autogen/setup.py
Chi Wang 5387a0a607
Agent notebook example with human feedback; Support shell command and multiple code blocks; Improve the system message for assistant agent; Improve utility functions for config lists; reuse docker image (#1056)
* add agent notebook and documentation

* fix bug

* set flush to True when printing msg in agent

* add a math problem in agent notebook

* remove

* header

* improve notebook doc

* notebook update

* improve notebook example

* improve doc

* agent notebook example with user feedback

* log

* log

* improve notebook doc

* improve print

* doc

* human_input_mode

* human_input_mode str

* indent

* indent

* Update flaml/autogen/agent/user_proxy_agent.py

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* shell command and multiple code blocks

* Update notebook/autogen_agent.ipynb

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update notebook/autogen_agent.ipynb

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* Update notebook/autogen_agent.ipynb

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

* coding agent

* math notebook

* renaming and doc format

* typo

* infer lang

* sh

* docker

* docker

* reset consecutive autoreply counter

* fix explanation

* paper talk

* human feedback

* web info

* rename test

* config list explanation

* link to blogpost

* installation

* homepage features

* features

* features

* rename agent

* remove notebook

* notebook test

* docker command

* notebook update

* lang -> cmd

* notebook

* make it work for gpt-3.5

* return full log

* quote

* docker

* docker

* docker

* docker

* docker

* docker image list

* notebook

* notebook

* use_docker

* use_docker

* use_docker

* doc

* agent

* doc

* abs path

* pandas

* docker

* reuse docker image

* context window

* news

* print format

* pyspark version in py3.8

* pyspark in py3.8

* pyspark and ray

* quote

* pyspark

* pyspark

* pyspark

---------

Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
2023-06-09 18:40:04 +00:00

146 lines
4.0 KiB
Python

import setuptools
import os
here = os.path.abspath(os.path.dirname(__file__))
with open("README.md", "r", encoding="UTF-8") as fh:
long_description = fh.read()
# Get the code version
version = {}
with open(os.path.join(here, "flaml/version.py")) as fp:
exec(fp.read(), version)
__version__ = version["__version__"]
install_requires = [
"NumPy>=1.17.0rc1",
]
setuptools.setup(
name="FLAML",
version=__version__,
author="Microsoft Corporation",
author_email="hpo@microsoft.com",
description="A fast library for automated machine learning and tuning",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/microsoft/FLAML",
packages=setuptools.find_packages(include=["flaml*"]),
package_data={
"flaml.default": ["*/*.json"],
},
include_package_data=True,
install_requires=install_requires,
extras_require={
"automl": [
"lightgbm>=2.3.1",
"xgboost>=0.90",
"scipy>=1.4.1",
"pandas>=1.1.4",
"scikit-learn>=0.24",
],
"notebook": [
"jupyter",
],
"spark": [
"pyspark>=3.2.0",
"joblibspark>=0.5.0",
],
"test": [
"lightgbm>=2.3.1",
"xgboost>=0.90",
"scipy>=1.4.1",
"pandas>=1.1.4",
"scikit-learn>=0.24",
"thop",
"pytest>=6.1.1",
"coverage>=5.3",
"pre-commit",
"torch",
"torchvision",
"catboost>=0.26,<1.2",
"rgf-python",
"optuna==2.8.0",
"openml",
"statsmodels>=0.12.2",
"psutil==5.8.0",
"dataclasses",
"transformers[torch]==4.26",
"datasets",
"nltk",
"rouge_score",
"hcrystalball==0.1.10",
"seqeval",
"pytorch-forecasting>=0.9.0,<=0.10.1",
"mlflow",
"pyspark>=3.2.0",
"joblibspark>=0.5.0",
"nbconvert",
"nbformat",
"ipykernel",
"pytorch-lightning<1.9.1", # test_forecast_panel
"requests<2.29.0", # https://github.com/docker/docker-py/issues/3113
"packaging",
],
"catboost": ["catboost>=0.26"],
"blendsearch": ["optuna==2.8.0"],
"ray": [
"ray[tune]~=1.13",
],
"azureml": [
"azureml-mlflow",
],
"nni": [
"nni",
],
"vw": [
"vowpalwabbit>=8.10.0, <9.0.0",
"scikit-learn",
],
"hf": [
"transformers[torch]==4.26",
"datasets",
"nltk",
"rouge_score",
"seqeval",
],
"nlp": [ # for backward compatibility; hf is the new option name
"transformers[torch]==4.26",
"datasets",
"nltk",
"rouge_score",
"seqeval",
],
"ts_forecast": [
"holidays<0.14", # to prevent installation error for prophet
"prophet>=1.0.1",
"statsmodels>=0.12.2",
"hcrystalball==0.1.10",
],
"forecast": [
"holidays<0.14", # to prevent installation error for prophet
"prophet>=1.0.1",
"statsmodels>=0.12.2",
"hcrystalball==0.1.10",
"pytorch-forecasting>=0.9.0",
],
"benchmark": ["catboost>=0.26", "psutil==5.8.0", "xgboost==1.3.3"],
"openai": ["openai==0.27.4", "diskcache"],
"autogen": ["openai==0.27.4", "diskcache", "docker"],
"synapse": [
"joblibspark>=0.5.0",
"optuna==2.8.0",
"pyspark>=3.2.0",
],
"autozero": ["scikit-learn", "pandas", "packaging"],
},
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires=">=3.6",
)