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### 4. TensorRT Optimization

# Convert ONNX to TensorRT
trt_logger = trt.Logger(trt.Logger.WARNING)
with trt.Builder(trt_logger) as builder:
    with builder.create_network(1) as network:
        with trt.OnnxParser(network, trt_logger) as parser:
            with open("model.onnx", "rb") as model:
                parser.parse(model.read())
            engine = builder.build_cuda_engine(network)

---

## 🔹 PyTorch Ecosystem
### 1. TorchVision

from torchvision.models import efficientnet_b0
from torchvision.ops import nms, roi_align

# Pretrained models
model = efficientnet_b0(pretrained=True)

# Computer vision ops
boxes = torch.tensor([[10, 20, 50, 60], [15, 25, 40, 70]])
scores = torch.tensor([0.9, 0.8])
keep = nms(boxes, scores, iou_threshold=0.5)

### 2. TorchText

from torchtext.data import Field, BucketIterator
from torchtext.datasets import IMDB

# Define fields
TEXT = Field(tokenize='spacy', lower=True, include_lengths=True)
LABEL = Field(sequential=False, dtype=torch.float)

# Load dataset
train_data, test_data = IMDB.splits(TEXT, LABEL)

# Build vocabulary
TEXT.build_vocab(train_data, max_size=25000)
LABEL.build_vocab(train_data)

### 3. TorchAudio

import torchaudio
import torchaudio.transforms as T

# Load audio
waveform, sample_rate = torchaudio.load('audio.wav')

# Spectrogram
spectrogram = T.Spectrogram()(waveform)

# MFCC
mfcc = T.MFCC(sample_rate=sample_rate)(waveform)

# Audio augmentation
augmented = T.TimeStretch()(waveform, n_freq=0.5)

---

## 🔹 Best Practices Summary
1. For GNNs: Normalize node features and use appropriate pooling
2. For Neural ODEs: Monitor ODE solver statistics during training
3. For Interpretability: Combine multiple explanation methods
4. For Deployment: Profile models before deployment (latency/throughput)
5. For Production: Implement monitoring for model drift

---

### 📌 Final Thoughts
Congratulations on completing this comprehensive PyTorch journey! You've learned:

✔️ Core PyTorch fundamentals
✔️ Deep neural networks & CNNs
✔️ Sequence modeling with RNNs/Transformers
✔️ Generative models & reinforcement learning
✔️ Advanced architectures & deployment

#PyTorch #DeepLearning #MachineLearning 🎓🚀

Final Practice Exercises:
1. Implement a GNN for molecular property prediction
2. Train a Neural ODE on irregularly-sampled time series
3. Deploy a model with TorchServe and create a monitoring dashboard
4. Compare SHAP and Integrated Gradients for your CNN model
5. Optimize a transformer model with TensorRT

# Molecular GNN starter
class MolecularGNN(nn.Module):
    def __init__(self, node_features, edge_features, hidden_dim):
        super().__init__()
        self.node_encoder = nn.Linear(node_features, hidden_dim)
        self.edge_encoder = nn.Linear(edge_features, hidden_dim)
        self.conv = tg.nn.MessagePassing(aggr='mean')
        
    def forward(self, data):
        x, edge_index, edge_attr = data.x, data.edge_index, data.edge_attr
        x = self.node_encoder(x)
        edge_attr = self.edge_encoder(edge_attr)
        return self.conv(x, edge_index, edge_attr)

❤5

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