paramflow 0.4

A lightweight library for hyperparameter and configuration management

paramflow

ParamFlow is a lightweight and versatile library for managing hyperparameters and configurations, tailored for machine learning projects and applications requiring layered parameter handling. It merges parameters from multiple sources, generates command-line argument parsers, and simplifies parameter overrides, providing a seamless and efficient experience.

Features

• Layered configuration: Merge parameters from files, environment variables, and command-line arguments.
• Immutable dictionary: Provides a read-only dictionary with attribute-style access.
• Profile support: Manage multiple sets of parameters with profile-based layering.
• Layered meta-parameters: paramflow configures itself using a layered approach.
• Automatic type conversion: Converts types during merging based on target parameter types.
• Command-line argument parsing: Automatically generates an argparse parser from parameter definitions.
• Nested Configuration: Allows for nested configuration and merging.

Installation

pip install paramflow

Install with .env support:

pip install "paramflow[dotenv]"

Basic Usage

Example Configuration File (params.toml)

[default]
learning_rate = 0.001
batch_size = 64

Loading Parameters in Python (app.py)

import paramflow as pf

params = pf.load('params.toml')
print(params.learning_rate)  # 0.001

Generating Command-line Help

Running the script with --help displays both meta-parameters and parameters:

python app.py --help

Meta-Parameter Layering

Meta-parameters control how paramflow.load reads its own configuration. Layering order:

1. paramflow.load arguments
2. Environment variables (default prefix: P_)
3. Command-line arguments (argparse)

Activating Profiles

Via command-line:

python print_params.py --profile dqn-adam

Via environment variable:

P_PROFILE=dqn-adam python print_params.py

Parameter Layering

Parameters are merged from multiple sources in the following order:

1. Configuration files (.toml, .yaml, .ini, .json, .env)
2. Environment variables (default prefix: P_)
3. Command-line arguments (argparse)

Customizing Layering Order

You can specify the order explicitly (env and args are reserved names):

params = pf.load('params.toml', 'env', '.env', 'args')

Overriding Parameters

Override parameters via command-line arguments:

python print_params.py --profile dqn-adam --learning_rate 0.0002

Managing ML Hyperparameter Profiles

Example Configuration (params.toml)

[default]
learning_rate = 0.00025
batch_size = 32
optimizer_class = 'torch.optim.RMSprop'
optimizer_kwargs = { momentum = 0.95 }
random_seed = 13

[adam]
learning_rate = 1e-4
optimizer_class = 'torch.optim.Adam'
optimizer_kwargs = {}

Activating a Profile

python app.py --profile adam

This overrides:

• learning_rate → 1e-4
• optimizer_class → torch.optim.Adam
• optimizer_kwargs → {}

Managing Development Stages

Profiles can be used to manage configurations for different environments.

Example Configuration (params.toml)

[default]
debug = true
database_url = "mysql://localhost:3306/myapp"

[dev]
database_url = "mysql://dev:3306/myapp"

[prod]
debug = false
database_url = "mysql://prod:3306/myapp"

Activating a Profile

export P_PROFILE=dev
python app.py