Libra/pyproject.toml at main · NetX-lab/Libra · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
[build-system]
requires = ["setuptools>=68", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "libra-agentic-rl"
version = "0.2.0"
description = "Efficient resource management for agentic RL post-training"
readme = "README.md"
requires-python = ">=3.10,<3.13"
license = {file = "LICENSE"}
authors = [
  {name = "Kaiwen Chen"},
  {name = "Xin Tan"},
  {name = "Jingzong Li"},
  {name = "Hong Xu"},
]
keywords = [
  "agentic reinforcement learning",
  "distributed training",
  "resource management",
  "vllm",
  "megatron-core",
]
classifiers = [
  "Development Status :: 3 - Alpha",
  "License :: OSI Approved :: MIT License",
  "Programming Language :: Python :: 3",
  "Programming Language :: Python :: 3.10",
  "Programming Language :: Python :: 3.11",
  "Programming Language :: Python :: 3.12",
  "Topic :: Scientific/Engineering :: Artificial Intelligence",
]
dynamic = ["dependencies"]

[project.urls]
Homepage = "https://github.com/NetX-lab/Libra"
Repository = "https://github.com/NetX-lab/Libra"
Paper = "https://arxiv.org/abs/2606.03077"

[tool.setuptools]
package-dir = {"RL_Framework" = "."}
packages = [
  "RL_Framework",
  "RL_Framework.engine",
  "RL_Framework.env",
  "RL_Framework.infra",
  "RL_Framework.infra.cost_model",
  "RL_Framework.infra.elastic",
  "RL_Framework.infra.execution",
  "RL_Framework.infra.observability",
  "RL_Framework.infra.scheduling",
  "RL_Framework.infra.sync",
  "RL_Framework.launcher",
  "RL_Framework.trainer",
  "RL_Framework.workflow",
]

[tool.setuptools.dynamic]
dependencies = {file = ["requirements.txt"]}

[tool.setuptools.package-data]
"RL_Framework.infra.elastic" = ["*.c"]