mlflow error on an EC2 instance with postgreSQL backend database - postgresql

Question

I am trying to run this command:
mlflow server --backend-store-uri postgresql://aagmlflow:mlflow-#aagmlflow.cbh3397nepzq.us-east-1.rds.amazonaws.com/mlflow --default-artifact-root file:/root/mlruns -h 0.0.0.0 -p8000
Error is below here:
/usr/local/lib/python3.7/dist-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use "pip install psycopg2-binary" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.
""")
2022/10/27 16:16:03 WARNING mlflow.store.db.utils: SQLAlchemy engine could not be created. The following exception is caught.
(psycopg2.OperationalError) FATAL: password authentication failed for user "aagmlflow"
FATAL: password authentication failed for user "aagmlflow"
(Background on this error at: https://sqlalche.me/e/14/e3q8)
Operation will be retried in 0.1 seconds
2022/10/27 16:16:03 WARNING mlflow.store.db.utils: SQLAlchemy engine could not be created. The following exception is caught.
(psycopg2.OperationalError) FATAL: password authentication failed for user "aagmlflow"
FATAL: password authentication failed for user "aagmlflow"
(Background on this error at: https://sqlalche.me/e/14/e3q8)
Operation will be retried in 0.3 seconds
2022/10/27 16:16:04 WARNING mlflow.store.db.utils: SQLAlchemy engine could not be created. The following exception is caught.
(psycopg2.OperationalError) FATAL: password authentication failed for user "aagmlflow"
FATAL: password authentication failed for user "aagmlflow"
(Background on this error at: https://sqlalche.me/e/14/e3q8)
what could i be doing wrong here?
Is there any way i could troubleshoot this? Please i need help from the community.

Answer 1

this actually helped me resolved this issue.
mlflow server --backend-store-uri postgresql \
--default-artifact-root file:/tmp \
--host 0.0.0.0 \
--port 8000
I also ran a docker container with this too.
# Docker multistage build to reduce image size
FROM python:3.10 AS build
RUN python -m venv /opt/venv
# Make sure we use the virtualenv:
ENV PATH="/opt/venv/bin:$PATH"
RUN pip install mlflow
FROM python:3.10-slim
COPY --from=build /opt/venv /opt/venv
COPY --from=build /usr/lib /usr/lib
# Make sure we use the virtualenv:
ENV PATH="/opt/venv/bin:$PATH"
EXPOSE 8000/tcp
ENTRYPOINT [ "mlflow", "server", "--host", "0.0.0.0"]
CMD [ "--backend-store-uri", "/tmp"]
then build & run the container
docker build -t mlflow-tracking . && docker run -dp 80:8000 mlflow-tracking