echelon3

Config-driven PyTorch training framework. Every component of a training run — network, dataset, augmentations, losses, metrics, optimizer, scheduler, trainer, export — is described in a YAML config as a module / type / config triple and instantiated dynamically:

net:
  module: echelon3.nets.classifier   # import path (or a path to a .py file)
  type: ClassifierNet                # class or factory function in that module
  config: { ... }                    # constructor kwargs

There is no component registry: anything importable can be plugged in — classes from echelon3, from torch/timm/torchmetrics/albumentations, or from your own project code living next to your configs.

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

Install

pip install echelon3

Train

echelon3-train --config-dir ./configs --config-name my_experiment

CLIs: echelon3-train, echelon3-finetune (warm-start / freeze / head-only), echelon3-evaluate, echelon3-run (inference), echelon3-export (ONNX).

Multi-GPU — built in, no torchrun

Name the GPUs and echelon3 spawns one DDP worker per GPU itself:

echelon3-train --config-dir ./configs --config-name my_experiment gpus=[0,1,2,3]

gpus is a root config key — leave it out and echelon3 uses every visible GPU on the node. dataloaders.train.config.batch_size is the global batch size; it is split across ranks automatically. torchrun (and SLURM srun) still work unchanged for multi-node / elastic jobs.

DataParallel was removed in 0.5.0 — multiple GPUs always run as DDP.

Mixed precision

Training, evaluation and inference use bf16 automatic mixed precision by default on capable GPUs (fp32 on CPU / unsupported GPUs) — a large speedup on modern hardware. Force full fp32 with precision: fp32 under trainer.config (or precision: fp32 at the config root for evaluate / run).

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.

Project links

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  • Released:
Newer release available (0.11.0)

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Credits

Author: Kirill Groshenkov

GitHub Statistics

Data verified by PyPI on Jul 8, 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

Tags

computer-vision config-driven deep-learning hydra pytorch training

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