20. Model Export & Production Serialization: TorchScript (JIT Tracing), ONNX & TensorRT Compilation • Deep Learning & PyTorch Neural Networks Architecture • Rishabh Ki Kaksha

Deep Learning & PyTorch Neural Networks Architecture › 20. Model Export & Production Serialization: TorchScript (JIT Tracing), ONNX & TensorRT Compilation

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Course Syllabus

Module 1: PyTorch Tensors, Autograd & Computational Graphs

01. PyTorch Tensor Architecture: Strides, Storage Buffers, Memory Layout (Contiguous vs Non-Contiguous) 02. Dynamic Computational Graphs & The PyTorch Autograd Engine (grad_fn, requires_grad, backward) 03. GPU Acceleration with CUDA: Tensor Device Placement (to("cuda")), CUDA Streams & Pinned Memory

Module 2: Multi-Layer Perceptrons (MLPs) & Custom Training Loops

04. Building Custom nn.Module Classes, Custom Parameters & Activation Functions (ReLU, GELU, Swish) 05. Custom Datasets & High-Performance DataLoaders (num_workers, collate_fn, pin_memory) 06. The Enterprise Training Loop: Forward Pass, Loss Computation, Zero Grad, Backward Pass & Optimizer Step 07. Neural Regularization Techniques: Dropout, Batch Normalization (BatchNorm1d) & Layer Normalization (LayerNorm)

Module 3: Convolutional Neural Networks (CNNs) & Computer Vision Architecture

08. Convolutional Operations: Kernels, Filters, Strides, Padding & Feature Map Dilation 09. Modern CNN Architectures: Residual Connections (ResNet), Squeeze-and-Excitation & ConvNeXt 10. Transfer Learning & Vision Fine-Tuning with Torchvision Pretrained Models

Module 4: Recurrent Architectures: RNNs, LSTMs & GRUs for Sequential Data

11. Sequential Modeling: Vanilla RNNs & The Vanishing / Exploding Gradient Problem 12. Long Short-Term Memory (LSTM) Networks: Forget Gate, Input Gate, Candidate State & Output Gate Math 13. Gated Recurrent Units (GRUs) & Bidirectional Sequence Processing in PyTorch

Module 5: Modern Transformer Architecture from Scratch in PyTorch

14. Scaled Dot-Product Attention: Query, Key, Value Matrices & Attention Softmax Calculation 15. Multi-Head Attention (MHA) & Causal Masking Implementation from Scratch in Pure PyTorch 16. Building a Complete GPT-Style Decoder-Only Transformer Language Model from Scratch

Module 6: PyTorch Distributed (DDP), Mixed Precision (AMP) & Model Checkpointing

17. Automatic Mixed Precision (torch.cuda.amp.autocast & GradScaler) for 2x Faster GPU Training 18. Distributed Data Parallel (DDP) Architecture: Multi-GPU Synchronization with torch.distributed 19. Robust Checkpointing: Saving State Dicts, Resuming Training & Early Stopping Callbacks 20. Model Export & Production Serialization: TorchScript (JIT Tracing), ONNX & TensorRT Compilation

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# 20. Model Export & Production Serialization: TorchScript (JIT Tracing), ONNX & TensorRT Compilation

> Deep Learning & PyTorch Notes by Rishabh - Topic 20

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### 1. The Production Serving Deployment Stack 🚀

Python runtime production servers me slow hota hai (GIL lock, heavy memory).  
**Deployment Pipeline:**
$$\text{PyTorch (Python)} \longrightarrow \text{ONNX (Open Neural Network Exchange)} \longrightarrow \text{TensorRT / Triton (C++ GPU Inference)}$$

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### 2. Exporting Model to ONNX

```python
import torch

model.eval()
dummy_input = torch.randn(1, 3, 224, 224).cuda()

torch.onnx.export(
    model,
    dummy_input,
    "model_production.onnx",
    export_params=True,
    opset_version=17,
    input_names=['input_image'],
    output_names=['class_logits'],
    dynamic_axes={'input_image': {0: 'batch_size'}, 'class_logits': {0: 'batch_size'}}
)
```

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### 3. Master Comparison Matrix

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### 4. Interview Ke 3 Sawal Is Topic Se

1. **JIT Tracing (`torch.jit.trace`) aur JIT Scripting (`torch.jit.script`) me kya difference hai?** -> Tracing dummy input pass karke specific execution path record karti hai (Dynamic if-else statements drop ho sakti hain); Scripting AST code parse karke control flow preserve karti hai.
2. **NVIDIA TensorRT model latency 5x kaise fast karta hai?** -> Layer fusion (Conv+BatchNorm+ReLU into 1 kernel), Precision quantization (INT8/FP16), aur GPU specific hardware architecture memory tuning karke.
3. **Triton Inference Server dynamic batching kaise karta hai?** -> Alag-alag clients se aane wali individual microsecond requests ko GPU par single batch me combine karke parallel execute karta hai.

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**Course Completed:** Deep Learning & PyTorch Neural Networks Architecture (All 20 Lessons Completed!) 🎉🚀🧠🔥⚡💎

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