This tech talk includes a demonstration of a basic hyperparameter search using open-source tools, Ray Tune and MLflow tracking.
Hyperparameter optimization is the process of fine-tuning the hyperparameters of a machine learning model in order to improve its performance. Hyperparameters are the values that control the behavior of a machine learning model, such as the learning rate or the regularization strength. In order to optimize these hyperparameters, we need to find the best combination of hyperparameters that results in the highest performance of the model.
One way to optimize hyperparameters is to use the Ray Tune library, which is a library for hyperparameter tuning and black-box optimization. Ray Tune provides a simple API for defining and running hyperparameter searches, and it can be used with any machine learning model or training script.
To use Ray Tune for hyperparameter optimization, you need to define the hyperparameters that you want to tune and specify the search space for each hyperparameter. You can then define a tuning function that takes in the hyperparameters as arguments and trains the model using those hyperparameters. Ray Tune will then search through the hyperparameter space and run the tuning function with different combinations of hyperparameters in order to find the combination that results in the best model performance.
MLFlow is a tool for tracking machine learning experiments, including hyperparameter optimization experiments. By using MLFlow to track your experiments, you can easily log and visualize the results of your hyperparameter searches, compare different runs, and reproduce past results. To use MLFlow with Ray Tune, you can use the MLFlow API to log the results of each run and track the performance of the model over time. This can help you understand how different hyperparameters affect the performance of your model and make informed decisions about which hyperparameters to use.
This tech talk includes a demonstration of a basic hyperparameter search using two popular open-source tools, Ray Tune and MLflow tracking.