PyTorch Model Training Fundamentals

Apply PyTorch indexing, reshaping, broadcasting, and reduction operations while preserving model-required tensor shapes.

Control tensor dtypes and devices to prevent invalid or inefficient computations.

Compute, inspect, and reset gradients using PyTorch's automatic differentiation lifecycle.

Choose correctly among detach(), torch.no_grad(), and torch.inference_mode() for a given computation.

Diagnose a broken computation graph caused by detachment, in-place mutation, or non-differentiable operations.

Implement an nn.Module whose parameters and nested submodules are registered correctly.

Inspect, initialize, save, and load model parameters through a state_dict.

Implement a custom Dataset that returns consistently structured training examples.

Configure a DataLoader with appropriate batching, shuffling, collation, and worker settings.

Match model outputs, target encoding, and loss-function requirements for a classification task.

Implement one correct optimization step with gradient clearing, forward computation, loss calculation, backpropagation, and parameter updating.

Implement a validation pass with correct model mode and gradient settings.

Aggregate batch losses and metrics into unbiased epoch-level measurements.

Save and resume a checkpoint containing model, optimizer, scheduler, and training-state data.

Integrate a learning-rate scheduler into a training loop with the correct update timing.

Use a tiny-batch overfitting test to validate that a training pipeline can learn.

Diagnose tensor shape, dtype, device, and label-range failures from PyTorch error messages.

Inspect gradient norms and parameter updates to identify vanishing, exploding, or absent gradients.

Interpret training and validation curves to distinguish underfitting, overfitting, and optimization failure.

Run a controlled training experiment that isolates the effect of one hyperparameter or regularization change.

Move models and batches through a device-agnostic CPU/GPU training workflow.

Use automatic mixed precision with gradient scaling in a numerically safe training step.

Use gradient accumulation to train with an effective batch size larger than GPU memory permits.

Configure random seeds and deterministic settings, and document the remaining sources of nondeterminism.

Profile a training pipeline to determine whether its main bottleneck is data loading, host-to-device transfer, or model computation.

Convert raw text into reproducible token IDs using a fitted vocabulary with explicit unknown-token handling.

Build padded variable-length text batches and corresponding attention masks with a custom collation function.

Train and evaluate an embedding-based text classifier using the reusable PyTorch training workflow.

Implement and train a Transformer-encoder text classifier with positional information, padding masks, and sequence pooling.

Fine-tune a pretrained Hugging Face text-classification model using an explicit PyTorch training and validation loop.