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Experiment Tracking with TensorBoard and MLflow

The Trainer integrates with TensorBoard and MLflow for experiment tracking:

  • TensorBoard: Logs metrics and visualizations during training, allowing users to monitor the training process.

  • MLflow: Tracks experiments, logs parameters, metrics, and artifacts, and provides a user-friendly interface for comparing different runs.

To utilize these tracking tools, the Trainer can be configured with appropriate writers that handle the logging of metrics and other relevant information during training.

Example of using TensorBoard tracking:

from perceptrain import TrainConfig
from perceptrain.types import ExperimentTrackingTool

# Set up tracking with TensorBoard
config = TrainConfig(max_iter=100, tracking_tool=ExperimentTrackingTool.TENSORBOARD)

Example of using MLflow tracking:

from perceptrain.types import ExperimentTrackingTool

# Set up tracking with MLflow
config = TrainConfig(max_iter=100, tracking_tool=ExperimentTrackingTool.MLFLOW)