MLflow is an open-source platform designed to manage the machine learning lifecycle, including experimentation, reproducibility, and deployment. It provides a suite of tools to help data scientists and machine learning engineers track experiments, package code into reproducible runs, and share and deploy models. For more information, visit the official MLflow website.
When using MLflow, you might encounter the following error message: mlflow.exceptions.MlflowException: Invalid metric. This error typically occurs when logging metrics during an MLflow run.
While executing your MLflow script, the process fails, and the console outputs the error message indicating an invalid metric. This interrupts the logging of metrics and can prevent the successful tracking of your experiment's performance.
The error mlflow.exceptions.MlflowException: Invalid metric suggests that MLflow is unable to recognize the metric you are trying to log. This can happen if the metric name is incorrect or if the value being logged does not conform to expected data types or formats.
To resolve the Invalid metric error, follow these steps:
Ensure that the metric name you are using is correctly spelled and does not contain any unsupported characters. Metric names should be strings and can include letters, numbers, underscores, and hyphens.
Verify that the metric value you are logging is a valid number, such as a float or integer. MLflow expects metric values to be numeric for proper logging and analysis.
Review your code to ensure that the mlflow.log_metric function is called with the correct parameters. Here is an example of how to log a metric correctly:
import mlflow
with mlflow.start_run():
mlflow.log_metric("accuracy", 0.95)
After making the necessary corrections, rerun your script to ensure that the error is resolved. If the issue persists, double-check the metric name and value for any overlooked mistakes.
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