Abstract:

Below is your Special Annexure 5: PyTorch Best Practices & Industry Checklist — crafted to be professional, comprehensive, and suitable for inclusion in a technical textbook.
It contains best practices followed by AI engineers, researchers, and industry professionals to build reliable, efficient, and production-ready PyTorch models.

Special Annexure 5: PyTorch Best Practices & Industry Checklist

Guidelines for Efficient, Scalable, and Production-Ready Deep Learning Systems

Part A: PyTorch Best Practices (Training & Development)

1. Use GPU/TPU Efficiently

Always check for device availability:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
Move only necessary tensors to GPU.
For multi-GPU tasks, prefer Distributed Data Parallel (DDP) over DataParallel.

2. Prefer DataLoader with num_workers

Use efficient data loading:
- num_workers = 2–8 depending on CPU
- pin_memory=True for GPU training
Avoid expensive operations inside __getitem__.

3. Use Mixed Precision Training

Use AMP (torch.cuda.amp) to:
- Reduce memory usage
- Speed up training
Required for:
- CNNs
- Transformers
- Large models

4. Use Checkpointing

Save:

model_state_dict
optimizer_state_dict
epoch
scheduler_state_dict

Enables complete recovery after interruption.

5. Use torch.no_grad() for Inference

Prevents gradient tracking during evaluation:

with torch.no_grad():
    pred = model(x)

Saves memory + improves inference speed.

6. Always Set Manual Seeds

Ensures reproducibility:

torch.manual_seed(42)
np.random.seed(42)
random.seed(42)

7. Use Standard Training Loop Template

Always include:

Zero gradients
Forward pass
Loss calculation
Backward
Step optimizer
Scheduler step

Consistency ensures fewer bugs.

8. Avoid In-Place Operations When Uncertain

Operations like:

x.relu_()

may interfere with autograd.
Use in-place operations only when certain of no gradient issues.

9. Move Computation to GPU, Not Data to CPU

Avoid unnecessary transfers:

Do preprocessing on CPU
Do forward + backward on GPU

10. Monitor GPU Memory

Use:

nvidia-smi
torch.cuda.memory_summary()
Gradient accumulation for large batches

Part B: PyTorch Best Practices (Model Design)

1. Modularize the Model

Split model into:

Encoder
Decoder
Head
Loss function
Utilities

Makes debugging and reuse easier.

2. Use Pretrained Models

Prefer pretrained weights for:

CV (ImageNet models)
NLP (BERT, RoBERTa)
Audio (Wav2Vec)

Benefits:

Faster training
Better accuracy
Smaller datasets

3. Always Use Batch Normalization or Layer Normalization

Improves:

Convergence
Stability
Generalization

4. Avoid Very Deep Networks Without Residuals

Residual connections prevent:

Vanishing gradients
Training instability

5. Prefer ReLU6, GELU, or SiLU in Modern Architectures

Modern activations:

Improve gradient flow
Increase accuracy
Reduce loss oscillation

Part C: PyTorch Best Practices (Debugging & Monitoring)

1. Use Gradient Checking

Check exploding gradients with:

torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=2.0)

2. Print Model Summary

Use:

from torchsummary import summary
summary(model, input_size=(3, 224, 224))

3. Use TensorBoard

For:

Loss tracking
Accuracy curves
Histograms
Graph visualization

4. Debug with hooks

Attach hooks to layers to debug:

Gradients
Outputs
Weights

5. Use torch.autograd.set_detect_anomaly(True)

Helps locate autograd errors by printing stack traces.

Part D: PyTorch Best Practices (Performance Optimization)

1. Use Efficient Data Formats

Use torchvision transforms for images
Use LMDB for large datasets
Use torchaudio’s fast signal transforms

2. Fuse Operations

Use TorchScript or JIT to:

Fuse convolution + batchnorm
Optimize activation sequences

3. Use DDP for Speed

DistributedDataParallel advantages:

Faster than DataParallel
Less overhead
Better scalability

4. Enable cudnn.benchmark

Improves speed for fixed input sizes:

torch.backends.cudnn.benchmark = True

5. Profile Your Model

Use:

torch.utils.bottleneck
PyTorch Profiler
Identifies slow layers and I/O bottlenecks.

Part E: PyTorch Best Practices (Production Deployment)

1. Use TorchScript or ONNX

Benefits:

Platform independence
Hardware optimization
Mobile deployment (Android/iOS)

2. Wrap Model in FastAPI/Flask

Serve model predictions using:

REST API
JSON inputs
Batch inference

3. Use Model Quantization

Quantization types:

Dynamic
Static
Quantization-aware training

Reduces:

Model size
Latency
Power consumption

4. Use A/B Testing for Model Updates

Compare:

Old model
New model

Measure:

Accuracy
Response time
User engagement

5. Logging & Monitoring

Use:

MLflow
Weights & Biases
TensorBoard

Track:

Metrics
Model versions
Deployment logs

Part F: Industry Checklist (Ready-to-Use)

✔ Development Phase Checklist

Set random seeds
Prepare train/val/test split
Implement DataLoader
Define model architecture
Add normalization layers

✔ Training Phase Checklist

AMP mixed precision enabled
Proper optimizer selected
Learning rate scheduler applied
Gradient clipping if required
Checkpointing implemented
TensorBoard monitoring active

✔ Evaluation Phase Checklist

Use torch.no_grad()
Calculate correct metrics
Compare against baseline
Perform error analysis

✔ Production Phase Checklist

Convert to TorchScript or ONNX
Run latency and throughput tests
Implement logging and monitoring
Create FastAPI/Flask server
Setup automated CI/CD
Ensure model rollback version