# API Endpoints - Optimization Parameters Wired Up

All optimization parameters are now exposed through the API endpoints.

## ✅ Updated Endpoints

### 1. `/train/start` (Fine-tuning)

**Request Model**: `TrainRequest`

**New Optimization Parameters**:

- `gradient_accumulation_steps` (int, default: 1) - Gradient accumulation
- `use_amp` (bool, default: True) - Mixed precision training
- `warmup_steps` (int, default: 0) - Learning rate warmup
- `num_workers` (Optional[int], default: None) - Data loading workers
- `resume_from_checkpoint` (Optional[str], default: None) - Resume training
- `use_ema` (bool, default: False) - Exponential Moving Average
- `ema_decay` (float, default: 0.9999) - EMA decay factor
- `use_onecycle` (bool, default: False) - OneCycleLR scheduler
- `use_gradient_checkpointing` (bool, default: False) - Memory-efficient training
- `compile_model` (bool, default: True) - Torch.compile optimization

**Example Request**:

```json
{
  "training_data_dir": "data/training",
  "epochs": 10,
  "lr": 1e-5,
  "batch_size": 1,
  "use_amp": true,
  "gradient_accumulation_steps": 4,
  "use_ema": true,
  "use_onecycle": true,
  "compile_model": true
}
```

### 2. `/train/pretrain` (Pre-training)

**Request Model**: `PretrainRequest`

**New Optimization Parameters**:

- All the same as `/train/start` plus:
- `cache_dir` (Optional[str], default: None) - BA result caching directory

**Example Request**:

```json
{
  "arkit_sequences_dir": "data/arkit_sequences",
  "epochs": 10,
  "lr": 1e-4,
  "use_amp": true,
  "use_ema": true,
  "use_onecycle": true,
  "cache_dir": "cache/ba_results",
  "compile_model": true
}
```

### 3. `/dataset/build` (Dataset Building)

**Request Model**: `BuildDatasetRequest`

**New Optimization Parameters**:

- `use_batched_inference` (bool, default: False) - Batch multiple sequences
- `inference_batch_size` (int, default: 4) - Batch size for inference
- `use_inference_cache` (bool, default: False) - Cache inference results
- `cache_dir` (Optional[str], default: None) - Inference cache directory
- `compile_model` (bool, default: True) - Torch.compile for inference

**Example Request**:

```json
{
  "sequences_dir": "data/sequences",
  "output_dir": "data/training",
  "use_batched_inference": true,
  "inference_batch_size": 4,
  "use_inference_cache": true,
  "cache_dir": "cache/inference",
  "compile_model": true
}
```

## 🔄 Data Flow

```
API Request (JSON)
    ↓
Request Model (Pydantic validation)
    ↓
Router Endpoint (training.py)
    ↓
CLI Function (cli.py) - passes through all params
    ↓
Service Function (fine_tune.py / pretrain.py / data_pipeline.py)
    ↓
Optimized Training/Inference
```

## 📝 Files Updated

1. **`ylff/models/api_models.py`**

   - Added optimization fields to `TrainRequest`
   - Added optimization fields to `PretrainRequest`
   - Added optimization fields to `BuildDatasetRequest`

2. **`ylff/routers/training.py`**

   - Updated `/train/start` to pass optimization params
   - Updated `/train/pretrain` to pass optimization params
   - Updated `/dataset/build` to pass optimization params

3. **`ylff/cli.py`**
   - Updated `train()` CLI function to accept optimization params
   - Updated `pretrain()` CLI function to accept optimization params
   - Updated `build_dataset()` CLI function to accept optimization params
   - All params are passed through to service functions

## 🎯 Usage Examples

### Fast Training via API

```bash
curl -X POST "http://localhost:8000/api/v1/train/start" \
  -H "Content-Type: application/json" \
  -d '{
    "training_data_dir": "data/training",
    "epochs": 10,
    "use_amp": true,
    "gradient_accumulation_steps": 4,
    "use_ema": true,
    "use_onecycle": true,
    "compile_model": true
  }'
```

### Optimized Dataset Building

```bash
curl -X POST "http://localhost:8000/api/v1/dataset/build" \
  -H "Content-Type: application/json" \
  -d '{
    "sequences_dir": "data/sequences",
    "use_batched_inference": true,
    "inference_batch_size": 4,
    "use_inference_cache": true,
    "cache_dir": "cache/inference"
  }'
```

## ✅ Status

All optimization parameters are:

- ✅ Defined in API request models
- ✅ Validated by Pydantic
- ✅ Passed through router endpoints
- ✅ Accepted by CLI functions
- ✅ Forwarded to service functions
- ✅ Documented with descriptions and examples

The API is fully wired up to use all optimization capabilities! 🚀