echelon3 0.4.1

Config-driven PyTorch training framework: assemble nets, data, losses and trainers from YAML

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

Multi-GPU (DDP):

CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 \
    $(which echelon3-train) --config-dir ./configs --config-name my_experiment

dataloaders.train.config.batch_size is the global batch size; under DDP it is split across ranks automatically. Without torchrun the trainer falls back to DataParallel (device / device_ids config keys).

Quick start

See examples/ for a self-contained smoke run: synthetic dataset generation and a minimal classifier config.