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

Data verified by PyPI on Jul 8, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.11.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for veryviolet from gravatar.com veryviolet

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

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

echelon3-0.7.0.tar.gz (120.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

echelon3-0.7.0-py3-none-any.whl (125.3 kB view details)

Uploaded Python 3

File details

Details for the file echelon3-0.7.0.tar.gz.

File metadata

  • Download URL: echelon3-0.7.0.tar.gz
  • Upload date:
  • Size: 120.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for echelon3-0.7.0.tar.gz
Algorithm Hash digest
SHA256 22cb578d986335d018d8d64cbd4f26908444501cb3739f1a0253325d9a78c234
MD5 64b8cf3e8c393194ecea8140fd267ef4
BLAKE2b-256 a1d5a76e90a5e994e29af181d23a6bffbc8ff7f44675d9ff5f9dffef2c2f179b

See more details on using hashes here.

Provenance

The following attestation bundles were made for echelon3-0.7.0.tar.gz:

Publisher: publish.yml on veryviolet/echelon3

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file echelon3-0.7.0-py3-none-any.whl.

File metadata

  • Download URL: echelon3-0.7.0-py3-none-any.whl
  • Upload date:
  • Size: 125.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for echelon3-0.7.0-py3-none-any.whl
Algorithm Hash digest
SHA256 22ca3cc52bdaed66b9b93f285eb5ce80d49dbc4796dc76872d157e42d0fc6657
MD5 664b130f4c1f740f857ab00694a8e605
BLAKE2b-256 df19bbf8a64559e28f6ca3adb04f1df2073c1b5bbe1320ad0daddce8fcbef99b

See more details on using hashes here.

Provenance

The following attestation bundles were made for echelon3-0.7.0-py3-none-any.whl:

Publisher: publish.yml on veryviolet/echelon3

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.11.0

2 files

0.10.9

2 files

0.10.8

2 files

0.10.7

2 files

0.10.6

2 files

0.10.5

2 files

0.10.4

2 files

0.10.3

2 files

0.10.2

2 files

0.10.1

2 files

0.10.0

2 files

0.9.6

2 files

0.9.5

2 files

0.9.4

2 files

0.9.3

2 files

0.9.2

2 files

0.9.1

2 files

0.9.0

2 files

0.8.7

2 files

0.8.6

2 files

0.8.5

2 files

0.8.4

2 files

0.8.3

2 files

0.8.2

2 files

0.8.1

2 files

0.8.0

2 files

0.7.12

2 files

0.7.11

2 files

0.7.10

2 files

0.7.9

2 files

0.7.8

2 files

0.7.7

2 files

0.7.6

2 files

0.7.5

2 files

0.7.4

2 files

0.7.3

2 files

0.7.2

2 files

0.7.1

2 files

This release

0.7.0 This release

2 files

0.6.0

2 files

0.5.2

2 files

0.5.1

2 files

0.5.0

2 files

0.4.1

2 files

0.4.0

2 files

0.3.1

2 files

0.3.0

2 files

0.2.0

2 files

0.1.0

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page