ato 2.0.2

A minimal, composable config layer for Python and ML pipelines. Built to stay, not to impress.

Ato: A Tiny Orchestrator

A minimal, composable config layer for Python and ML pipelines

Ato is a minimal, composable config system for Python and ML pipelines.
It lets you chain, merge, and freeze modular configs,
so you can move seamlessly from dynamic experiments to static production builds.

Unlike heavy frameworks, Ato keeps everything transparent and Pythonic —
you can use it alongside tools like Hydra, WandB, or MLflow without friction.
It’s built for people who prefer clarity over magic.

After all, Ato was never built to impress — it was built to stay.

Developer’s Note

I didn’t know there was a great tool called Hydra.
So I built something a bit simpler, a bit more opinionated,
and maybe a bit more compatible — something that could also work nicely
with amazing tools like Hydra, WandB, or MLflow.

Even though I didn’t know these tools at the time,
I deliberately designed for compatibility —
and later, after learning about Hydra and others,
I added explicit interop layers.
Because I know how tempting — and exhausting —
it can be to move from a familiar environment
to a new, more attractive one.

I’ve been the only user so far —
not because I wanted to hide it,
but because I never had anyone around
who could really tell me if it was good enough.
Maybe this is the right time to find out.

So — there’s no need to compete.
Just try it once.
This tool won’t make you tired.
It might even feel a little kind.

---

Ato is designed to work with your existing tools — not replace them. It provides configuration management, experiment tracking, and hyperparameter optimization as a philosophical layer that plays nicely with Hydra, MLflow, W&B, and whatever else you use.

Why Ato?

Ato isn't trying to compete with Hydra or replace your experiment tracking platform. It's for the projects that live before things get complicated — or for teams that want clarity over features.

Philosophy over framework: Ato gives you enough structure to stay organized, without imposing a rigid system. Use it standalone, or layer it on top of Hydra, MLflow, or W&B. It's a tool, not a commitment.

Core Differentiators

• True Namespace Isolation: MultiScope provides independent config contexts (unique to Ato!)
• Configuration Transparency: Visualize exact config merge order - debug configs with manual command
• Built-in Experiment Tracking: SQLite-based tracking with no external services required
• Structural Hashing: Track experiment structure changes automatically

Developer Experience

• Zero Boilerplate: Auto-nested configs, lazy evaluation, attribute access
• CLI-first Design: Configure experiments from command line without touching code
• Framework Agnostic: Works with PyTorch, TensorFlow, JAX, or pure Python

Quick Start

pip install ato

30-Second Example

from ato.scope import Scope

scope = Scope()

@scope.observe(default=True)
def config(cfg):
    cfg.lr = 0.001
    cfg.batch_size = 32
    cfg.model = 'resnet50'

@scope
def train(cfg):
    print(f"Training {cfg.model} with lr={cfg.lr}")
    # Your training code here

if __name__ == '__main__':
    train()  # python train.py
    # Override from CLI: python train.py lr=0.01 model=%resnet101%

---

Table of Contents

• ADict: Enhanced Dictionary
• Scope: Configuration Management
  • MultiScope: Namespace Isolation ⭐ Unique to Ato
  • Config Documentation & Debugging ⭐ Unique to Ato
• SQL Tracker: Experiment Tracking
• Hyperparameter Optimization
• Best Practices
• Future Work
• Working with Existing Tools

---

ADict: Enhanced Dictionary

ADict is an enhanced dictionary designed for managing experiment configurations. It combines the simplicity of Python dictionaries with powerful features for ML workflows.

Core Features

These are the fundamental capabilities that make ADict powerful for experiment management:

Feature	Description	Why It Matters
Structural Hashing	Hash based on keys + types, not values	Track when experiment structure changes
Nested Access	Dot notation for nested configs	config.model.lr instead of config['model']['lr']
Format Agnostic	Load/save JSON, YAML, TOML, XYZ	Work with any config format
Safe Updates	update_if_absent() method	Prevent accidental overwrites

Developer Convenience Features

These utilities maximize developer productivity and reduce boilerplate:

Feature	Description	Benefit
Auto-nested (ADict.auto())	Infinite depth lazy creation	config.a.b.c = 1 just works - no KeyError
Attribute-style Assignment	config.lr = 0.1	Cleaner, more readable code
Conditional Updates	Only update missing keys	Merge configs safely

Quick Examples

from ato.adict import ADict

# Structural hashing - track config structure changes
config1 = ADict(lr=0.1, epochs=100, model='resnet50')
config2 = ADict(lr=0.01, epochs=200, model='resnet101')
print(config1.get_structural_hash() == config2.get_structural_hash())  # True

config3 = ADict(lr=0.1, epochs='100', model='resnet50')  # epochs is str!
print(config1.get_structural_hash() == config3.get_structural_hash())  # False

# Load/save any format
config = ADict.from_file('config.json')
config.dump('config.yaml')

# Safe updates
config.update_if_absent(lr=0.01, scheduler='cosine')  # Only adds scheduler

Convenience Features in Detail

Auto-nested: Zero Boilerplate Config Building

The most loved feature - no more manual nesting:

# ❌ Traditional way
config = ADict()
config.model = ADict()
config.model.backbone = ADict()
config.model.backbone.layers = [64, 128, 256]

# ✅ With ADict.auto()
config = ADict.auto()
config.model.backbone.layers = [64, 128, 256]  # Just works!
config.data.augmentation.brightness = 0.2

Perfect for Scope integration:

from ato.scope import Scope

scope = Scope()

@scope.observe(default=True)
def config(cfg):
    # No pre-definition needed!
    cfg.training.optimizer.name = 'AdamW'
    cfg.training.optimizer.lr = 0.001
    cfg.model.encoder.num_layers = 12

Works with CLI:

python train.py model.backbone.resnet.depth=50 data.batch_size=32

More Convenience Utilities

# Attribute-style access
config.lr = 0.1
print(config.lr)  # Instead of config['lr']

# Nested access
print(config.model.backbone.type)  # Clean and readable

# Conditional updates - merge configs safely
base_config.update_if_absent(**experiment_config)

---

Scope: Configuration Management

Scope solves configuration complexity through priority-based merging and CLI integration. No more scattered config files or hard-coded parameters.

Key Concepts

Default Configs (priority=0)
    ↓
Named Configs (priority=0+)
    ↓
CLI Arguments (highest priority)
    ↓
Lazy Configs (computed after CLI)

Basic Usage

Simple Configuration

from ato.scope import Scope

scope = Scope()

@scope.observe()
def my_config(config):
    config.dataset = 'cifar10'
    config.lr = 0.001
    config.batch_size = 32

@scope
def train(config):
    print(f"Training on {config.dataset}")
    # Your code here

if __name__ == '__main__':
    train()

Priority-based Merging

@scope.observe(default=True)  # Always applied
def defaults(cfg):
    cfg.lr = 0.001
    cfg.epochs = 100

@scope.observe(priority=1)  # Applied after defaults
def high_lr(cfg):
    cfg.lr = 0.01

@scope.observe(priority=2)  # Applied last
def long_training(cfg):
    cfg.epochs = 300

python train.py                           # lr=0.001, epochs=100
python train.py high_lr                   # lr=0.01, epochs=100
python train.py high_lr long_training     # lr=0.01, epochs=300

CLI Configuration

Override any parameter from command line:

# Simple values
python train.py lr=0.01 batch_size=64

# Nested configs
python train.py model.backbone=%resnet101% model.depth=101

# Lists and complex types
python train.py layers=[64,128,256,512] dropout=0.5

# Combine with named configs
python train.py my_config lr=0.001 batch_size=128

Note: Wrap strings with % (e.g., %resnet101%) instead of quotes.

Advanced Features

1. Lazy Evaluation - Dynamic Configuration

Sometimes you need configs that depend on other values set via CLI:

@scope.observe()
def base_config(cfg):
    cfg.model = 'resnet50'
    cfg.dataset = 'imagenet'

@scope.observe(lazy=True)  # Evaluated AFTER CLI args
def computed_config(cfg):
    # Adjust based on dataset
    if cfg.dataset == 'imagenet':
        cfg.num_classes = 1000
        cfg.image_size = 224
    elif cfg.dataset == 'cifar10':
        cfg.num_classes = 10
        cfg.image_size = 32

python train.py dataset=%cifar10% computed_config
# Results in: num_classes=10, image_size=32

Python 3.11+ Context Manager:

@scope.observe()
def my_config(cfg):
    cfg.model = 'resnet50'
    cfg.num_layers = 50

    with Scope.lazy():  # Evaluated after CLI
        if cfg.model == 'resnet101':
            cfg.num_layers = 101

2. MultiScope - Multiple Configuration Contexts

Unique to Ato: Manage completely separate configuration namespaces. Unlike Hydra's config groups, MultiScope provides true namespace isolation with independent priority systems.

Why MultiScope?

Challenge	Hydra's Approach	Ato's MultiScope
Separate model/data configs	Config groups in one namespace	Independent scopes with own priorities
Avoid key collisions	Manual prefixing (model.lr, train.lr)	Automatic namespace isolation
Different teams/modules	Single config file	Each scope can be owned separately
Priority conflicts	Global priority system	Per-scope priority system

Basic Usage

from ato.scope import Scope, MultiScope

model_scope = Scope(name='model')
data_scope = Scope(name='data')
scope = MultiScope(model_scope, data_scope)

@model_scope.observe(default=True)
def model_config(model):
    model.backbone = 'resnet50'
    model.pretrained = True

@data_scope.observe(default=True)
def data_config(data):
    data.dataset = 'cifar10'
    data.batch_size = 32

@scope
def train(model, data):  # Named parameters match scope names
    print(f"Training {model.backbone} on {data.dataset}")

Real-world: Team Collaboration

Different team members can own different scopes without conflicts:

# team_model.py - ML team owns this
model_scope = Scope(name='model')

@model_scope.observe(default=True)
def resnet_default(model):
    model.backbone = 'resnet50'
    model.lr = 0.1  # Model-specific learning rate

@model_scope.observe(priority=1)
def resnet101(model):
    model.backbone = 'resnet101'
    model.lr = 0.05  # Different lr for bigger model

# team_data.py - Data team owns this
data_scope = Scope(name='data')

@data_scope.observe(default=True)
def cifar_default(data):
    data.dataset = 'cifar10'
    data.lr = 0.001  # Data augmentation learning rate (no conflict!)

@data_scope.observe(priority=1)
def imagenet(data):
    data.dataset = 'imagenet'
    data.workers = 16

# train.py - Integration point
from team_model import model_scope
from team_data import data_scope

scope = MultiScope(model_scope, data_scope)

@scope
def train(model, data):
    # Both have 'lr' but in separate namespaces!
    print(f"Model LR: {model.lr}, Data LR: {data.lr}")

Key advantage: model.lr and data.lr are completely independent. No need for naming conventions like model_lr vs data_lr.

CLI with MultiScope

Override each scope independently:

# Override model scope only
python train.py model.backbone=%resnet101%

# Override data scope only
python train.py data.dataset=%imagenet%

# Override both
python train.py model.backbone=%resnet101% data.dataset=%imagenet%

# Call named configs per scope
python train.py resnet101 imagenet

3. Import/Export Configs

Ato supports importing configs from multiple frameworks:

@scope.observe()
def load_external(config):
    # Load from any format
    config.load('experiments/baseline.json')
    config.load('models/resnet.yaml')

    # Export to any format
    config.dump('output/final_config.toml')

    # Import OpenMMLab configs - handles _base_ inheritance automatically
    config.load_mm_config('mmdet_configs/faster_rcnn.py')

OpenMMLab compatibility is built-in:

• Automatically resolves _base_ inheritance chains
• Supports _delete_ keys for config overriding
• Makes migration from MMDetection/MMSegmentation/etc. seamless

Hydra-style config composition is also built-in via compose_hierarchy:

from ato.adict import ADict

# Hydra-style directory structure:
# configs/
#   ├── config.yaml          # base config
#   ├── model/
#   │   ├── resnet50.yaml
#   │   └── resnet101.yaml
#   └── data/
#       ├── cifar10.yaml
#       └── imagenet.yaml

config = ADict.compose_hierarchy(
    root='configs',
    config_filename='config',
    select={
        'model': 'resnet50',      # or ['resnet50', 'resnet101'] for multiple
        'data': 'imagenet'
    },
    overrides={
        'model.lr': 0.01,
        'data.batch_size': 64
    },
    required=['model.backbone', 'data.dataset'],  # Validation
    on_missing='warn'  # or 'error'
)

Key features:

• Config groups (model/, data/, optimizer/, etc.)
• Automatic file discovery (tries .yaml, .json, .toml, .xyz)
• Dotted overrides (model.lr=0.01)
• Required key validation
• Flexible error handling

4. Argparse Integration

Mix Ato with existing argparse code:

from ato.scope import Scope
import argparse

scope = Scope(use_external_parser=True)
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--seed', type=int, default=42)

@scope.observe(default=True)
def config(cfg):
    cfg.lr = 0.001
    cfg.batch_size = 32

@scope
def train(cfg):
    print(f"GPU: {cfg.gpu}, LR: {cfg.lr}")

if __name__ == '__main__':
    parser.parse_args()  # Merges argparse with scope
    train()

5. Configuration Documentation & Inspection

One of Ato's most powerful features: Auto-generate documentation AND visualize the exact order of configuration application.

Basic Documentation

@scope.manual
def config_docs(cfg):
    cfg.lr = 'Learning rate for optimizer'
    cfg.batch_size = 'Number of samples per batch'
    cfg.model = 'Model architecture (resnet50, resnet101, etc.)'

python train.py manual

Output:

--------------------------------------------------
[Scope "config"]
(The Applying Order of Views)
defaults → (CLI Inputs) → lazy_config → main

(User Manuals)
config.lr: Learning rate for optimizer
config.batch_size: Number of samples per batch
config.model: Model architecture (resnet50, resnet101, etc.)
--------------------------------------------------

Why This Matters

The applying order visualization shows you exactly how your configs are merged:

• Which config functions are applied (in order)
• When CLI inputs override values
• Where lazy configs are evaluated
• The final function that uses the config

This prevents configuration bugs by making the merge order explicit and debuggable.

MultiScope Documentation

For complex projects with multiple scopes, manual shows each scope separately:

from ato.scope import Scope, MultiScope

model_scope = Scope(name='model')
train_scope = Scope(name='train')
scope = MultiScope(model_scope, train_scope)

@model_scope.observe(default=True)
def model_defaults(model):
    model.backbone = 'resnet50'
    model.num_layers = 50

@model_scope.observe(priority=1)
def model_advanced(model):
    model.pretrained = True

@model_scope.observe(lazy=True)
def model_lazy(model):
    if model.backbone == 'resnet101':
        model.num_layers = 101

@train_scope.observe(default=True)
def train_defaults(train):
    train.lr = 0.001
    train.epochs = 100

@model_scope.manual
def model_docs(model):
    model.backbone = 'Model backbone architecture'
    model.num_layers = 'Number of layers in the model'

@train_scope.manual
def train_docs(train):
    train.lr = 'Learning rate for optimizer'
    train.epochs = 'Total training epochs'

@scope
def main(model, train):
    print(f"Training {model.backbone} with lr={train.lr}")

if __name__ == '__main__':
    main()

python train.py manual

Output:

--------------------------------------------------
[Scope "model"]
(The Applying Order of Views)
model_defaults → model_advanced → (CLI Inputs) → model_lazy → main

(User Manuals)
model.backbone: Model backbone architecture
model.num_layers: Number of layers in the model
--------------------------------------------------
[Scope "train"]
(The Applying Order of Views)
train_defaults → (CLI Inputs) → main

(User Manuals)
train.lr: Learning rate for optimizer
train.epochs: Total training epochs
--------------------------------------------------

Real-world Example

This is especially valuable when debugging why a config value isn't what you expect:

@scope.observe(default=True)
def defaults(cfg):
    cfg.lr = 0.001

@scope.observe(priority=1)
def experiment_config(cfg):
    cfg.lr = 0.01

@scope.observe(priority=2)
def another_config(cfg):
    cfg.lr = 0.1

@scope.observe(lazy=True)
def adaptive_lr(cfg):
    if cfg.batch_size > 64:
        cfg.lr = cfg.lr * 2

When you run python train.py manual, you see:

(The Applying Order of Views)
defaults → experiment_config → another_config → (CLI Inputs) → adaptive_lr → main

Now it's crystal clear why lr=0.1 (from another_config) and not 0.01!

---

SQL Tracker: Experiment Tracking

Lightweight experiment tracking using SQLite - no external services, no setup complexity.

Why SQL Tracker?

• Zero Setup: Just a SQLite file, no servers
• Full History: Track all runs, metrics, and artifacts
• Smart Search: Find similar experiments by config structure
• Code Versioning: Track code changes via fingerprints

Database Schema

Project (my_ml_project)
├── Experiment (run_1)
│   ├── config: {...}
│   ├── structural_hash: "abc123..."
│   ├── Metrics: [loss, accuracy, ...]
│   ├── Artifacts: [model.pt, plots/*, ...]
│   └── Fingerprints: [model_forward, train_step, ...]
├── Experiment (run_2)
└── ...

Quick Start

Logging Experiments

from ato.db_routers.sql.manager import SQLLogger
from ato.adict import ADict

# Setup config
config = ADict(
    experiment=ADict(
        project_name='image_classification',
        sql=ADict(db_path='sqlite:///experiments.db')
    ),
    # Your hyperparameters
    lr=0.001,
    batch_size=32,
    model='resnet50'
)

# Create logger
logger = SQLLogger(config)

# Start experiment run
run_id = logger.run(tags=['baseline', 'resnet50', 'cifar10'])

# Training loop
for epoch in range(100):
    # Your training code
    train_loss = train_one_epoch()
    val_acc = validate()

    # Log metrics
    logger.log_metric('train_loss', train_loss, step=epoch)
    logger.log_metric('val_accuracy', val_acc, step=epoch)

# Log artifacts
logger.log_artifact(run_id, 'checkpoints/model_best.pt',
                   data_type='model',
                   metadata={'epoch': best_epoch})

# Finish run
logger.finish(status='completed')

Querying Experiments

from ato.db_routers.sql.manager import SQLFinder

finder = SQLFinder(config)

# Get all runs in project
runs = finder.get_runs_in_project('image_classification')
for run in runs:
    print(f"Run {run.id}: {run.config.model} - {run.status}")

# Find best performing run
best_run = finder.find_best_run(
    project_name='image_classification',
    metric_key='val_accuracy',
    mode='max'  # or 'min' for loss
)
print(f"Best config: {best_run.config}")

# Find similar experiments (same config structure)
similar = finder.find_similar_runs(run_id=123)
print(f"Found {len(similar)} runs with similar config structure")

# Trace statistics (code fingerprints)
stats = finder.get_trace_statistics('image_classification', trace_id='model_forward')
print(f"Model forward pass has {stats['static_trace_versions']} versions")

Real-world Example: Experiment Comparison

# Compare hyperparameter impact
finder = SQLFinder(config)

runs = finder.get_runs_in_project('my_project')
for run in runs:
    # Get final accuracy
    final_metrics = [m for m in run.metrics if m.key == 'val_accuracy']
    best_acc = max(m.value for m in final_metrics) if final_metrics else 0

    print(f"LR: {run.config.lr}, Batch: {run.config.batch_size} → Acc: {best_acc:.2%}")

Features Summary

Feature	Description
Structural Hash	Auto-track config structure changes
Metric Logging	Time-series metrics with step tracking
Artifact Management	Track model checkpoints, plots, data files
Fingerprint Tracking	Version control for code (static & runtime)
Smart Search	Find similar configs, best runs, statistics

---

Hyperparameter Optimization

Built-in Hyperband algorithm for efficient hyperparameter search with early stopping.

Extensible Design

Ato's hyperopt module is built for extensibility and reusability:

Component	Purpose	Benefit
GridSpaceMixIn	Parameter sampling logic	Reusable across different algorithms
HyperOpt	Base optimization class	Easy to implement custom strategies
DistributedMixIn	Distributed training support	Optional, composable

This design makes it trivial to implement custom search algorithms:

from ato.hyperopt.base import GridSpaceMixIn, HyperOpt

class RandomSearch(GridSpaceMixIn, HyperOpt):
    def main(self, func):
        # Reuse GridSpaceMixIn.prepare_distributions()
        configs = self.prepare_distributions(self.config, self.search_spaces)

        # Implement random sampling
        import random
        random.shuffle(configs)

        results = []
        for config in configs[:10]:  # Sample 10 random configs
            metric = func(config)
            results.append((config, metric))

        return max(results, key=lambda x: x[1])

How Hyperband Works

Hyperband uses successive halving:

1. Start with many configs, train briefly
2. Keep top performers, discard poor ones
3. Train survivors longer
4. Repeat until one winner remains

Basic Usage

from ato.adict import ADict
from ato.hyperopt.hyperband import HyperBand
from ato.scope import Scope

scope = Scope()

# Define search space
search_spaces = ADict(
    lr=ADict(
        param_type='FLOAT',
        param_range=(1e-5, 1e-1),
        num_samples=20,
        space_type='LOG'  # Logarithmic spacing
    ),
    batch_size=ADict(
        param_type='INTEGER',
        param_range=(16, 128),
        num_samples=5,
        space_type='LOG'
    ),
    model=ADict(
        param_type='CATEGORY',
        categories=['resnet50', 'resnet101', 'efficientnet_b0']
    )
)

# Create Hyperband optimizer
hyperband = HyperBand(
    scope,
    search_spaces,
    halving_rate=0.3,      # Keep top 30% each round
    num_min_samples=3,     # Stop when <= 3 configs remain
    mode='max'             # Maximize metric (use 'min' for loss)
)

@hyperband.main
def train(config):
    # Your training code
    model = create_model(config.model)
    optimizer = Adam(lr=config.lr)

    # Use __num_halved__ for early stopping
    num_epochs = compute_epochs(config.__num_halved__)

    # Train and return metric
    val_acc = train_and_evaluate(model, optimizer, num_epochs)
    return val_acc

if __name__ == '__main__':
    # Run hyperparameter search
    best_result = train()
    print(f"Best config: {best_result.config}")
    print(f"Best metric: {best_result.metric}")

Automatic Step Calculation

Let Hyperband compute optimal training steps:

hyperband = HyperBand(scope, search_spaces, halving_rate=0.3, num_min_samples=4)

max_steps = 100000
steps_per_generation = hyperband.compute_optimized_initial_training_steps(max_steps)
# Example output: [27, 88, 292, 972, 3240, 10800, 36000, 120000]

# Use in training
@hyperband.main
def train(config):
    generation = config.__num_halved__
    num_steps = steps_per_generation[generation]

    metric = train_for_n_steps(num_steps)
    return metric

Parameter Types

Type	Description	Example
FLOAT	Continuous values	Learning rate, dropout
INTEGER	Discrete integers	Batch size, num layers
CATEGORY	Categorical choices	Model type, optimizer

Space types:

• LOG: Logarithmic spacing (good for learning rates)
• LINEAR: Linear spacing (default)

Distributed Hyperparameter Search

Ato supports distributed hyperparameter optimization out of the box:

from ato.hyperopt.hyperband import DistributedHyperBand
import torch.distributed as dist

# Initialize distributed training
dist.init_process_group(backend='nccl')
rank = dist.get_rank()
world_size = dist.get_world_size()

# Create distributed hyperband
hyperband = DistributedHyperBand(
    scope,
    search_spaces,
    halving_rate=0.3,
    num_min_samples=3,
    mode='max',
    rank=rank,
    world_size=world_size,
    backend='pytorch'
)

@hyperband.main
def train(config):
    # Your distributed training code
    model = create_model(config)
    model = DDP(model, device_ids=[rank])
    metric = train_and_evaluate(model)
    return metric

if __name__ == '__main__':
    result = train()
    if rank == 0:
        print(f"Best config: {result.config}")

Key features:

• Automatic work distribution across GPUs
• Synchronized config selection via broadcast_object_from_root
• Results aggregation with all_gather_object
• Compatible with PyTorch DDP, FSDP, DeepSpeed

---

Best Practices

1. Project Structure

my_project/
├── configs/
│   ├── default.py       # Default config with @scope.observe(default=True)
│   ├── models.py        # Model-specific configs
│   └── datasets.py      # Dataset configs
├── train.py             # Main training script
├── experiments.db       # SQLite experiment tracking
└── experiments/
    ├── run_001/
    │   ├── checkpoints/
    │   └── logs/
    └── run_002/

2. Config Organization

# configs/default.py
from ato.scope import Scope

scope = Scope()

@scope.observe(default=True)
def defaults(cfg):
    # Data
    cfg.data = ADict(
        dataset='cifar10',
        batch_size=32,
        num_workers=4
    )

    # Model
    cfg.model = ADict(
        backbone='resnet50',
        pretrained=True
    )

    # Training
    cfg.train = ADict(
        lr=0.001,
        epochs=100,
        optimizer='adam'
    )

    # Experiment tracking
    cfg.experiment = ADict(
        project_name='my_project',
        sql=ADict(db_path='sqlite:///experiments.db')
    )

3. Combined Workflow

from ato.scope import Scope
from ato.db_routers.sql.manager import SQLLogger
from configs.default import scope

@scope
def train(cfg):
    # Setup experiment tracking
    logger = SQLLogger(cfg)
    run_id = logger.run(tags=[cfg.model.backbone, cfg.data.dataset])

    try:
        # Training loop
        for epoch in range(cfg.train.epochs):
            loss = train_epoch()
            acc = validate()

            logger.log_metric('loss', loss, epoch)
            logger.log_metric('accuracy', acc, epoch)

        logger.finish(status='completed')

    except Exception as e:
        logger.finish(status='failed')
        raise e

if __name__ == '__main__':
    train()

4. Reproducibility Checklist

• ✅ Use structural hashing to track config changes
• ✅ Log all hyperparameters to SQLLogger
• ✅ Tag experiments with meaningful labels
• ✅ Track artifacts (checkpoints, plots)
• ✅ Use lazy configs for derived parameters
• ✅ Document configs with @scope.manual

---

Requirements

• Python >= 3.7
• SQLAlchemy (for SQL Tracker)
• PyYAML, toml (for config serialization)

See pyproject.toml for full dependencies.

---

License

MIT License

---

Future Work — Optional, Modular, Non-Intrusive

We're planning to add an HTML dashboard (as a small local daemon) for teams that want visual exploration:

Planned features:

• Metric comparison & trends
• Run history & artifact browsing
• Configuration diffs (including structural hash visualization)
• Interactive hyperparameter analysis

Philosophy stays the same:

• No hard dependency - Ato core (Scope / ADict / SQL tracker / HyperOpt) works 100% without the dashboard
• No coupling - The dashboard is a separate process that reads from SQLite/logs; it doesn't block or modify your runs
• Zero lock-in - Remove the dashboard and nothing in your training code changes
• Fully modular - Pick only what you need

Example workflows:

What you need	What you use
Just configs	ADict + Scope only — no DB, no UI
Headless tracking	Add SQL tracker — still no UI
Visual exploration	Start dashboard daemon when you want; stop it and keep training
Full stack	Use everything, or mix with MLflow/W&B dashboards

Guiding rule: Ato is a set of small, composable tools — not a monolith. Use what helps; ignore the rest.

---

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

Development Setup

git clone https://github.com/yourusername/ato.git
cd ato
pip install -e .

---

Working with Existing Tools

Ato isn't meant to replace Hydra, MLflow, or W&B — it's a composable layer you can use alongside them.

Think of Ato as a "config control surface" that gives you clarity and structure without forcing you into a framework. Many teams use Ato for the 90% of experiments that don't need heavy infrastructure, then graduate to larger tools when needed.

Ato + Hydra = Better Together

Ato has built-in Hydra compatibility via compose_hierarchy():

from ato.adict import ADict

# Load Hydra-style configs directly
config = ADict.compose_hierarchy(
    root='configs',
    config_filename='config',
    select={'model': 'resnet50', 'data': 'imagenet'},
    overrides={'model.lr': 0.01}
)

# Now add Ato's unique features on top:
# - MultiScope for namespace isolation
# - `manual` command for merge debugging
# - Built-in SQL tracking

Migration from Hydra is literally just replacing hydra.compose() with ADict.compose_hierarchy().

What Makes Ato Different?

Ato focuses on three unique capabilities that complement existing tools:

Feature	What It Solves	Why It Matters
MultiScope	True namespace isolation	Multiple teams can own separate config scopes without key collisions (no model_lr vs data_lr prefixing needed)
manual command	Config merge order visualization	Debug why a config value is set — see exact merge order, not just final result
Offline-first tracking	Zero-setup SQLite tracking	Experiment tracking without servers, platforms, or external dependencies

Compatibility Matrix

Ato plays nicely with your existing stack:

Tool	Ato's Role	Integration
Hydra	Extends with MultiScope + merge debugging	compose_hierarchy() loads Hydra configs directly
MLflow	Lightweight alternative for simple projects	Use Ato's SQL tracker for offline work, MLflow for dashboards
W&B	Offline-first complement	Track locally with Ato, sync to W&B when needed
OpenMMLab	Config migration layer	load_mm_config() handles _base_ inheritance
PyTorch/TF/JAX	Framework-agnostic config + tracking	Works with any training framework

When to Use What

Use Ato alone for:

• Individual research experiments
• Projects that don't need a dashboard
• Teams wanting namespace isolation (MultiScope)
• Config merge debugging (manual command)

Use Ato + Hydra when:

• You need Hydra's deep config hierarchies
• Your team already uses Hydra YAML structure
• You want MultiScope on top of Hydra's composition

Use Ato + MLflow/W&B when:

• You want local-first tracking with optional cloud sync
• You need Ato's structural hashing + offline SQLite
• Your team prefers MLflow/W&B dashboards for collaboration

Graduate to pure MLflow/W&B when:

• You need real-time dashboards and team collaboration UI
• Model registry and dataset versioning become critical
• Your experiments are production-facing

What Ato Doesn't Do

Ato intentionally skips features that larger tools handle better:

• ❌ Real-time web dashboards (use MLflow/W&B)
• ❌ Model registry (use MLflow)
• ❌ Dataset versioning (use W&B/DVC)
• ❌ Deep plugin ecosystems (use Hydra)

Ato's philosophy: give you enough structure to stay organized, without becoming infrastructure.