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name: nn-training-workflows description: "Author and train Jittor models with layers, losses, optimizers, schedulers, and state handling." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

nn-training-workflows

Use this sub-skill for Jittor model authoring, scratch training loops, optimizer and scheduler setup, parameter/state handling, gradient accumulation, and train/eval behavior.

Start here

• Read references/nn-api-reference.md for the layer, loss, optimizer, scheduler, and init signatures that matter for training.
• Read references/training-recipes.md for the bounded quadratic-regression recipe, PyTorch-to-Jittor porting map, accumulation order, and save/load pattern.
• Read references/troubleshooting.md when training fails, loss shapes mismatch, parameters are missing, or state does not reload.
• Run scripts/training_smoke.py to verify a tiny CPU training loop; it is CPU-safe and network-free by default. Use --help first, then --steps and --assert-loss-drop for a bounded check.

Use when

• You need to define a model by subclassing Module and implementing execute.
• You need nn.Linear, nn.Conv, nn.BatchNorm, nn.Dropout, nn.Sequential, or recurrent layers for a training graph.
• You need nn.SGD, nn.Adam, nn.AdamW, nn.RMSprop, nn.Adan, Optimizer.step, backward, zero_grad, gradient clipping, or a learning-rate scheduler.
• You need state_dict, load_state_dict, save, load, or checkpoint conversion between Jittor and PyTorch-style state bundles.
• You need a bounded recipe for accumulation, clipping, or mode switching.

Do not use for

• Raw Var or autograd fundamentals.
• Dataset, dataloader, model-zoo, or checkpoint-download workflows.
• Backend flags, compiler setup, or runtime/performance tuning.

Operating rules

• Implement execute, not forward.
• Call model.train() before training and model.eval() before validation or inference.
• Use optimizer.step(loss) for the simple case, or optimizer.backward(loss) plus optimizer.step() when you need accumulation.
• Keep non-trainable state in buffers, not in hidden Python containers.
• Treat state_dict shape and name mismatches as a load-time issue to inspect, not something to ignore silently.

Fast path

1. Confirm the model exposes parameters.
2. Pick the loss whose shape contract matches the target.
3. Choose an optimizer and, if needed, a scheduler.
4. Verify with scripts/training_smoke.py.
5. If the loop is unstable, open the troubleshooting reference before changing the architecture.