HyperArgs 0.0.3

HyperArgs is a typed configuration library for Python. Define settings as type-annotated classes; HyperArgs parses command-line flags, configuration files, and environment variables into a single, type-checked object, with an optional browser-based editor.

HyperArgs

HyperArgs is a typed configuration library for Python.
It lets you define hyperparameters and settings as strongly typed Python classes, parse them from multiple sources (command line, JSON, TOML, YAML), enforce dependency constraints, and monitor field changes with decorators.

HyperArgs provides:

• Typed arguments — IntArg, FloatArg, StrArg, BoolArg, OptionArg.
• Config classes — subclass Conf to declare structured, type-safe settings.
• Multi-source parsing — load from CLI, JSON, TOML, YAML, or dict.
• Dependency management — declare relationships between fields.
• Change monitoring — automatically trigger updates when fields change.
• Environment variable binding — args can bind to env vars via env_bind.

Installation

pip install hyperargs

Quick Start

1. Define a configuration

# file: train.py

from hyperargs import Conf, add_dependency, monitor_on
from hyperargs.args import IntArg, FloatArg, StrArg, BoolArg, OptionArg

class TrainConf(Conf):
    learning_rate = FloatArg(0.001, min_value=1e-6, max_value=1.0)
    batch_size = IntArg(32, min_value=1)
    num_epochs = IntArg(10, min_value=1)
    optimizer = OptionArg("adam", options=["adam", "sgd", "rmsprop"])
    use_gpu = BoolArg(True)

    @monitor_on("learning_rate")
    def adjust_schedule(self):
        print(f"Learning rate changed to {self.learning_rate.value()}")

conf = TrainConf.parse_command_line(strict=True)
print(conf)

...

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2. Parse configurations

• From command line:

python train.py --config_path config.yaml

Supported formats: .json, .toml, .yaml, .yml.

• From Python dict:

conf = TrainConf.from_dict({"learning_rate": 0.01, "batch_size": 64})
print(conf.to_yaml())

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3. Save configurations

conf.save_to_file("config.json")

Supported formats: .json, .toml, .yaml, .yml.

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4. Add dependencies

For example, you may want to ensure that the optimizer type is set before its corresponding optimizer config.

class OptimizerConf(Conf):
    lr: FloatArg(1e-3)

class AdamConf(OptimizerConf):
    beta1 = FloatArg(0.9)
    beta2 = FloatArg(0.999)

class SGDConf(OptimizerConf):
    momentum = FloatArg(0.9)

# Ensure optimizer type is decided first, then configs are parsed
@Conf.add_dependency("optim_type", "configs")
class OptimConf(Conf):
    optim_type = OptionArg("adam", options=["adam", "sgd"])
    configs = OptimizerConf()

    # Switch the config to the correct type when optim_type is being setted
    @Conf.monitor_on('optim_type')
    def init_configs(self) -> None:
        if self.optim_type.value() == 'adam':
            self.configs = AdamConf()
        elif self.optim_type.value() == 'sgd':
            self.configs = SGDConf()

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5. Environment variable binding

Arguments can bind directly to environment variables. For example, setting the MASTER_ADDR for distributed training:

import os
os.environ["MASTER_ADDR"] = "192.168.1.42"

class DistConf(Conf):
    master_addr = StrArg("127.0.0.1", env_bind="MASTER_ADDR")

conf = DistConf()
print(conf.master_addr.value())  # "192.168.1.42"

API Overview

You can import the main API directly:

from hyperargs import Conf, add_dependency, monitor_on
from hyperargs.args import IntArg, FloatArg, StrArg, BoolArg, OptionArg

• Arg subclasses
  • IntArg(default, min_value=None, max_value=None, allow_none=False, env_bind=None)
  • FloatArg(default, min_value=None, max_value=None, allow_none=False, env_bind=None)
  • StrArg(default, allow_none=False, env_bind=None)
  • BoolArg(default, allow_none=False, env_bind=None)
  • OptionArg(default, options, allow_none=False, env_bind=None)
• Conf — base class for config schemas.
• monitor_on(fields) — decorator to watch fields and trigger methods.
• add_dependency(parent, child) — enforce field dependency order.

Example Workflow

# config.yaml
learning_rate: 0.01
batch_size: 64
num_epochs: 20
optimizer: sgd
use_gpu: false

conf = TrainConf.parse_command_line()
# run: python train.py --config_path config.yaml
print(conf.optimizer.value())  # "sgd"

Roadmap

Stage 1 — Config files & strings ✅

• Parse from JSON, TOML, YAML strings
• Parse from config files
• Convert and save configs

Stage 2 — Command line flags 🚧

• Parse configs directly from CLI flags
  (e.g. --optimizer.params.learning_rate 0.01 --batch_size 64)

Stage 3 — GUI settings 🚧

• Popup browser page to set configs interactively
• Export configs after editing

License

Apache License 2.0