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

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

Project links

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  • Released:
Newer release available (0.11.0)

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Avatar for veryviolet from gravatar.com veryviolet

Credits

Author: Kirill Groshenkov

GitHub Statistics

Data verified by PyPI on Jul 11, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.10

Provides Extra

detection docs export sam smp test

Tags

computer-vision config-driven deep-learning pytorch training

Classifiers

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