MLflow Artifact Storage

Currently, artifact storage is not persistent.

Artifacts are saved locally inside the container at:

/app/mlruns

Since Railway containers use ephemeral storage, these artifacts may be lost after redeploys or restarts.

For persistent artifact storage, configure an S3-compatible bucket (e.g., AWS S3, Cloudflare R2, MinIO) by setting:

BACKEND_S3=s3://mlflow-artifacts <Your_bucket_address>
AWS_ACCESS_KEY_ID=<YOUR_ACCESS_KEY_ID>
AWS_SECRET_ACCESS_KEY=<YOUR_SECRET_ACCESS_KEY>
AWS_DEFAULT_REGION=...

The MLflow server is started with:

--default-artifact-root ${BACKEND_S3:-$BACKEND_s3}

Therefore, if BACKEND_S3 is defined, MLflow automatically detects and uses it as the artifact bucket.