not able to dockerize mlflow - docker

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.

Answer 2

Here is a link to Github where I put MLflow in a docker that uses azurite in the background to also pull the models later from it.
As a short notification, you need to give your script how ever you execute it the address where it should save the artifacts. You can do this with .env files or set these things manually.
set MLFLOW_TRACKING_URI=http://localhost:5000
Important is to also give these information not only your docker but also the script for the model training ;)
Here you can find a complete tutorial how to use MLflow and SKlearn together in different theoretical szenarios since it is also a bit tricky later on.
I hope you get enough inspiration how to use it.