pyproject.toml

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "soup-cli"
version = "0.73.0"
description = "Fine-tune and post-train LLMs in one command. No SSH, no config hell."
readme = "README.md"
license = "Apache-2.0"
# Upper-bounded on purpose (#358). CI tests 3.10 / 3.11 / 3.12 and nothing above.
# Without a ceiling, pip on 3.13+ resolves torch wheels nobody here has run, and
# the failure is not a Soup error message — it is a loader crash inside c10.dll /
# libc10.so before any Soup code executes, so the user has nothing to act on.
# `tests/test_requires_python_bound.py` derives this bound from the CI matrix, so
# widening one without the other fails the suite.
requires-python = ">=3.10,<3.13"
authors = [
    { name = "Soup Team", email = "team@trysoup.dev" },
]
keywords = ["llm", "fine-tuning", "lora", "qlora", "machine-learning"]
classifiers = [
    "Development Status :: 3 - Alpha",
    "Intended Audience :: Developers",
    "Intended Audience :: Science/Research",
    "License :: OSI Approved :: Apache Software License",
    "Programming Language :: Python :: 3",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
]
# Core install (`pip install soup-cli`) is intentionally light: the CLI, config
# system, and data tools — no PyTorch. v0.71.0 split the heavy training stack
# (torch / transformers / peft / trl / datasets / bitsandbytes / accelerate)
# into the `[train]` extra below.
dependencies = [
    "typer>=0.9.0,<0.21.0",
    "rich>=13.0.0",
    "pydantic>=2.0.0",
    "pyyaml>=6.0",
    "huggingface-hub>=0.16.0",
    "plotext>=5.2.0",
]

[project.optional-dependencies]
# v0.71.0 — heavy training stack. `pip install 'soup-cli[train]'` to fine-tune.
# These were core dependencies through v0.70.0; pins are unchanged.
train = [
    "torch>=2.0.0",
    "transformers>=4.36.0,<5.0.0",
    "peft>=0.7.0",
    # Capped below 0.29. #326 raised this from `<0.27` after migrating the six
    # preference trainers (bco, dpo, ipo, kto, orpo, simpo) off the two trl APIs
    # that moved. The cap is no longer set by those trainers at all — see the
    # 0.29 note at the bottom, which is about `online_dpo`.
    #
    # trl removed `max_prompt_length` from the preference configs in STAGES
    # rather than in one release — which is why a single spot-check gives the
    # wrong answer. This table is now verified the only way that settles it, by
    # CONSTRUCTING each config with the exact kwargs the wrappers pass:
    #
    #   version          dpo   kto   orpo   cpo   bco
    #   0.14.0 - 0.26.2  yes   yes   yes    yes   yes
    #   0.27.0 - 0.27.2  yes   NO    yes    yes   yes
    #   0.28.0           yes   NO    NO     NO    NO
    #   0.29.0 - 1.9.2   NO    NO    NO     NO    NO
    #
    # There is NO successor field: `max_length` survives on every version and
    # absorbs prompt truncation, so the migration is to stop passing the keyword
    # to a config that no longer has it, not to rename it. The trainers now ask
    # the config class whether it accepts the keyword (trainer/_trl_compat.py)
    # instead of comparing versions, because a version table is exactly what was
    # wrong twice before. On every trl that still has the field the behaviour is
    # byte-identical — measured: the six train to identical losses on 0.26.2,
    # 0.28.0 and 1.9.2 on the same fixture.
    #
    # Separately, trl 0.29.0 dropped ORPOConfig / CPOConfig / BCOConfig (and
    # their trainers) from the public `trl` namespace. They were not deleted —
    # they live on under `trl.experimental.<algo>` — so those three trainers now
    # fall back to that path when the public import is gone.
    #
    # WHY THE CAP IS 0.29 AND NOT HIGHER: `soup train --task online_dpo` is
    # broken on 0.29.x, and the cause is not in these trainers. trl 0.29 moved
    # `BasePairwiseJudge` to `trl.experimental.judges` while still accepting
    # `judge=`, so the wrapper correctly selects the judge path and then
    # `eval/judge.py::_base_pairwise_judge_cls` cannot import the base class.
    # trl 1.x drops `judge=` entirely, so the wrapper falls back to
    # `reward_funcs=` and online_dpo works again — measured: all nine tasks build
    # on 1.9.2, and the six preference tasks train there. Raising the cap past
    # 0.29 therefore needs (a) a `trl.experimental.judges` fallback in
    # eval/judge.py and (b) validation of the trl surfaces this bump did not
    # exercise (serve, GRPO variants, PPO with a real reward model). Shipping
    # against a release nobody has validated is not a substitute for that.
    #
    # The underlying bug was latent, not new: the trl imports live inside
    # `setup()`, which no test had ever called on those wrappers, so CI stayed
    # green while anyone pip-installing got a broken install. v0.72.4's
    # end-to-end preference tests are what surfaced it.
    #
    # The floor is 0.14.0, not 0.7.0: `setup()` imports GRPOTrainer
    # unconditionally and trl first exports it at 0.14.0 (OnlineDPO / KTO / BCO /
    # BasePairwiseJudge arrive at 0.11.0; trl 0.7.0 has none of them). Resolvers
    # normally pick the newest allowed version, so the impossible floor only bit
    # under a constraints file or anyone reading the metadata as a support claim.
    "trl>=0.14.0,<0.29",
    "datasets>=2.14.0",
    "bitsandbytes>=0.41.0",
    "accelerate>=0.25.0",
]
# v0.71.0 — convenience meta-extra pulling the main optional stacks.
all = ["soup-cli[train,serve,ui,data,mcp]"]
eval = ["lm-eval>=0.4.0"]
data = ["datasketch>=1.6.0"]
wandb = ["wandb>=0.15.0,<0.18.0"]
# Self-references `[train]` so CI / contributors get the full training stack
# (CI runs `pip install -e ".[dev]"`; without this every test would fail at
# `import torch`).
dev = ["soup-cli[train,mcp]", "cryptography>=41.0.0", "reportlab>=4.0.0", "pytest>=7.0", "ruff>=0.1.0", "pytest-cov>=4.0", "httpx>=0.24.0", "mypy>=1.8.0", "pre-commit>=3.5.0"]
ui = ["fastapi>=0.104.0", "uvicorn>=0.24.0"]
serve = ["fastapi>=0.104.0", "uvicorn>=0.24.0"]
serve-fast = ["vllm>=0.4.0", "fastapi>=0.104.0", "uvicorn>=0.24.0"]
generate = ["httpx>=0.24.0"]
deepspeed = ["deepspeed>=0.12.0"]
fast = ["unsloth>=2024.8"]
vision = ["Pillow>=9.0.0"]
qat = ["torchao>=0.4.0"]
liger = ["liger-kernel>=0.3.0"]
ring-attn = ["ring-flash-attn>=0.1.0"]
onnx = ["optimum[onnxruntime]>=1.16.0"]
tensorrt = ["tensorrt_llm>=0.9.0"]
audio = ["librosa>=0.10.0", "soundfile>=0.12.0"]
awq = ["autoawq>=0.2.0"]
gptq = ["auto-gptq>=0.7.0"]
sglang = ["sglang>=0.2.0", "fastapi>=0.104.0", "uvicorn>=0.24.0"]
mlx = ["mlx>=0.20.0", "mlx-lm>=0.20.0"]
cce = ["cut-cross-entropy>=24.10.0"]
tui = ["textual>=0.50.0"]
# v0.53.8 #89 — bundle MLflow / SwanLab / Trackio for `--tracker` users.
trackers = ["mlflow>=2.0.0", "swanlab>=0.3.0", "trackio>=0.0.1"]
# v0.53.8 #85 — fsspec backends for remote dataset loading (s3 / gs / az / oci).
remote = ["fsspec>=2024.1.0", "s3fs>=2024.1.0", "gcsfs>=2024.1.0", "adlfs>=2024.1.0"]
# v0.53.10 #150 — bundle scikit-optimize so `soup data mix --optimize` runs the
# Bayesian-style loop instead of falling back to the v0.48.0 Dirichlet sampler.
mix = ["scikit-optimize>=0.9.0"]
# v0.53.10 #113 — production-grade data quality: langdetect (language) +
# presidio-analyzer (PII). Llama-Guard-3-1B is documented as a manual recipe
# (license + ~600 MB weight blob too large to bundle by default).
data-pro = ["langdetect>=1.0.9", "presidio-analyzer>=2.2.0"]
# v0.71.2 #179/#185 — ed25519 detached signing for `soup attest` / `soup
# adapters sign`. Pure-offline; Sigstore keyless (OIDC + Fulcio/Rekor network)
# stays infra-blocked and is NOT bundled here.
sign = ["cryptography>=41.0.0"]
# v0.71.3 #181 — reportlab PDF rendering for `soup train --annex-xi *.pdf`.
pdf = ["reportlab>=4.0.0"]
# v0.71.3 #180 — codecarbon offline energy/CO2 measurement for
# `soup train --track-energy` (offline; no IP-geolocation network call).
carbon = ["codecarbon>=2.0.0"]
# v0.71.13 #225/#227 — prompt-program / tool-schema compilers
# (`soup compile` / `soup compile-tools`). Lazy-imported with a friendly
# ImportError; not installed by default (heavy dependency trees).
compile = ["dspy-ai>=2.5.0", "textgrad>=0.1.0", "gepa>=0.0.1"]
# v0.71.18 #16 — serverless cloud GPU training (`soup train --cloud modal`).
# Lazy-imported; only needed for `--cloud-submit` (plan-only render needs no
# dependency). Modal auth is via `modal setup`.
modal = ["modal>=0.60.0"]
# v0.71.28 - `soup mcp serve` MCP server. The official `mcp` python SDK is
# lazy-imported (only src/soup_cli/mcp_server/server.py touches it), so the CLI
# stays light without it. Floor pinned to guard against SDK API churn.
#
# v0.72.3: capped below 2.0. The floor alone was not enough — mcp 2.0.0 removed
# `mcp.shared.memory.create_connected_server_and_client_session` and dropped
# `Server.list_tools`, which broke every `soup mcp serve` round-trip test on all
# nine CI jobs the day it was published. 1.29.0 (the newest 1.x) still exposes
# both, verified directly. Migrating to the 2.x API is its own piece of work,
# and shipping against a major nobody has validated is not a substitute for it.
mcp = ["mcp>=1.2.0,<2"]

[project.scripts]
soup = "soup_cli.cli:run"

[project.urls]
Homepage = "https://github.com/MakazhanAlpamys/Soup"
Repository = "https://github.com/MakazhanAlpamys/Soup"
Issues = "https://github.com/MakazhanAlpamys/Soup/issues"
Discord = "https://discord.gg/8RgVbFA6Zq"

[tool.hatch.build.targets.wheel]
packages = ["src/soup_cli"]
# v0.53.8 #93 — include bundled fixture JSONLs as package data so
# `soup data demo` works in zipapp / namespace-package installs.
# Hatchling's ``packages = ["src/soup_cli"]`` already recurses into the
# package directory, so we use the artifacts directive (NOT
# force-include, which double-shipped the files in v0.53.8 and produced
# a "duplicate filename in local headers" 400 from PyPI upload).
artifacts = [
  "src/soup_cli/data/_fixtures/*.jsonl",
  "src/soup_cli/data/_fixtures/unlearning/*.jsonl",
  "src/soup_cli/data/_fixtures/behavior/*.jsonl",
  "src/soup_cli/data/_fixtures/gate/*.jsonl",
]

[tool.ruff]
target-version = "py310"
line-length = 100

[tool.ruff.lint]
select = ["E", "F", "I", "N", "W"]

[tool.mypy]
# Intentionally lenient: the codebase leans on torch / transformers / peft / trl,
# none of which ship complete stubs. This config surfaces type issues in our own
# code without drowning in third-party `Any`s. The CI `type-check` job is
# non-blocking; tighten incrementally as modules gain annotations.
python_version = "3.10"
files = ["src/soup_cli"]
ignore_missing_imports = true
follow_imports = "silent"

[tool.pytest.ini_options]
testpaths = ["tests"]
markers = [
    "smoke: slow smoke tests that download models and run training (run with: pytest -m smoke)",
    "unit: fast isolated tests — no subprocess, network, filesystem, or real model load",
    "integration: tests that touch real subprocess, SQLite, filesystem, or HTTP",
]
addopts = "-m 'not smoke' --cov=soup_cli --cov-fail-under=77 --cov-report=term-missing:skip-covered"