Question
Answer and Explanation
To add tags to an experiment in MLflow, you can use the MLflow tracking API. Here's how you can do it, along with explanations and examples:
1. Using the `mlflow.set_experiment` and `mlflow.set_tag` functions:
- First, ensure you have an active experiment. If one doesn't exist, you can create a new one using mlflow.set_experiment("Your Experiment Name"). If the experiment already exists, this call will simply activate it.
- After activating the experiment, you can use mlflow.set_tag to add specific key-value pair tags.
2. Example Code:
import mlflow
# Set or create an experiment
mlflow.set_experiment("My_Awesome_Experiment")
# Start an MLflow run (optional, but common when doing ML tasks)
with mlflow.start_run() as run:
# Add tags to the current experiment
mlflow.set_tag("Dataset", "Titanic")
mlflow.set_tag("Model_Type", "Logistic Regression")
mlflow.set_tag("Version", "v1.0")
mlflow.set_tag("Author", "John Doe")
mlflow.log_metric("accuracy", 0.85) # Add a log metric for demonstration
print(f"MLflow Run ID: {run.info.run_id}") # Print run id
3. Explanation of the Code:
- import mlflow: Imports the MLflow library.
- mlflow.set_experiment("My_Awesome_Experiment"): Sets the active experiment. If it doesn't exist, it will be created. If the experiment exists, it becomes the active experiment for the current script.
- with mlflow.start_run() as run: Starts a new run within the context of the experiment. This is optional, but it is good practice to start new runs when tracking specific experiments.
- mlflow.set_tag("Dataset", "Titanic"), mlflow.set_tag("Model_Type", "Logistic Regression"), mlflow.set_tag("Version", "v1.0"), mlflow.set_tag("Author", "John Doe"): These lines add tags to the experiment with keys and corresponding values.
- mlflow.log_metric("accuracy", 0.85): This logs the metric for demonstaration purposes.
- print(f"MLflow Run ID: {run.info.run_id}"): This print statement returns a unique run id.
4. Important Notes:
- Tags can be any key-value pair. This is helpful for adding metadata about your experiments like dataset details, model types, version numbers, author names, and other relevant information for your ML project.
- Tags are applied to the active run inside of the selected experiment and are associated with the run.
- You can view your tags in the MLflow UI after the run is completed.
- If you're not in a run context, tags are usually added at the run level.
By following these steps and using the provided code, you can efficiently add tags to your MLflow experiments, allowing for better organization and tracking of your machine learning projects.