Principle:Mlflow Mlflow Parameter Logging
| Knowledge Sources | |
|---|---|
| Domains | ML_Ops, Experiment_Tracking |
| Last Updated | 2026-02-13 20:00 GMT |
Overview
Recording the input configuration and hyperparameters of an experiment run to enable reproducibility and systematic comparison.
Description
Parameters are the fixed input values that define how a particular experiment run is configured. In machine learning, these are most commonly hyperparameters (learning rate, batch size, number of layers, regularization strength), but the concept extends to any configuration knob that influences the outcome: data preprocessing choices, feature selection criteria, random seeds, and algorithmic variants. Parameters are distinct from metrics in that they are set before or during the run and are not expected to change over the course of training.
Recording parameters serves two critical purposes. First, it enables reproducibility: given the same code, data, and parameters, a practitioner should be able to recreate the same results. Second, it enables comparison: by examining which parameter configurations led to the best outcomes across many runs, practitioners can make informed decisions about where to focus further experimentation. Without parameter logging, the link between configuration and outcome is lost, and experiments become unrepeatable.
Parameters are typically stored as string key-value pairs. Numeric and boolean values are automatically converted to their string representation. This uniform storage format simplifies querying and display, though it means the original type information is not preserved in the tracking store. Parameter keys follow naming conventions that support hierarchical organization through dot or slash separators (e.g., optimizer.learning_rate).
Usage
Log parameters immediately after starting a run and before beginning training. Log individual parameters when they are computed dynamically, and log batches of parameters when reading from a configuration file or dictionary. Use parameter logging for any value that a future reader would need to reproduce or understand the run: model architecture choices, data splits, preprocessing flags, and training regime settings.
Theoretical Basis
Parameter logging implements an immutable configuration record pattern:
Immutability: Once a parameter is logged for a given key within a run, it cannot be overwritten with a different value. This constraint enforces the principle that a run's configuration is fixed at the time of execution. Attempting to log a different value for the same key raises an error, preventing accidental or silent configuration drift.
String Normalization: All parameter values are converted to strings before storage. This normalization eliminates type ambiguity at the storage layer and ensures that comparison and display logic does not need to handle arbitrary Python types. The trade-off is that downstream consumers must parse strings back into typed values if needed.
Batch Efficiency: When many parameters are logged together, sending them as a single batch operation reduces the number of round-trips to the tracking backend. This is important for remote tracking servers where network latency would otherwise dominate the logging overhead.
Asynchronous Option: For latency-sensitive training loops, parameter logging can be performed asynchronously. The logging call returns immediately with a future object, and the actual write occurs in a background thread. This prevents tracking overhead from slowing down the training step.