# Dream Trainer Documentation Roadmap

## Priority Matrix

### 🔴 Critical (Must Have)
These documents are essential for users to successfully use Dream Trainer.

| Document | Current State | Priority | Effort | Owner |
|----------|--------------|----------|--------|-------|
| API Reference: Trainers | Missing | P0 | High | |
| API Reference: Mixins | Missing | P0 | High | |
| Core Concepts Guide | Missing | P0 | Medium | |
| Debugging Common Issues | Missing | P0 | Medium | |
| Installation Troubleshooting | Partial | P0 | Low | |

### 🟡 Important (Should Have)
These enhance user experience and adoption.

| Document | Current State | Priority | Effort | Owner |
|----------|--------------|----------|--------|-------|
| Tutorial: First Trainer | Missing | P1 | Medium | |
| Tutorial: Multi-GPU | Missing | P1 | Medium | |
| Performance Tuning Guide | Missing | P1 | High | |
| Migration from Lightning | Missing | P1 | Medium | |
| Example: Vision Models | Partial | P1 | Low | |

### 🟢 Nice to Have
These provide additional value for advanced users.

| Document | Current State | Priority | Effort | Owner |
|----------|--------------|----------|--------|-------|
| Video Tutorials | Missing | P2 | High | |
| Benchmarks | Missing | P2 | Medium | |
| Case Studies | Missing | P2 | Medium | |
| Contributing Guide | Missing | P2 | Low | |

## Detailed Page Specifications

### 1. Core Concepts Guide (New Page)

**File**: `docs/core-concepts.md`

```markdown
# Core Concepts

## Understanding DTensor

### What is DTensor?
- Definition and purpose
- Comparison with traditional distributed tensors
- Code example: Creating a DTensor

### Placement and Sharding
- DeviceMesh concept
- Sharding specifications
- Interactive examples

## The Mixin Architecture

### Why Mixins?
- Composition vs inheritance
- Flexibility and reusability
- Real-world analogy

### How Mixins Work
- Method Resolution Order (MRO)
- Combining functionalities
- Code example: Building a custom trainer

### Available Mixins
- SetupMixin: Model and optimizer management
- EvalMetricMixin: Metrics tracking
- LoggerMixin: Logging capabilities
- QuantizeMixin: Model quantization

## Training Loop Lifecycle

### Initialization Phase
- Configuration loading
- Device setup
- Model initialization on meta device

### Training Phase
- Forward pass
- Loss computation
- Backward pass with gradient accumulation
- Optimizer step

### Validation Phase
- Metric computation
- Checkpointing decisions
- Early stopping logic

## State Management

### Trainer State
- global_step tracking
- epoch management
- checkpoint resumption

### Distributed State
- Process synchronization
- Collective operations
- Fault tolerance
```

### 2. API Reference Structure

**Directory**: `docs/api/`

```
api/
├── index.md                 # API overview and navigation
├── trainers/
│   ├── abstract.md         # AbstractTrainer reference
│   ├── base.md            # BaseTrainer reference
│   └── dream.md           # DreamTrainer reference
├── mixins/
│   ├── setup.md           # Setup mixins
│   ├── eval_metric.md     # Evaluation mixins
│   ├── loggers.md         # Logger mixins
│   └── quantize.md        # Quantization mixins
├── callbacks/
│   ├── base.md            # Callback base class
│   ├── checkpoint.md      # Checkpoint callbacks
│   ├── monitoring.md      # Monitoring callbacks
│   └── performance.md     # Performance callbacks
├── configuration/
│   ├── parameters.md      # Parameter classes
│   ├── device.md          # Device configuration
│   └── training.md        # Training configuration
└── utilities/
    ├── world.md           # World management
    ├── data.md            # Data utilities
    └── common.md          # Common utilities
```

### 3. Tutorial Series

**Directory**: `docs/tutorials/`

#### Tutorial 1: Your First Trainer
```markdown
# Tutorial 1: Building Your First Trainer

**Time**: 30 minutes  
**Prerequisites**: Basic PyTorch knowledge

## Learning Objectives
- Understand Dream Trainer's basic structure
- Build a simple MNIST classifier
- Run training locally

## Step 1: Setup
```python
# Install Dream Trainer
pip install dream-trainer[metrics]

# Verify installation
import dream_trainer
print(dream_trainer.__version__)
```

## Step 2: Create the Model
```python
import torch.nn as nn

class MNISTModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 32, 3)
        self.conv2 = nn.Conv2d(32, 64, 3)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)
    
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = torch.flatten(x, 1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x
```

## Step 3: Define the Trainer
[... complete tutorial content ...]
```

### 4. Example Gallery Structure

**Directory**: `examples/`

```python
# examples/vision/resnet_imagenet.py
"""
ResNet ImageNet Training Example

This example demonstrates:
- Multi-GPU training with FSDP2
- Mixed precision training
- Efficient data loading
- Learning rate scheduling

Usage:
    python resnet_imagenet.py --batch-size 256 --gpus 8
"""

from dataclasses import dataclass
from dream_trainer import DreamTrainer, DreamTrainerConfig
from dream_trainer.configs import DeviceParameters, TrainingParameters

@dataclass
class ResNetConfig(DreamTrainerConfig):
    """Configuration for ResNet training"""
    model_name: str = "resnet50"
    num_classes: int = 1000
    learning_rate: float = 0.1
    momentum: float = 0.9
    weight_decay: float = 1e-4
    
# ... complete example ...
```

### 5. Migration Guides

**File**: `docs/migration/from-pytorch-lightning.md`

```markdown
# Migrating from PyTorch Lightning

## Overview

This guide helps you migrate existing PyTorch Lightning code to Dream Trainer.

## Conceptual Mapping

| PyTorch Lightning | Dream Trainer | Notes |
|-------------------|---------------|-------|
| `LightningModule` | `BaseTrainer` + Mixins | Composition over inheritance |
| `training_step()` | `training_step()` | Same interface |
| `configure_optimizers()` | `configure_optimizers()` | Same interface |
| `LightningDataModule` | `DataLoaderSetupMixin` | More flexible |
| `Trainer(...)` | `Config + Callbacks` | Explicit configuration |

## Step-by-Step Migration

### Step 1: Convert LightningModule

**Before (Lightning):**
```python
class MyModel(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.model = torch.nn.Linear(10, 1)
    
    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.model(x)
        loss = F.mse_loss(y_hat, y)
        self.log('train_loss', loss)
        return loss
```

**After (Dream Trainer):**
```python
class MyTrainer(BaseTrainer, SetupMixin):
    def configure_models(self):
        self.model = torch.nn.Linear(10, 1)
    
    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.model(x)
        loss = F.mse_loss(y_hat, y)
        self.backward(loss)
        return {"loss": loss}
```

[... continue with more examples ...]
```

## Implementation Schedule

### Week 1-2: Foundation
- [ ] Core Concepts guide
- [ ] API reference structure setup
- [ ] Trainer API documentation

### Week 3-4: API Documentation  
- [ ] Mixins API documentation
- [ ] Callbacks API documentation
- [ ] Configuration API documentation
- [ ] Utilities documentation

### Week 5-6: Tutorials
- [ ] Tutorial 1: First Trainer
- [ ] Tutorial 2: Multi-GPU Training
- [ ] Tutorial 3: Custom Components
- [ ] Tutorial 4: Production Setup

### Week 7-8: Examples & Advanced
- [ ] Vision examples (3)
- [ ] NLP examples (3)
- [ ] Performance tuning guide
- [ ] Debugging guide

### Week 9: Community
- [ ] Migration guides
- [ ] Contributing guide
- [ ] FAQ compilation

## Quality Checklist

### For Each Document:
- [ ] Clear learning objectives
- [ ] Runnable code examples
- [ ] Links to related documents
- [ ] Tested on latest version
- [ ] Reviewed by subject expert

### For API Reference:
- [ ] All public methods documented
- [ ] Type hints included
- [ ] Examples for each class
- [ ] Inheritance hierarchy clear

### For Tutorials:
- [ ] Progressive difficulty
- [ ] Time estimates accurate
- [ ] Prerequisites listed
- [ ] Downloadable notebooks

## Metrics & Tracking

### Documentation Coverage
```python
# Track with automated script
total_public_apis = count_public_apis()
documented_apis = count_documented_apis()
coverage = documented_apis / total_public_apis * 100
```

### User Success Metrics
- Time to first successful training run
- Support ticket reduction
- Community engagement
- Documentation feedback scores

## Next Steps

1. Set up documentation infrastructure
2. Create issue templates for docs
3. Assign owners to priority items
4. Begin with Critical (P0) items
5. Weekly progress reviews 