Hi guys, I am having trouble to run my kedro from a docker build. I'm using MLflow and the

kedro_mlflow.io.artifacts.MlflowArtifactDataset

I followed the instructions for building the container from kedro-docker repo but when running, those artifacts want to access my local windows path instead of the containers path. Do you guys know what additional settings I have to make? All my settings in are pretty much vanilla. The

mlflow_tracking_uri

is set to null

"{dataset}.team_lexicon":
  type: kedro_mlflow.io.artifacts.MlflowArtifactDataset  
  dataset:
    type: pandas.ParquetDataset  
    filepath: data/03_primary/{dataset}/team_lexicon.pq 
    metadata:
      kedro-viz:
        layer: primary  
        preview_args:
            nrows: 5

Traceback (most recent call last):
  
kedro.io.core.DatasetError: Failed while saving data to dataset MlflowParquetDataset(filepath=/home/kedro_docker/data/03_primary/D1-24-25/team_lexicon.pq, load_args={}, protocol=file, save_args={}).
[Errno 13] Permission denied: '/C:'

Someone will reply to you shortly. In the meantime, this might help:

I should use the AI more 🫠. Solved by explicitly setting the

mlflow_tracking_uri

was that our AI or the

Ask AI

button above?

It was with the ask ai button. Maybe I was lucky because it referenced the base integration of mlflow in the docs. > Based on the error description, it appears you're encountering an issue with path handling when using

MlflowArtifactDataset

in a Docker container. When using MLflow with Kedro in Docker, you need to ensure proper configuration: > 1. Create a

mlflow.yml

configuration file in your

conf/local

directory > 2. Configure the MLflow tracking URI in the configuration file: > 3. server: > 4. mlflow_tracking_uri: mlflow_runs > For Docker deployment, make sure your Docker container has the correct volume mounts and working directory set up. The working directory should be specified as the directory containing your Kedro project on the container. Now unfortunately my runs aren't logged when running the pipeline. Do you have any idea on why that might be? **when I'm checking the running container mlflow still created

mlruns

defautl folder and not the uri i provided by setting

server:
  mlflow_tracking_uri: mlruns_docker

even trying to use the default doesnt work.

server:
  mlflow_tracking_uri: mlruns