You can try the following:

Instead of using mlflow.login(), you can set the tracking URI and the token directly in your code. Here is an example:

import mlflow

# Set the tracking URI to your Databricks workspace
mlflow.set_tracking_uri("https://community.cloud.databricks.com")

# Set the Databricks token
token = "YOUR_PERSONAL_ACCESS_TOKEN"

# Configure the MLFlow client to use the token
mlflow.set_experiment("your-experiment-name")
mlflow.start_run()
mlflow.log_param("param1", 5)
mlflow.log_metric("metric1", 0.85)
mlflow.end_run()