Metadata-Version: 2.4
Name: wandb-generic
Version: 1.0.12
Summary: A truly generic Python package for Weights & Biases integration with any ML/DL library or long-running function.
Author: WandB Generic Contributors
License: MIT License
        
        Copyright (c) 2024 wandb-generic contributors
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE. 
Project-URL: Homepage, https://github.com/MayukhSobo/wandb-generic
Project-URL: Documentation, https://github.com/MayukhSobo/wandb-generic#readme
Project-URL: Repository, https://github.com/MayukhSobo/wandb-generic
Project-URL: Bug Tracker, https://github.com/MayukhSobo/wandb-generic/issues
Project-URL: Changelog, https://github.com/MayukhSobo/wandb-generic/releases
Keywords: wandb,weights-and-biases,machine-learning,deep-learning,experiment-tracking,logging,pytorch,tensorflow,generic
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: System :: Logging
Classifier: Typing :: Typed
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: wandb>=0.15.0
Requires-Dist: pyyaml>=6.0
Requires-Dist: numpy>=1.19.0
Provides-Extra: torch
Requires-Dist: torch>=1.9.0; extra == "torch"
Provides-Extra: tensorflow
Requires-Dist: tensorflow>=2.6.0; extra == "tensorflow"
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: pytest-cov>=4.0; extra == "dev"
Requires-Dist: black>=22.0; extra == "dev"
Requires-Dist: isort>=5.10; extra == "dev"
Requires-Dist: mypy>=1.0; extra == "dev"
Requires-Dist: pre-commit>=2.20; extra == "dev"
Provides-Extra: all
Requires-Dist: torch>=1.9.0; extra == "all"
Requires-Dist: tensorflow>=2.6.0; extra == "all"
Dynamic: license-file

# WandB Generic Logger 🚀

[![PyPI version](https://badge.fury.io/py/wandb-generic.svg)](https://badge.fury.io/py/wandb-generic)
[![Python](https://img.shields.io/pypi/pyversions/wandb-generic.svg)](https://pypi.org/project/wandb-generic/)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/wandb-generic)](https://pypi.org/project/wandb-generic/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Tests](https://github.com/MayukhSobo/wandb-generic/actions/workflows/test.yml/badge.svg)](https://github.com/MayukhSobo/wandb-generic/actions/workflows/test.yml)
[![GitHub issues](https://img.shields.io/github/issues/MayukhSobo/wandb-generic)](https://github.com/MayukhSobo/wandb-generic/issues)
[![GitHub stars](https://img.shields.io/github/stars/MayukhSobo/wandb-generic)](https://github.com/MayukhSobo/wandb-generic/stargazers)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

A **truly generic** and professional Python package that brings Weights & Biases experiment tracking to any ML/DL library or long-running function. No more manual logging - just specify what variables you want to track in a config file and let the magic happen!

## ✨ Key Features

- **🔧 Framework Agnostic**: Works with PyTorch, TensorFlow, scikit-learn, or any Python function
- **📝 Flexible Variable Logging**: Log ANY variables from your function by name - no hardcoded metrics
- **🎯 Multiple Logging Patterns**: Automatic capture, generator functions, context managers
- **🧪 Beyond ML**: Use for finance, physics, optimization, data processing - any domain
- **🔄 Hyperparameter Sweeps**: Built-in WandB sweep integration
- **💾 Model Checkpointing**: Automatic artifact logging
- **🚦 Professional**: Error handling, type hints, comprehensive validation

## 🚀 Quick Start

### Installation

```bash
pip install wandb-generic
```

### Basic Usage

1. **Create a config file** (`config.yaml`):

```yaml
wandb:
  project: "my-awesome-project"

hyperparameters:
  learning_rate: 0.01
  epochs: 10

logger:
  metrics:
    - loss        # ANY variable name from your function  
    - epoch       # Traditional names work
    - accuracy    # Descriptive names work
    - x           # Short names work (single letters)
    - y           # Any variables you create
```

2. **Add the decorator** to your function:

```python
from wandb_generic import WandbGenericLogger

@WandbGenericLogger(config_path="config.yaml")
def train_model(wandb_run=None):
    model = create_model()
    optimizer = torch.optim.Adam(model.parameters(), lr=wandb_run.config.learning_rate)
    
    for epoch in range(wandb_run.config.epochs):
        # Use ANY variable names you want!
        loss = train_one_epoch(model, optimizer)
        accuracy = validate_model(model)
        x = loss      # Custom names work too!
        y = epoch     # Any variable names
        
        # These variables are automatically logged! ✨
        # No manual wandb.log() calls needed
    
    return model

# Run your training
trained_model = train_model()
```

That's it! The decorator automatically captures and logs the variables specified in your config.

## 🎯 Framework Examples

### PyTorch

```python
@WandbGenericLogger(config_path="config.yaml")
def train_pytorch_model(wandb_run=None):
    model = torch.nn.Sequential(...)
    optimizer = torch.optim.Adam(model.parameters())
    
    for epoch in range(wandb_run.config.epochs):
        train_loss = train_one_epoch(model, optimizer)
        val_accuracy = validate_model(model)
        learning_rate = optimizer.param_groups[0]['lr']
        # Auto-logged based on config
```

### TensorFlow/Keras

```python
@WandbGenericLogger(config_path="config.yaml")
def train_tf_model(wandb_run=None):
    model = tf.keras.Sequential([...])
    
    for epoch in range(wandb_run.config.epochs):
        history = model.fit(x_train, y_train, validation_data=(x_val, y_val))
        train_loss = history.history['loss'][0]
        val_loss = history.history['val_loss'][0]
        val_accuracy = history.history['val_accuracy'][0]
        # Auto-logged based on config
```

### Scikit-learn

```python
@WandbGenericLogger(config_path="config.yaml")  
def train_sklearn_model(wandb_run=None):
    model = RandomForestClassifier()
    
    for n_estimators in [10, 50, 100, 200]:
        model.set_params(n_estimators=n_estimators)
        model.fit(X_train, y_train)
        
        train_score = model.score(X_train, y_train)
        val_score = model.score(X_val, y_val)
        feature_importance = model.feature_importances_.mean()
        # Auto-logged based on config
```

## 🧪 Beyond Machine Learning

This package works for ANY domain:

### Financial Analysis

```python
@WandbGenericLogger(config_path="config.yaml")
def analyze_trading_strategy(wandb_run=None):
    for trading_day in range(wandb_run.config.epochs):
        portfolio_return = execute_trading_strategy()
        sharpe_ratio = calculate_sharpe_ratio()
        max_drawdown = calculate_drawdown()
        volatility = calculate_volatility()
        # All metrics logged automatically
```

### Scientific Computing

```python
@WandbGenericLogger(config_path="config.yaml")
def simulate_physics(wandb_run=None):
    for time_step in range(wandb_run.config.epochs):
        kinetic_energy = calculate_kinetic_energy()
        potential_energy = calculate_potential_energy()
        total_energy = kinetic_energy + potential_energy
        system_temperature = calculate_temperature()
        # Physics metrics logged automatically
```

## 📁 Configuration Reference

### Complete YAML Configuration

```yaml
wandb:
  project: "project-name"           # Required
  entity: "your-entity"             # Optional
  tags: ["tag1", "tag2"]           # Optional
  notes: "Experiment description"   # Optional

hyperparameters:
  learning_rate: 0.01              # Any hyperparameters you want
  batch_size: 32
  epochs: 100

sweep:
  method: "bayes"                   # random, grid, bayes
  metric:
    name: "loss"                    # Any metric name from your function
    goal: "minimize"                # minimize or maximize
  parameters:
    learning_rate:
      values: [0.1, 0.01, 0.001]
    batch_size:
      values: [16, 32, 64]

logger:
  metrics:                          # List ANY variable names to log
    - loss
    - accuracy
    - epoch
    - custom_metric
    - processing_time
  log_frequency: 1                  # Log every N iterations

checkpoint:
  name: "my-model"
  type: "model"
  save_frequency: 5                 # Save every N epochs
```

## 🔄 Supported Logging Patterns

### 1. Automatic Variable Capture (Recommended)

```python
@WandbGenericLogger(config_path="config.yaml")
def my_function(wandb_run=None):
    for iteration in range(10):
        metric_value = compute_metric()
        loss_score = compute_loss()
        # Variables automatically logged if in config
```

### 2. Generator Functions

```python
@WandbGenericLogger(config_path="config.yaml")
def training_generator(wandb_run=None):
    for epoch in range(10):
        loss = train_epoch()
        yield {"loss": loss, "epoch": epoch}
```

### 3. Context Manager

```python
from wandb_generic import WandbMetricLogger

@WandbGenericLogger(config_path="config.yaml")
def explicit_logging(wandb_run=None):
    with WandbMetricLogger(wandb_run) as logger:
        for i in range(10):
            metric = compute_metric()
            logger.log({"iteration": i, "metric": metric})
```

## 🔧 Advanced Features

### Custom Logging Function

```python
def my_custom_logger(metrics, step):
    print(f"Step {step}: {metrics}")

@WandbGenericLogger(
    config_path="config.yaml",
    log_frequency=5,  # Log every 5 iterations
    custom_logger=my_custom_logger
)
def my_function(wandb_run=None):
    # Your code here
    pass
```

### Error Handling

The package includes comprehensive error handling:
- Validates config file structure
- Handles missing metrics gracefully  
- Converts tensor types automatically (PyTorch, NumPy)
- Provides helpful error messages

### Type Safety

Full type hints for better IDE support:

```python
from typing import Dict, Any, List
from wandb_generic import WandbGenericLogger, WandbMetricLogger
```

## 🚀 Migration from Manual Logging

**Before (Manual logging):**
```python
def train_model():
    wandb.init(project="my-project")
    
    for epoch in range(10):
        loss = train_epoch()
        acc = validate()
        
        wandb.log({
            "loss": loss,
            "accuracy": acc,
            "epoch": epoch
        })
```

**After (Generic logging):**
```python
@WandbGenericLogger(config_path="config.yaml")
def train_model(wandb_run=None):
    for epoch in range(wandb_run.config.epochs):
        loss = train_epoch()
        accuracy = validate()
        # That's it! No manual logging needed
```

## 🤝 Contributing

We welcome contributions! Please see our contributing guidelines and feel free to submit issues or pull requests.

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🙋‍♂️ Support

- 📖 **Documentation**: Complete examples in the `examples/` directory
- 🐛 **Issues**: Report bugs on our GitHub issues page
- 💬 **Discussions**: Join our community discussions

---

**Ready to make your experiment tracking effortless and truly generic? Install wandb-generic today!** 🚀
