select-trainer

Development

Guides users through configuring the PINA Trainer for solver training, including batching strategy, data splitting, and Lightning options. Use when the user asks "how do I train", "how to train", "set up training", "trainer", "Trainer", "train my model", "fit", "training loop", or similar. Also triggers when the user mentions batching, training configuration, train/val/test split, or is confused about how to start training.

QUICK START

How to use this skill

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Source SKILL.md: https://github.com/mathLab/PINA/blob/HEAD/.opencode/skills/select-trainer/SKILL.md

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Configure Training for a PINA Solver

[!IMPORTANT] Read RULES.md before using this skill — it applies to all skills.

Use this skill to set up and run training once a solver has been chosen.

PINA's Trainer wraps Lightning's Trainer with PINA-specific defaults: data splitting across conditions, batching strategies, device placement for inverse parameters, and gradient tracking for physics-informed solvers.

Step 1 — Understand what the trainer needs

The trainer needs three things. If any are missing, ask the user:

Solver (required) — already chosen and instantiated.
Domain discretisation — all DomainEquationCondition domains must be sampled before the trainer can create dataloaders.
Training config — epochs, batch size, accelerator, device.

Domain discretisation

Physics-informed problems use domain=... in their conditions. The trainer requires every such domain to have been sampled:

problem.discretise_domain(n=1000, mode="random")

If you forget, the trainer raises a clear error listing which domains are missing.

Step 2 — Configure the trainer

The constructor has two groups of parameters: PINA-specific and Lightning **kwargs passed through to the parent class.

PINA-specific parameters

Parameter	Default	What it controls
`solver`	— (required)	The solver instance to train
`batch_size`	`None`	`None` = full batch; int = mini-batches
`train_size` / `val_size` / `test_size`	`1.0 / 0.0 / 0.0`	Fraction split per condition
`batching_mode`	`"common_batch_size"`	How batches are built across conditions (see below)
`automatic_batching`	`False`	`True` = Lightning's default collation; `False` = direct subset retrieval
`num_workers`	`0`	Dataloader workers
`pin_memory`	`False`	Pin memory for faster GPU transfer
`shuffle`	`True`	Shuffle before splitting

Batching modes

The mode controls how condition data is assembled into batches:

Mode	Behaviour	When to use
`"common_batch_size"`	Each condition supplies `batch_size` points per batch	Default — works for most cases
`"proportional"`	Batch sizes are scaled by condition dataset sizes	Unbalanced datasets (e.g., many interior points but few boundary points)
`"separate_conditions"`	Iterates through each condition separately	Each condition's data is heterogeneous (e.g., one is pointwise, another is a graph)

Common Lightning kwargs

These are passed as **kwargs and fully documented by PyTorch Lightning. Key ones for PINA users:

Kwarg	What it does
`max_epochs`	Number of training epochs
`accelerator`	`"cpu"`, `"gpu"`, `"mps"` (Apple Silicon)
`devices`	Device index or count (e.g., `1`, `[0]`, `"auto"`)
`precision`	`"16-mixed"`, `"32"`, `"64"`, `"bf16-mixed"`
`enable_progress_bar`	`True` / `False`
`gradient_clip_val`	Gradient clipping threshold
`callbacks`	List of Lightning callbacks (e.g., `ModelCheckpoint`, `EarlyStopping`)

Step 3 — Train and test

Usage is uniform regardless of solver type:

from pina import Trainer

trainer = Trainer(
    solver=solver,
    max_epochs=1000,
    batch_size=32,
    accelerator="cpu",
    train_size=0.8,
    val_size=0.1,
    test_size=0.1,
)

trainer.train()   # Lightning fit()
trainer.test()    # Lightning test() — optional

Templates

Full-batch physics-informed

problem.discretise_domain(n=5000, mode="random")

trainer = Trainer(
    solver=solver,
    max_epochs=10000,
    batch_size=None,          # full batch — no mini-batching
    accelerator="cpu",
)
trainer.train()

Mini-batch supervised

trainer = Trainer(
    solver=solver,
    max_epochs=500,
    batch_size=64,
    train_size=0.8,
    val_size=0.1,
    test_size=0.1,
    accelerator="gpu",
    devices=1,
    num_workers=4,
    shuffle=True,
)
trainer.train()
trainer.test()

Unbalanced conditions (proportional batching)

trainer = Trainer(
    solver=solver,
    batch_size=512,
    batching_mode="proportional",
    max_epochs=1000,
)

Checklist

Solver already chosen and instantiated (see select-solver skill)
All domains discretised: problem.discretise_domain(n=..., mode="...")
Batch size decided: None for full-batch, an int for mini-batches
Batching mode selected based on condition balance
Train/val/test split configured
Accelerator and device chosen
Training started: trainer.train()