MLFLOW Artifacts stored in remote storage not showing on UI

Question

Set up:

• tracking server openstack instance
• mlflow tracking database on second openstack instance
• traring on on-prem HPC node

Tracking server start

python3 -m mlflow server --dev --app-name oidc-auth --host 0.0.0.0
--port 8080 --backend-store-uri postgresql+psycopg2://$DB_USERNAME:$DB_PASSWORD@$DB_IP_ADDRESS:5432/$DB_NAME

Experiment (i need artifact_location because each research group will want to use their on remote directory

# Create a new experiment
    experiment = mlflow.get_experiment_by_name(experiment_name)
    if experiment is None:
        exp = mlflow.create_experiment(experiment_name, artifact_location='/lustre/scratch127/mlops_artifacts')
        set_experiment = mlflow.set_experiment(experiment_id=exp)
    else:
        set_experiment = mlflow.set_experiment(experiment_name)

From logs

First Experiment Elastic Net Name: exp_EL_research Experiment_id: 8 Artifact Location: /lustre/scratch127/mlops_artifacts Active run id is fff785c73a814fc89102ed7a486de96d Active run name is bedecked-grub-472 Artifact URI is /lustre/scratch127/mlops_artifacts/fff785c73a814fc89102ed7a486de96d/artifacts

first question why artifact_uri is not /lustre/scratch127/mlops_artifacts/8/fff785c73a814fc89102ed7a486de96d/artifacts ??

# Log the model
mlflow.sklearn.log_model(sk_model=lr, artifact_path=artifact_path, registered_model_name=model_name
fff785c73a814fc89102ed7a486de96d
└── artifacts
        └── 8
               ├── conda.yaml
               ├── metadata
               │   ├── conda.yaml
               │   ├── MLmodel
               │   ├── python_env.yaml
               │   └── requirements.txt
               ├── MLmodel
               ├── model.pkl
               ├── python_env.yaml
               └── requirements.txt