AI Atlas › Trenowanie i dostrajanie Proces uczenia Training optimization · id optimization
Sterowanie przebiegiem aktualizacji wag.
Steering how the weights get updated.
In one sentence To sterowanie tym, jak i jak szybko trening zmienia ustawienia modelu.
This controls how training changes model parameters and how large those changes are.
Problem it solves Uczenie trwa zbyt długo, jest niestabilne albo zużywa więcej zasobów, niż możesz przeznaczyć.
Training takes too long, becomes unstable or consumes more resources than you can allocate.
What it does Dobiera regułę aktualizacji, wielkość kroków i format obliczeń, a pomiary błędu pomagają reagować na problemy.
It selects update rules, step sizes and numerical formats, while error measurements help respond to problems.
Everyday example Podczas treningu modelu zdjęć porównujesz ustawienia kroku, zużycie pamięci i przebieg błędu, zanim wybierzesz dłuższy trening.
While training an image model, you compare step settings, memory use and error trends before choosing a longer run.
This concerns you if you… kontrolujesz przebieg treningu chcesz ograniczyć czas, pamięć lub niestabilność uczenia control a training run want to reduce training time, memory or instability Examples optymalizatory (AdamW, Muon) · harmonogram learning rate · mieszana precyzja · stabilność treningu optimizers (AdamW, Muon) · learning-rate schedule · mixed precision · training stability Subtopics Harmonogram learning rate (rozgrzewka, cosinus, WSD) (Learning-rate schedules) : A plan for changing the learning rate during training. Common patterns include warm-up followed by decay; WSD adds a longer stable-rate phase before the final decay, making training runs easier to extend.Optymalizatory (SGD, Adam/AdamW, Muon) (Optimizers for neural network training) : Rules that turn a gradient into a weight update: from SGD with momentum, through adaptive Adam and AdamW, to newer matrix-aware optimisers such as Muon.Oszczędzanie pamięci podczas treningu (Memory-efficient training) : Techniques that shrink memory used by activations, gradients and optimiser states, so larger models train on the same hardware.Pakowanie sekwencji w treningu (sequence packing) (Sequence packing for training) : Concatenating several short examples into one context-length sequence to reduce padding in a batch.Przenoszenie hiperparametrów z małego modelu na duży (μP) (Maximal update parametrization (μP) and hyperparameter transfer) : A network parametrisation under which optimal hyperparameters are tuned on a small model and transferred unchanged to a large one.Rozmiar partii treningowej i akumulacja gradientu (Training batch size and gradient accumulation) : Choosing how many examples go into each update step: gradient accumulation over micro-batches and the critical batch size.Stabilność treningu i skoki straty (Training stability) : Diagnosing and mitigating instabilities such as sudden spikes or divergence in loss during long training runs of large models. The problems can be reproduced in smaller models to test safeguards in the training recipe.Trening w mieszanej precyzji (FP16, BF16, FP8) (Mixed-precision training) : Running most operations in shorter number formats while keeping key values in full precision. It cuts memory use and speeds up training on modern GPUs.Related topics Sources