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_sizeis global and split across ranks automatically. - bf16 mixed precision by default on capable GPUs (
precision: fp32to opt out), plus an optionaltorch.compileknob 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,~schedulerdrops a section,+key=…adds one; typed values, list literals,${oc.env:VAR,default}interpolation, anddefaults: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
1 maintainer
Metadata
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Release files for echelon3 0.14.0
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