At minimum, a training script needs to answer:
- What am I training?
- On what data?
- To minimize what objective?
- How are updates applied repeatedly?
Core Pieces
Model definition
A network or function with parameters to learn.
Data pipeline
Code to load, preprocess, batch, and usually shuffle training data.
Loss function
A scalar objective that tells the model how wrong it is.
Optimization step
An optimizer or update rule that changes parameters based on the loss.
Forward pass
Run inputs through the model to get predictions.
Backward pass / gradient computation
Compute how parameters affected the loss.
Training loop
Repeat over batches and epochs:
- get batch
- run model
- compute loss
- backprop
- update weights
- clear gradients
Configuration / hyperparameters
Things like learning rate, batch size, epochs, seed, and device.
Checkpointing
Save model state so training can resume or the best weights can be kept.
Logging / metrics
Track loss and usually validation metrics so you know whether training is working.
Commonly expected
These are not strictly universal, but most training scripts also include:
- validation loop
- device handling for CPU / GPU
- reproducibility setup like random seeds
- argument parsing or config files
- early stopping
- learning rate scheduling