nas-torch

Frugal memetic Neural Architecture Search combining an autoregressive Transformer controller (REINFORCE) with an Artificial Bee Colony (ABC) metaheuristic, designed to run on consumer-grade hardware.

Layout

File	Purpose
`layer_classes.py`	Dataclass configs for each supported layer type
`model.py`	`DynamicNet`: builds a runnable model from a list of layer configs
`optimizer.py`	Search strategies: SA, GA, ABC, RL (LSTM) and Transformer controllers
`cifar_common.py`	Shared seeding, GPU preloading and proxy evaluation for the CIFAR scripts
`Cifar_hybrid.py`	Transformer + ABC hybrid search on CIFAR-10
`Cifar100_hybrid.py`	Same hybrid search on CIFAR-100
`Cifar_SA.py`	Standalone Simulated Annealing baseline
`Cifar_abc.py`	Standalone ABC baseline
`random_search.py`	Budget-matched Random Search baseline
`benchmark_technique.py`	Multi-dataset benchmark (California Housing, Breast Cancer, Fashion-MNIST)
`test_bank_eva.py`	Hybrid search on the Credit Card Fraud dataset
`launcher.py`	Runs every experiment script sequentially

Requirements

pip install -r requirements.txt

Running

Individual experiments:

python Cifar_hybrid.py

All experiments in sequence:

python launcher.py

Results (accuracy, parameter count, search time, evaluation budget) are written as JSON under results/. The fraud script expects data/creditcard.csv; the image datasets are downloaded automatically to data/ on first run.

Metadata

Release files for nas-torch 0.1.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for nas-torch 0.1.1
File	Size	Uploaded
nas_torch-0.1.1.tar.gz	15.0 kB	Jul 14, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for nas-torch 0.1.1
File	Interpreter	ABI	Platform	Reset
nas_torch-0.1.1-py3-none-any.whl 14.8 kB Jul 14, 2026	Python 3	none	any	Details

Total release size: 29.8 kB

Release files / nas_torch-0.1.1.tar.gz

Download URL	nas_torch-0.1.1.tar.gz
Size	15.0 kB
Tags	Source
SHA-256 checksum How to use checksums	`ef5605b77d4cff4a080838646a1749f4a9c63fcb5759c778c62a6fa06bd9c86b`
BLAKE2b-256 checksum How to use checksums	`cff395fee8dc815c77f16b163292993e265a833ef51548cff6f19f352c8a3c15`
Upload date	Jul 14, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.13.9`

Release files / nas_torch-0.1.1-py3-none-any.whl

Download URL	nas_torch-0.1.1-py3-none-any.whl
Size	14.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`8e8cc534fc070e2027210e8dbc7aa124a687e3a646b81fa5bdc85133cefe7019`
BLAKE2b-256 checksum How to use checksums	`d639c78bca71c276d66e687a18d4226f6bc99bb87b8a6b870a65e71389e6aab7`
Upload date	Jul 14, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.13.9`