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PyTorch Deep Learning Model Development and Deployment

30 Chapters
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01
PyTorch ecosystem and deployment landscape
core libraries, TorchScript, ONNX, and production runtime options.
PyTorchecosystem
02
Tensor operations and autograd mechanics
building blocks for model development and gradient-based optimization.
operationsautograd
03
Dataset and DataLoader engineering
efficient input pipelines, transforms, samplers, and prefetching for training at scale.
DataLoaderefficient
04
Model architecture definition with nn.Module
layers, containers, parameter registration, and forward computation graphs.
architecturedefinition
05
Loss functions and optimizers
selecting, configuring, and customizing objectives and update rules for deep networks.
functionsoptimizers
06
Training loop implementation
epoch iteration, backpropagation, gradient clipping, and metric tracking.
Trainingimplementation
07
Validation and overfitting control
early stopping, regularization, dropout, and data augmentation strategies.
Validationoverfitting
08
Learning rate scheduling and hyperparameter tuning
schedulers, grid search, and Bayesian optimization with PyTorch.
schedulinghyperparameter
09
GPU acceleration and mixed precision training
CUDA semantics, AMP, gradient scaling, and memory optimization.
GPUacceleration
10
Distributed training with DistributedDataParallel
process groups, samplers, and multi-node synchronization.
Distributedtraining
11
Model checkpointing and experiment management
saving/loading state dicts, resuming training, and logging with TensorBoard.
checkpointingexperiment
12
Transfer learning and fine-tuning
leveraging pretrained models, freezing layers, and adapting to new domains.
Transferfine-tuning
13
Computer vision model development
CNN architectures, torchvision, and custom image classification pipelines.
Computerdevelopment
14
Natural language processing with PyTorch
embeddings, RNNs, Transformers, and torchtext integration.
languageprocessing
15
Sequence-to-sequence and attention mechanisms
encoder-decoder models, teacher forcing, and beam search.
Sequence-to-sequenceattention
16
Generative models in PyTorch
VAEs, GANs, and diffusion model training fundamentals.
GenerativePyTorch
17
Model interpretability and debugging
hooks, gradient inspection, saliency maps, and common training failure diagnosis.
interpretabilitydebugging
18
TorchScript and tracing
converting eager models to serializable, optimizable intermediate representations.
TorchScriptconverting
19
ONNX export and interoperability
exporting PyTorch models, validating graphs, and running with ONNX Runtime.
ONNXinteroperability
20
Model quantization and pruning
post-training quantization, quantization-aware training, and structured pruning.
quantizationpost-training
21
Deployment with TorchServe
packaging models, configuring handlers, and scaling inference endpoints.
DeploymentTorchServe
22
Serving models with FastAPI and TorchScript
building REST APIs, batching requests, and async inference.
FastAPITorchScript
23
Mobile and edge deployment
TorchScript Lite, optimizing for Android/iOS, and on-device inference constraints.
deploymentTorchScript
24
Containerization and orchestration
Dockerizing PyTorch models, Kubernetes deployment, and resource management.
Containerizationorchestration
25
Cloud deployment on AWS SageMaker
training jobs, model registry, and real-time inference endpoints.
deploymentAWS
26
Model monitoring and maintenance
drift detection, performance logging, and automated retraining pipelines.
monitoringmaintenance
27
CI/CD for PyTorch models
testing, versioning, and automated deployment workflows with MLflow and GitHub Actions.
CI/CDPyTorch
28
Performance profiling and optimization
PyTorch Profiler, bottleneck identification, and inference latency reduction.
Performanceprofiling
29
Security and privacy in deployed models
adversarial robustness, differential privacy, and secure inference.
Securitydeployed
30
End-to-end capstone
developing, training, optimizing, and deploying a PyTorch model to a production environment.
End-to-endcapstone