Daniel
Daniel
Pipeline Space defined via yaml: - [x] Hyperparameters are supported - [ ] Architecture Parameters are not yet supported (See #66 ) - see, example on how to use grammar...
Ideally, follows after #65. Possible usage: - `neps help`: Displays available arguments and their descriptions to guide users. - `neps init`: E.g. automatically creates a 'run_args' YAML configuration file, pre-filled...
To streamline configuration management, we could consolidate multiple YAML configurations—such as pipeline_space, custom_searcher, and others—into a single run_args file. Instead of referencing these configurations, they would be defined directly under...
Currently, neps.run() loads an optimizer with custom settings through two arguments: searcher_path and searcher. The current implementation uses searcher to specify the name of a YAML file within a folder...
- Integrating run_args, loading and checking settings from yaml file provided via run_args - Tests for functionality - Example usage in neps examples - Documentation
These proposals were implemented: - add global variable to handle path location in yaml tests - Parameter comparison in yaml tests through updated __eq__ function resulting in desired one line...
When utilizing a custom searcher, the parameter `max_cost_total` is currently being ignored. To make clear what scenario I mean, see this example: ```python def run_pipeline(**config): return {"loss": 1, "cost": 3}...
Currently, our YAML-defined pipeline space only supports the HPO parameters. It would be beneficial to expand this functionality to cover all available parameter types.
Purpose: To have a place in the documentation that provides clear, step-by-step instructions on how to use declarations to ensure that the process is simple and easy for users to...
Custom optimizers are specified through neps.run() by using the searcher argument, which inherits from BaseOptimizer. For loading the custom optimizer via 'run_args', we have chosen a design where users do...