问题
I am working on a docker environment(docker-compose) with a jupyter notebook docker image and a postgres docker image for running ML models and using google cloud storage to store the model artifacts. Storing the models on the cloud storage works fine but i can't get to show them within the MLFlow UI. I have seen similar problems but non of the solutions used google cloud storage as the storage location for artifacts. The error message says the following Unable to list artifacts stored under <gs-location> for the current run. Please contact your tracking server administrator to notify them of this error, which can happen when the tracking server lacks permission to list artifacts under the current run's root artifact directory.What could possibly be causing this problem?
回答1:
I had the exactly the same issue. Keywords are docker-compose, google cloud storage, success in storing in GCS, but failure in listing artifacts in UI. In my case, it turns out that in docker-compose file, if you assign the env vars by reading from a .env file (eg. GOOGLE_APPLICATION_CREDENTIALS), the server might start before the assignment. The quick solve is to assign the env var directly with key environment: instead of using key env_file:. For sensitive data that you still need to put in .env file, you can add wait time for the server, and add depends on: in docker-compose file to make sure that the database container starts before the mlflow server if you are using database-backed store.
来源：https://stackoverflow.com/questions/63727235/mlflow-artifacts-storing-artifactsgoogle-cloud-storage-but-not-displaying-them