AI & ML7 min read

Deep Learning with PyTorch

Build neural networks with PyTorch - tensors, custom datasets, training loops, transfer learning with pretrained models, and GPU acceleration.

PyTorchNeural NetworksDeep LearningGPU TrainingTransfer Learning

skill: Deep Learning with PyTorch category: AI & ML tags: [PyTorch, Neural Networks, Deep Learning, GPU Training, Transfer Learning] readingTime: 7

PyTorch is the de facto standard for deep learning research and production. Its define-by-run (eager execution) model makes debugging natural - you can print tensors, set breakpoints, and use standard Python tools. For most practical image/text tasks, fine-tuning a pretrained model outperforms training from scratch by a wide margin.

Workflow

Understand tensors and autogradPyTorch tensors are multi-dimensional arrays with GPU support. Autograd automatically computes gradients for any tensor computation - the foundation of neural network training.
Build a neural network with nn.Modulenn.Module is the base class for all PyTorch models. It tracks parameters, enables GPU movement, and integrates with optimizers and checkpoint saving.
Create custom Datasets and DataLoadersDataLoader handles batching, shuffling, and parallel data loading. Custom Dataset wraps any data source and applies per-sample transforms.
Write the training loopPyTorch training loops are explicit - zero gradients, forward pass, compute loss, backward pass, update weights. Explicit means debuggable and customizable.
Fine-tune pretrained modelsImageNet pretrained ResNet/EfficientNet features transfer to almost any image task. Fine-tuning requires orders of magnitude less data than training from scratch.

Output Format

PyTorch: device = cuda/cpu, nn.Module with Sequential, custom Dataset + DataLoader with transforms, explicit 5-step training loop, and ResNet50 fine-tuning with layer-specific LRs.

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Model(nn.Module): def forward(self, x): return self.net(x)
DataLoader(dataset, batch_size=32, shuffle=True, num_workers=4)
zero_grad → forward → loss → backward → clip_grad → step
model.fc = nn.Linear(in_features, num_classes)

Examples

Input

Output

Edge Cases & Gotchas

GPU memory OOM - Reduce batch size, use gradient accumulation (accumulate N batches before step), or use torch.cuda.amp for mixed precision (FP16 on GPU). torch.cuda.empty_cache() clears cached memory but doesn't solve fundamental OOM.
Reproducibility - Set seeds for reproducibility: torch.manual_seed(42); np.random.seed(42); torch.backends.cudnn.deterministic = True. Note: deterministic mode is slower.
DataLoader num_workers on Windows - num_workers > 0 on Windows requires if __name__ == "__main__": guard. On macOS with MPS, start with num_workers=0 and increase if stable.
Gradient accumulation - To simulate larger batch size: loss = loss / accumulation_steps; loss.backward(). Only call optimizer.step() and zero_grad() every accumulation_steps iterations.

When to Break These Rules

Use Keras/TensorFlow for production ML pipelines requiring TFX, TF Serving, or TF Lite - the TF ecosystem for production deployment is more mature than PyTorch serving.
Use Hugging Face Transformers (which wraps PyTorch) instead of raw PyTorch for NLP tasks - the pretrained model ecosystem is far richer than building from scratch.

Reference Files

https://pytorch.org/tutorials/
https://pytorch.org/docs/stable/