Skip to main content

anima substrate-native consciousness engine — py CLI (measurement/eval · serialize · corpus · sweep), hexa-toolchain-free numpy distribution

Project description

anima-python

anima 의 py CLI 를 pip 로 배포하는 채널 — hexa 툴체인 불필요. 엔진 측정/직렬화/코퍼스 경로가 numpy 하나만으로 돈다 (pi5 등 hexa-less 호스트용).

단일진입 보존(a_cli_single_entry): anima-python 콘솔 명령 = cli/anima.py:main 디스패처의 pip 바인딩. hexa 채널 anima(hx install anima) 와 동일 디스패처, 설치 채널만 2개 — 2nd entry 아님. 이름을 anima-python 로 둔 이유 = hexa anima 와 PATH 충돌 회피.

설치

PyPI 발행 후 (권장):

pip install anima-python           # base — numpy 만 (evaluate · corpus · chat-stub)
pip install "anima-python[train]"  # +torch +datasets (serialize · train · sweep)

PyPI 발행 대기 중: 위 명령은 anima-python 가 PyPI 에 발행된 뒤 동작한다. 발행은 release.ymlpypi-publish job(OIDC trusted-publishing)이 v* 태그에서 자동 수행하며, 오너의 1회 설정이 선결(① pypi.org 에 anima-python pending-publisher 등록: Owner=dancinlab·Repo=anima·Workflow=release.yml·Env=pypi ② repo 변수 PYPI_PUBLISH=true). 그 전까지는 아래 소스 설치를 쓴다.

소스에서 바로 설치 (PyPI 없이 지금 가능):

pip install "git+https://github.com/dancinlab/anima.git"            # base
pip install "anima-python[train] @ git+https://github.com/dancinlab/anima.git"  # +torch
# 또는 레포 클론 후: pip install .   /   pip install ".[train]"

명령 매트릭스

동사 티어 torch 없이 동작 비고
anima-python evaluate <clm> [--corpus …] [--gen N] base ✅ numpy py 2-production 측정 = ρ·AXON reach / 구 G0-G6. terminal-eligible (a_eval_py_canonical)
anima-python corpus <derivtrace|flat> --out F … base ✅ 순수 stdlib 절차적 학습-코퍼스 생성 (data-format 레버)
anima-python chat <clm> base ✅ (stub) 의식 A⇄G 루프는 hexa-native → hexa 진입 포인터 출력
anima-python serialize <pt> <clm> [train] ❌ torch .pt unpickle 에 torch 필요 (+ held-out DESCENT 게이트)
anima-python sweep --arms … --objectives … [train] 셀마다 train.py spawn → torch 필요
anima-python train <args> [train] ❌ torch+datasets production Lane-P 학습

verdict 규율

anima-python evaluate <clm> = py 2-production numpy 측정 경로 — hexa det-eval 과 동일 frozen bars·byte-parity 라 terminal 자격 동일(a_eval_py_canonical, 2nd-class 미러 아님). 큰 ckpt(303M+)는 mini 금지 · pool(summer/aiden)에서 측정.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

anima_python-0.13.1.tar.gz (275.8 kB view details)

Uploaded Source

Built Distribution

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

anima_python-0.13.1-py3-none-any.whl (259.5 kB view details)

Uploaded Python 3

File details

Details for the file anima_python-0.13.1.tar.gz.

File metadata

  • Download URL: anima_python-0.13.1.tar.gz
  • Upload date:
  • Size: 275.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.6

File hashes

Hashes for anima_python-0.13.1.tar.gz
Algorithm Hash digest
SHA256 ef6b672a1e8f19fc2fa4b1f98314d5b7323a063f3dbfa2a62b50b84ce05d1243
MD5 e46cf9dbbaeeef0004c12681bcbe2887
BLAKE2b-256 3714943f77ad1a2dbb8003b7ef0aafd71849409e991fd45c5f200e39ee94621c

See more details on using hashes here.

File details

Details for the file anima_python-0.13.1-py3-none-any.whl.

File metadata

  • Download URL: anima_python-0.13.1-py3-none-any.whl
  • Upload date:
  • Size: 259.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.6

File hashes

Hashes for anima_python-0.13.1-py3-none-any.whl
Algorithm Hash digest
SHA256 3d111ee07fe47d74135d6a24733539596f8fe587aa35913a38d2525d4a8a0359
MD5 5a959cf6a196b9550f63cad39b5bdcac
BLAKE2b-256 7263199049e0e40117ff040134dfc8362ecdfd1beae74dfc538c8e5f69ebc318

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page