echelon3

Describe a PyTorch training run in YAML — network, data, augmentations, losses, metrics, optimizer, scheduler, trainer, export — and run it with one command.

No training-loop boilerplate and no component registry: every piece is a module / type / config triple that echelon3 imports and instantiates, so anything importable drops straight in — classes from echelon3, from torch / timm / torchmetrics / albumentations, or from your own project code sitting next to your configs.

# my_run.yaml — a whole training run
net:  { module: my_pkg.nets, type: MyNet, config: { channels: 32 } }
data:
  train: { module: my_pkg.data, type: MyDataset, config: { split: train } }
  test:  { module: my_pkg.data, type: MyDataset, config: { split: test } }
dataloaders:
  train: { module: torch.utils.data, type: DataLoader, config: { batch_size: 64, shuffle: true } }
  test:  { module: torch.utils.data, type: DataLoader, config: { batch_size: 128 } }
loss:
  - ce: { module: torch.nn, type: CrossEntropyLoss, config: {} }
optimizer: { module: torch.optim, type: AdamW, config: { lr: 3e-4 } }
trainer:   { module: echelon3.trainers.baseline, type: Trainer, config: { epochs: 50 } }
target:    { path: ./out }

echelon3 train -cd . -cn my_run     # trains, validates, keeps the best, checkpoints, resumes

What you get

One CLI, five tasks — echelon3 train | finetune | evaluate | run | export.
Multi-GPU with no torchrun — gpus=[0,1,2,3] spawns one DDP worker per GPU; batch_size is global and split across ranks automatically.
bf16 mixed precision by default on capable GPUs (precision: fp32 to opt out), plus an optional torch.compile knob and TF32.
Batteries included in the trainer — automatic resume, keep-best-checkpoint, validation schedule, TensorBoard / mlops logging, clean Ctrl-C.
Override anything on the CLI — trainer.config.epochs=100 optimizer.config.lr=1e-4, ~scheduler drops a section, +key=… adds one; typed values, list literals, ${oc.env:VAR,default} interpolation, and defaults: config composition (OmegaConf).
Your own code, no registration — reference nets / datasets / losses by import path and run from your repo root (the cwd is on sys.path).

Install

pip install echelon3
# extras: echelon3[export] (ONNX)   echelon3[detection]   echelon3[smp]

CLI & overrides

echelon3 train    -cd ./configs -cn my_run          # train (auto-resumes from target.path)
echelon3 finetune -cd ./configs -cn my_run          # + warm-start / freeze / head-only
echelon3 evaluate -cd ./configs -cn my_run          # score the latest checkpoint
echelon3 export   -cd ./configs -cn my_run          # preprocess→net→postprocess to ONNX
echelon3 run      -cd ./configs -cn my_run          # inference over images / video

# override any config value; +add, ~delete, typed values, multi-GPU:
echelon3 train -cd ./configs -cn my_run \
    trainer.config.epochs=100 optimizer.config.lr=5e-4 \
    dataloaders.train.config.batch_size=256 \
    gpus=[0,1] +trainer.config.compile=true ~scheduler

--config-dir/-cd picks the directory, --config-name/-cn the YAML.

Quick start

examples/ has self-contained smoke runs — a classifier, a CenterNet-style detector and semantic segmentation — each with a synthetic-data generator and a minimal config that trains, validates and checkpoints on CPU or GPU. For example:

cd examples/segmentation
python gen_seg_data.py --root ./seg_data
SEG_DATA=./seg_data echelon3 train -cd . -cn segmentation_smoke device=cpu

Use it from your AI coding agent

Marketplace-installable plugins teach Codex and Claude Code the echelon3 config format and CLI, so the agent writes correct configs and runs them for you — repo: https://github.com/veryviolet/echelon3-agent-skills.

# Codex
codex plugin marketplace add veryviolet/echelon3-agent-skills
codex plugin add echelon3@veryviolet

# Claude Code (run inside the REPL)
/plugin marketplace add veryviolet/echelon3-agent-skills
/plugin install echelon3@veryviolet

Full documentation

https://veryviolet.github.io/echelon3/

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