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# 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! 🚀
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