not able to dockerize mlflow

Question

while dockerizing mlflow , only .trash is getting created beacuse of that in mlflow ui , getting error as "no experiments exists"

dockerfile

FROM python:3.7.0

RUN pip install mlflow==1.0.0

WORKDIR /data

EXPOSE 5000

CMD mlflow server \
    --backend-store-uri /data/ \
    --default-artifact-root /data/ \
    --host 0.0.0.0

docker compose :

  mlflow:
    # builds track_ml Dockerfile
    build:
      context: ./mlflow_dockerfile
    expose: 
      - "5000"
    ports:
      - "5000:5000"
    volumes: 
      - ./data:/data

Answer 1

You can use this Dockerfile, Taken from mlflow-workshop which is more generic and support different ENV to debug and working with different version.

By default it will store the artifacts and files inside /opt/mlflow. It's possible to define the following variables:

MLFLOW_HOME (/opt/mlflow)
MLFLOW_VERSION (0.7.0)
SERVER_PORT (5000)
SERVER_HOST (0.0.0.0)
FILE_STORE (${MLFLOW_HOME}/fileStore)
ARTIFACT_STORE (${MLFLOW_HOME}/artifactStore)

Dockerfile

FROM python:3.7.0
LABEL maintainer="Albert Franzi"

ENV MLFLOW_HOME /opt/mlflow
ENV MLFLOW_VERSION 0.7.0
ENV SERVER_PORT 5000
ENV SERVER_HOST 0.0.0.0
ENV FILE_STORE ${MLFLOW_HOME}/fileStore
ENV ARTIFACT_STORE ${MLFLOW_HOME}/artifactStore

RUN pip install mlflow==${MLFLOW_VERSION} && \
    mkdir -p ${MLFLOW_HOME}/scripts && \
    mkdir -p ${FILE_STORE} && \
    mkdir -p ${ARTIFACT_STORE}

COPY scripts/run.sh ${MLFLOW_HOME}/scripts/run.sh
RUN chmod +x ${MLFLOW_HOME}/scripts/run.sh

EXPOSE ${SERVER_PORT}/tcp

VOLUME ["${MLFLOW_HOME}/scripts/", "${FILE_STORE}", "${ARTIFACT_STORE}"]

WORKDIR ${MLFLOW_HOME}

ENTRYPOINT ["./scripts/run.sh"]

scripts/run.sh

#!/bin/sh

mlflow server \
    --file-store $FILE_STORE \
    --default-artifact-root $ARTIFACT_STORE \
    --host $SERVER_HOST \
    --port $SERVER_PORT

Launch MLFlow Tracking Docker

docker build -t my_mflow_image .
docker run -d -p 5000:5000 --name mlflow-tracking my_mflow_image

Run trainings

Since we have our MLflow Tracking docker exposed at 5000, we can log executions by setting the env variable MLFLOW_TRACKING_URI.

MLFLOW_TRACKING_URI=http://localhost:5000 python example.py

Also, better to remove - ./data:/data on first run, debug with out mount, and the suggest dockerfile you might need to mount different path that is mentioned in ENV based on your need.