to add a PORT file containing a port number only to each sub-dir of /abs/path/to/mlflow_models/ with one sub-dir holding one model
• If we need custom module imports in a model, add module.py and a __init__.py file to the sub-dir of that model under /abs/path/to/mlflow_models/

AWS Credentials

The following environment variables need to be defined:

• AWS_ACCESS_KEY_ID
• AWS_SECRET_ACCESS_KEY

Installing HashiCorp Packer & Terraform

We will go through deployment using Packer & Terraform command line tools which can be installed by following the instructions below:

• Installing Packer
• Installing Terraform

Getting HashiCorp Deployment Tool

git clone https://github.com/QubitPi/hashicorp-aws.git

Defining Packer Variables

Create a HashiCorp Packer variable values file named aws-mlflow-docker.pkrvars.hcl under hashicorp-aws/hashicorp/machine-learning/images/mlflow-docker directory with the following contents

ami_region     = "my-aws-region"
ami_name       = "my-mlflow-models"
instance_type  = "<one of t2.micro/t2.small/t2.medium/t2.large/t2.xlarge/t2.2xlarge>"
ml_models_path = "/abs/path/to/mlflow_models/"

• ami_region is the image region where ML models AMI will be published to. The published image will be private

• ami_name is the name of the resulting AMI that will appear when managing AMIs in the AWS console or via APIs. This can be the same across builds, because hashicorp-aws will deregister the old AMI with the same name and replace it with the current built one

• instance_type is the recommended AWS EC2 instance type running this image

• ml_models_path is the directory we made ready previously

  caution

  The path must not end with a /. For example, it's OK with ml_models_path = "/home/ubuntu/mymodel", but ml_models_path = "/home/ubuntu/mymodel/" is not

Defining Terraform Variables

Create a HashiCorp Terraform variable values file named aws-mlflow-docker.tfvars under hashicorp-aws/hashicorp/machine-learning/instances/mlflow-docker directory with the following contents:

hashicorp-aws/hashicorp/machine-learning/instances/mlflow-docker/aws-mlflow-docker.tfvars

aws_ec2_region    = "my-aws-region"
ami_name          = "my-mlflow-models"
instance_type     = "<one of t2.micro/t2.small/t2.medium/t2.large/t2.xlarge/t2.2xlarge>"
ec2_instance_name = "My MLflow models"
security_groups   = ["myKeyPairName"]

• aws_ec2_region is the EC2 runtime region
• ami_name is the name of the published AMI; it must be the same as the ami_name in Packer variable file
• instance_type is the AWS EC2 instance type used for deployed infrastructure
• ec2_instance_name is the deployed EC2 name as appeared in the instance list of AWS console; it can be arbitrary
• security_groups is the list of AWS Security Group names to associate with (yes, not ID, but name...)

Building AMI Image

cd hashicorp-aws/hashicorp/machine-learning/images/mlflow-docker
packer init .
packer validate -var "skip_create_ami=true" .
packer build -var "skip_create_ami=false" .

note

The packer simply builds a non-model specific AMI

Deploying to EC2

caution

Depending on the AMI and EC2 configs, please be aware AWS credit charges shall incur after the following commands execute

cd ../instances/mlflow-docker
terraform init
terraform validate
terraform apply -auto-approve

Troubleshooting

Model REST Container Fails to Start

WARNING mlflow.utils.virtualenv: Encountered unexpected error while creating /root/.mlflow/envs/mlflow-8aab4f34fgrg33aa5987gyub0037333d3e4a818bc5e
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/usr/local/lib/python3.8/dist-packages/mlflow/models/container/__init__.py", line 92, in _install_pyfunc_deps
    activate_cmd = _install_model_dependencies_to_env(model_path, env_manager) if model_path else []
  File "/usr/local/lib/python3.8/dist-packages/mlflow/models/container/__init__.py", line 167, in _install_model_dependencies_to_env
    env_activate_cmd = _get_or_create_virtualenv(model_path)
  File "/usr/local/lib/python3.8/dist-packages/mlflow/utils/virtualenv.py", line 379, in _get_or_create_virtualenv
    activate_cmd = _create_virtualenv(
  File "/usr/local/lib/python3.8/dist-packages/mlflow/utils/virtualenv.py", line 289, in _create_virtualenv
    _exec_cmd(cmd, capture_output=capture_output, cwd=tmpdir, extra_env=extra_env)
  File "/usr/local/lib/python3.8/dist-packages/mlflow/utils/process.py", line 120, in _exec_cmd
    raise ShellCommandException.from_completed_process(comp_process)
mlflow.utils.process.ShellCommandException: Non-zero exit code: -9
Command: ['bash', '-c', 'source /root/.mlflow/envs/mlflow-8aab4f34fgrg33aa5987gyub0037333d3e4a818bc5e/bin/activate && python -m pip install --quiet -r requirements.afergr030a0aedfe32e4a0a6f2e57.txt']

The container requires at least 4 GB of memory. Please make sure the EC2 instance has enough memory

Deployment via Screwdriver CD

hashicorp-aws supports deployment using Screwdriver CD. Please check it out.

Deployment via HACP

tip

Please try our HACP platform to deploy a Machine Learning API instance. It gives us one-click experience that helps us stand up the API in a minute.