New Container: MFflow

What is the new container you'd like to have?

I would like to provide a new testcontainer for mlflow, a tool for managing your machine learning model life cycle.

Why not just use a generic container for this?

I added handy utilites to get the url to track to and to get the client to interact with the container directly.

The implementation would look like:

import logging

import requests
from mlflow import MlflowClient
from testcontainers.core.container import DockerContainer
from testcontainers.core.waiting_utils import wait_container_is_ready

logger = logging.getLogger(__name__)


class MFlowContainer(DockerContainer):
    """Test container for MLflow.

    Args:
        image: the image to use. Change if you need different version.
        port: the internal port to use.  The exposed port is assigned automatically.
        cmd: the command to run. Defaults to "mlflow server". If you want to use the ui for debugging and testing use "mlflow ui".
    """

    def __init__(
        self, image: str = "ghcr.io/mlflow/mlflow:v2.14.1", port: int = 5000, cmd: str = "mlflow server"
    ) -> None:
        super().__init__(image=image)
        self.port = port
        self.with_exposed_ports(self.port)
        self.cmd = cmd

    def _configure(self) -> None:
        self.with_env("MLFLOW_PORT", str(self.port))
        self.with_env("MLFLOW_HOST", "0.0.0.0")
        self.with_command(self.cmd)

    def get_url(self) -> str:
        """Returns the url of the container.

        Returns:
            The url. Use to track to.
        """
        return f"http://{self.get_container_host_ip()}:{self.get_exposed_port(self.port)}"

    @wait_container_is_ready(requests.exceptions.ConnectionError, requests.exceptions.ReadTimeout)
    def _readiness_probe(self) -> None:
        # https://mlflow.org/docs/latest/deployment/deploy-model-locally.html?highlight=health
        response = requests.get(f"{self.get_url()}/health", timeout=1)
        response.raise_for_status()

    def get_client(self) -> MlflowClient:
        """Returns the MlflowClient of the container.

        Can be used for testing.

        """
        return MlflowClient(self.get_url())

    def start(self) -> "MFlowContainer":
        self._configure()
        super().start()
        self._readiness_probe()
        return self

Es werden keine Repository-Dateien oder Tests genannt. Beginne damit, vorhandene Testcontainers Python-Containerintegrationen und deren Tests zu finden, und orientiere dich dann an den vorgeschlagenen MLflow-Container-Einstiegspunkten; fertig ist die Arbeit, wenn der MLflow-Dienst bereit ist und seine URL sowie sein Client in Tests verwendet werden können.