curry-train

Run a short, reproducible benchmark of one optimizer step (forward + backward + optimizer step over N microbatches) using the project's registered runtime. Activate when the user asks to "benchmark a training step", "measure throughput", "time one optimizer step", or "smoke test the runtime". Wraps run_accumulated_step from curry_train.benchmark.

Diagnose a training failure or stall by inspecting recent logs, loss curves, OOM traces, NaN events, and config. Activate when the user asks "why did my training crash", "loss went to NaN", "OOM during step X", "training is not improving", or "help me debug this run". Delegates to the failure-diagnoser agent.

Lightning Fabric integration recipe — minimal 5-line setup that gives DDP / FSDP / mixed precision / mixed-precision while keeping a raw PyTorch training loop. Activate when the user asks "Lightning Fabric", "torchrun", "DDP setup", "FSDP setup", "mixed precision", or wires up the launch script.

Bootstrap a new curryTrain project by copying the framework Python template into the user's working directory. Use when the user runs /curry-train:init, asks to "start a new training project with curryTrain", "initialize curryTrain", "scaffold a curryTrain project", or wants the framework code copied locally for editing.

Scaffold a new model + dataset + config triple inside an existing curryTrain project. Activate when the user asks to "add a new model to curryTrain", "scaffold a new experiment", "generate model.py / dataset.py / config.yaml for X", or describes an idea they want to start training. Delegates the actual generation to the scaffolder agent.

Compare two training runs and produce a concise markdown diff covering config, key metrics, loss curves, and grad-norm trajectory. Activate when the user asks to "compare run A and run B", "diff two experiments", "did this change actually help", or "is this run better than the previous one". This is both the implementation of the action and the methodology guide for variance-aware comparison.

Define an abort condition before launching a run, so that a clearly broken or clearly under-performing run stops itself instead of consuming the full compute budget. Activate when the user asks "when should I kill a run", "abort condition", "early stop a bad run", "kill criterion", or before any expensive run.

Decide which parallelism primitive (DP, ZeRO, TP, PP, EP, CP) to introduce next based on what bottleneck appears at the current model size. Activate when the user asks "do I need tensor parallelism", "OOM at scale", "training too slow", "should I add pipeline parallel", "how to scale beyond N GPUs", or after capacity-sweep when single-GPU runs no longer fit.

Maintain a structured per-run journal capturing seed, config diff, git SHA, full training curves, kill events, rollbacks, and resumes — so that any run is fully reproducible and comparable later. Activate when the user asks "experiment tracking", "reproducibility", "run journal", "what should I record", or shows runs without traceable metadata.

Hydra + OmegaConf configuration layout for curryTrain projects — composable defaults, structured configs, CLI override syntax, sweep integration. Activate when the user asks "Hydra setup", "config management", "compose configs", "override CLI", "Hydra defaults list", or builds the experiment configuration.

Concrete recipe for running an Optuna-driven hyperparameter sweep through Hydra, with TPE/CMA-ES/Hyperband, distributed multi-rank trials, study persistence, and per-trial run journal. Activate when the user asks "set up an Optuna sweep", "run hyperparameter search", "Hydra Optuna sweeper", or "parallel HPO".

A Logger protocol decoupling the training code from any specific tracking backend (W&B, MLflow, Aim, TensorBoard) — with TensorBoard as the zero-dependency default. Activate when the user asks "experiment tracking", "W&B integration", "TensorBoard setup", "MLflow", "switch tracking backend", or wants tracking without lock-in.

Sharding the sequence dimension across ranks (Ring Attention) for very long contexts that don't fit attention memory on a single GPU. Activate when the user asks "context parallel", "CP", "Ring Attention", "long context training", "32k+ sequence", or runs out of memory on attention rather than parameters.

Distributed checkpoint format that survives changes in world size and parallelism topology. Built on torch.distributed.checkpoint. Activate when the user asks "DCP", "distributed checkpoint", "resume on different topology", "FSDP checkpoint", or "save sharded model".

Optimizer state sharding across DP ranks (ZeRO-1, ZeRO-2, ZeRO-3 / FSDP). Reduces per-rank memory by sharding gradient and/or parameter copies. Activate when the user asks "ZeRO", "FSDP", "optimizer sharding", "distributed optimizer", or "OOM in optimizer".

A bank of parallel expert MLPs that consume routed tokens from TopKRouter and return per-token outputs. The "doer" half of an MoE block. Activate when the user asks "MoE experts", "expert MLPs", "Mixtral", "expert parallel", or builds an MoE block.

Grouped-query attention with rotary positional embedding (RoPE). Standard component in modern LLMs (Llama-2/3, Qwen2/3, Mistral). Activate when the user asks "GQA", "grouped query attention", "RoPE", "rotary embedding", "attention with KV groups", or builds a transformer.

Compose multiple microbatch forward/backward passes into a single optimizer step, enabling effective batch sizes larger than memory permits. Activate when the user asks "gradient accumulation", "accumulate gradients", "effective batch size", "OOM at larger batch", or asks how to set the GA factor.

Bidirectional weight conversion between HuggingFace transformers format and curryTrain internal format, including offline fallback when the HF Hub is unreachable. Activate when the user asks "load HF weights", "HuggingFace bridge", "convert weights", "HF Hub unreachable", or "offline weight loading".

Leaky Integrate-and-Fire spiking neuron with surrogate gradient — converts continuous activations into binary spike trains over T timesteps. Used by spiking transformer architectures (CSLA-MT). Activate when the user asks "LIF neuron", "spiking neural network", "SNN", "spike encoding", "surrogate gradient", or wires up a spiking layer.

Centralized state tracking the multi-dimensional parallelism topology — DP rank, TP rank, PP rank, EP rank, CP rank — and the communication groups for each. Activate when the user asks "parallel state", "process group", "rank topology", "world setup", or wires up multi-dim parallelism.

Pipeline-parallel schedules (1F1B, interleaved 1F1B, GPipe). Manages microbatches flowing through stages on different ranks. Activate when the user asks "pipeline parallel", "PP", "1F1B", "GPipe", "interleaved pipeline", or has more layers than fit on a single node.

Activation checkpointing — recompute forward activations during backward instead of storing them, trading compute for memory. Activate when the user asks "activation checkpointing", "recompute", "OOM during backward", "gradient checkpointing", or needs to fit a larger model into memory.

Root Mean Square LayerNorm — drop the mean-subtraction from LayerNorm, keep only the RMS-based scaling. Used by Llama, Qwen, and most modern LLMs. Activate when the user asks "RMSNorm", "Llama norm", "RMS layer norm", "skip mean centering", or compares RMSNorm vs LayerNorm.

Top-K token routing for Mixture-of-Experts — for each token, pick the K experts with highest gating score. Activate when the user asks "MoE routing", "top-k router", "switch transformer routing", "expert choice", or builds an MoE model.

Tensor-parallel linear layers — column-parallel and row-parallel — for splitting matmuls across GPUs along the output or input feature dimension. Activate when the user asks "tensor parallel", "column parallel linear", "row parallel linear", "TP", or "split matmul across GPUs".

Methodology for building a leakage-safe data pipeline — split before preprocess, fit transforms on train only, time-aware splits for temporal data, deterministic shuffle. Activate when the user asks "how do I split my data", "data pipeline best practice", "is my normalizer leaking", "how to set up a dataset for curryTrain", or shows a pipeline that fits a transform on the full dataset.

A canonical set of low-cost assertions to run before any non-trivial training, catching the most common "silent" bugs (zero_grad missed, train/eval mode wrong, wrong tensor shape, label leakage in transforms). Activate when the user asks "what should I check before training", "preflight checks", "is my training set up correctly", or any time a fresh model is about to be trained.

Methodology for Stage 1 Skeleton — set up the minimum architecture (model, dataset adapter, config, registration) so the data-flow can be traced end-to-end before any optimization. Activate when the user asks "how do I add a new model", "what files does a curryTrain model need", "set up the architecture skeleton", "where does my model.py go", or "how does registration work".

Per-layer histograms of gradient magnitudes and activation statistics — used to detect dead layers, exploding gradients, or pathological depth scaling early. Activate when the user asks "is my gradient flow healthy", "are any layers dead", "exploding gradients", "vanishing gradients", or "what's a coord check" (related to muP).

Verify the loss at step 0 matches the value implied by a uniform-random model — typically -log(1/C) for C-way cross-entropy. Catches initialization bugs, double-softmax, missing bias init, and hidden activation issues. Activate when the user asks "what should my initial loss be", "init loss seems wrong", "is my model initialized correctly", or right after building a new model.

The canonical sanity check — train on 2-3 examples until the loss is near zero, proving the entire forward/backward/optimizer pipeline can fit. Activate when the user asks "how do I sanity check my model", "overfit one batch", "is my pipeline working", "loss is not decreasing", or before any real training of a fresh architecture.

Estimate the compute and dollar cost of a proposed training run before launching it, and compare against the expected gain from the small-scale ablation. Activate when the user asks "how much will this cost", "is this run worth the compute", "compute budget estimator", "how long will this take", or considers launching a multi-day run.

Find a near-optimal learning rate by sweeping LR exponentially over a few hundred mini-batches and watching where the loss starts to diverge — the Leslie Smith "LR range test". Activate when the user asks "what learning rate should I use", "lr finder", "lr range test", "calibrate the learning rate before training", or after Stage 2 sanity checks pass and they're ready to commit compute.

Estimate trial-to-trial variance by running the same configuration with multiple random seeds and checking whether claimed improvements exceed that variance. Activate when the user asks "is my improvement real or noise", "how many seeds do I need", "multi-seed variance check", "statistical significance for ML", or after any A/B comparison that ran with only one seed each.

Verify that activation statistics and gradient magnitudes are width-invariant under muP parameterization, so that hyperparameters tuned at small width transfer zero-shot to large width. Activate when the user asks "muP", "muTransfer", "tune small predict big", "coord check", "width-invariant init", or before any large-scale training where they want to skip per-width hyperparameter tuning.

Fit a power-law scaling curve to small-scale runs at multiple sizes, then extrapolate to predict large-scale loss before committing the compute. Activate when the user asks "scaling laws", "Chinchilla", "Kaplan", "predict large-scale loss from small-scale", "is my idea going to scale", or wants to do compute-optimal training.

Run a tiny, cheap A/B comparison between a baseline and a new idea on a small model and short training budget — to predict whether the idea is worth a full-scale run. Activate when the user asks "should I scale this up", "is this idea worth running for real", "test this idea cheaply first", "small-scale ablation", or "validate before scaling".

Construct a tiny synthetic task that *requires* the new feature to solve, run the model on it, and use success/failure as a structural signal of whether the feature is doing what it claims. Activate when the user asks "how do I test if my new mechanism actually works", "surrogate task", "synthetic benchmark", "probe task", or claims a new architecture component "helps with X" without quantitative evidence.

Sweep model capacity (width, depth, parameter count) at fixed compute to find the saturation point — where adding more parameters stops reducing the train loss. Activate when the user asks "how big should my model be", "capacity sweep", "is my model big enough", "find the right model size", or after Stage 3 pre-validation passes.