# Condensed: Faster Prediction with TensorRT

Summary: This tutorial demonstrates how to optimize AutoGluon models using TensorRT for faster inference while maintaining accuracy. It covers implementation techniques for model optimization using optimize_for_inference(), including proper setup with CUDA providers, batch size considerations, and precision settings (FP16). The tutorial helps with tasks like converting trained MultiModalPredictor models to TensorRT-optimized versions, validating optimization results, and handling post-optimization workflows. Key features include seamless TensorRT integration, performance validation methods, mixed precision support, and important warnings about model modification constraints and retraining procedures.

*This is a condensed version that preserves essential implementation details and context.*

Here's the condensed tutorial focusing on essential implementation details:

# Faster Prediction with TensorRT in AutoGluon

## Key Implementation Details

### Setup and Requirements
```python
# Required packages
!pip install autogluon.multimodal[tests]
!pip install -U "tensorrt>=10.0.0b0,<11.0"
```

### Training Configuration
```python
hyperparameters = {
    "optimization.max_epochs": 2,
    "model.names": ["numerical_mlp", "categorical_mlp", "timm_image", "hf_text", "fusion_mlp"],
    "model.timm_image.checkpoint_name": "mobilenetv3_small_100",
    "model.hf_text.checkpoint_name": "google/electra-small-discriminator",
}

predictor = MultiModalPredictor(label=label_col).fit(
    train_data=train_data,
    hyperparameters=hyperparameters,
    time_limit=120,
)
```

### TensorRT Optimization
```python
# Load and optimize model for inference
model_path = predictor.path
trt_predictor = MultiModalPredictor.load(path=model_path)
trt_predictor.optimize_for_inference()
```

## Critical Configurations

1. **Provider Options**:
```python
# Use CUDA execution provider for better precision
predictor.optimize_for_inference(providers=["CUDAExecutionProvider"])
```

2. **Batch Size Impact**:
- Affects inference speed
- Demonstrated with batch_size=2 in examples

## Important Warnings and Best Practices

1. **Model Modification Warning**:
- `optimize_for_inference()` modifies internal model for inference only
- Don't call `predictor.fit()` after optimization
- Reload model with `MultiModalPredictor.load` for retraining

2. **Precision Considerations**:
- Default uses FP16 mixed precision
- May have slight accuracy impact
- Use `assert_allclose` with appropriate tolerance:
```python
np.testing.assert_allclose(y_pred, y_pred_trt, atol=0.01)
```

3. **Performance Validation**:
- Always verify both speed improvement and accuracy maintenance
- Test with full dataset for accurate metrics
- Compare evaluation metrics between PyTorch and TensorRT versions

## Key Benefits

1. **Speed Improvements**:
- Significant inference speed increase
- Maintains similar accuracy levels
- Optimized for deployment environments

2. **Integration**:
- Seamless integration with existing AutoGluon workflows
- Drop-in replacement for standard prediction
- Compatible with existing model evaluation methods

This condensed version maintains all critical implementation details while removing unnecessary explanatory text and setup steps.