hypster
HyPSTER - Configuration Framework for Optimizing AI & AI Systems
Effortlessly track AI experiments with Hypster cards. Automatically log configuration parameters for easy reproducibility without code changes. Learn how to implement experiment tracking today.
Creating Reusable Configuration Templates or Components with HypsterLearn to create reusable configuration templates with Hypster. Express logic as Python functions and compose them using hp.nest() for flexible parameter overrides.
How to Use Boolean Parameters (`hp.bool()`) in Hypster ConfigurationsLearn to use hp.bool() for type-safe boolean flags in Hypster configurations. Easily declare and override boolean parameters with runtime dictionaries or key paths for flexible control.
How to Define a Complete Configuration Space with Hyperparameter Specifications in HypsterLearn to define a complete configuration space in Hypster using Python functions and the HP object to declare hyperparameters. Validate and materialize concrete values with instantiate().
How to Handle Configuration Errors Gracefully with Custom Messages in HypsterHandle configuration errors gracefully in Hypster using custom messages and the on unknown parameter. Learn to warn raise or ignore unknown parameters for better error handling.
How to Track Which Parameters Were Called During Hypster InstantiationLearn how to track parameters called during Hypster instantiation using HP.called_params and the instantiate wrapper. Simplify your configuration tracking.
How to Structure Configurations for Large Language Models (LLMs) with HypsterStructure LLM configurations with Hypster using Python functions for typed parameters validation nesting and dot notation overrides with the instantiate API.
How Hypster's `namespace_stack` Manages Nested Configurations for Hierarchical HyperparametersDiscover how Hypster's namespace_stack simplifies nested configurations. Automatically manage hierarchical hyperparameters with dot-notation paths effortlessly.
Using `options_only` with Hypster's `select` Parameters to Restrict ChoicesLearn how to use the options_only parameter with Hypster's select helpers to enforce strict whitelists and prevent invalid choices. Restrict options effectively.
How to Define and Validate Parameter Bounds (min/max) in HypsterLearn to define and validate parameter bounds min max in Hypster using min and max arguments with hp int and hp float functions Hypster handles input validation automatically
How to Use the Text Parameter Type in Hypster for String ConfigurationDiscover how to use the text parameter type in Hypster for string configuration. Learn to define string values with hp.text, ensuring robust input validation for your Hypster projects.
How to Debug Configuration Issues and Parameter Resolution Problems in HypsterDebug Hypster configuration issues and parameter resolution problems. Learn how Hypster detects unknown or unreachable parameters and resolves config values with its two stage pipeline.
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