Implementing our first DL experiment with MLflow autologging

Let's use the DL sentiment classifier we built in Chapter 1, Deep Learning Life Cycle and MLOps Challenges, and add MLflow autologging to it to explore MLflow's tracking capabilities:

1. First, we need to import the MLflow module:

   import mlflow

This will provide MLflow Application Programming Interfaces (APIs) for logging and loading models.

2. Just before we run the training code, we need to set up an active experiment using mlflow.set_experiment for the current running code:

   EXPERIMENT_NAME = "dl_model_chapter02"
   mlflow.set_experiment(EXPERIMENT_NAME)
   experiment = mlflow.get_experiment_by_name(EXPERIMENT_NAME)
   print("experiment_id:", experiment.experiment_id)

This sets an experiment named dl_model_chapter02 to be the current active experiment. If this experiment does not exist in your current tracking server, it will be created automatically.

Environment Variable

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