Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Ray | 27,782 | 80 | 298 | 18 hours ago | 87 | July 24, 2023 | 3,411 | apache-2.0 | Python | |
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads. | ||||||||||
Nni | 13,278 | 8 | 26 | 12 days ago | 54 | June 22, 2022 | 323 | mit | Python | |
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning. | ||||||||||
Flaml | 2,804 | 8 | 3 days ago | 88 | August 15, 2023 | 199 | mit | Jupyter Notebook | ||
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP. | ||||||||||
Lazypredict | 1,914 | 1 | 6 months ago | 12 | September 28, 2022 | 71 | mit | Python | ||
Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning | ||||||||||
Advisor | 1,202 | 4 years ago | 5 | October 04, 2018 | 19 | apache-2.0 | Jupyter Notebook | |||
Open-source implementation of Google Vizier for hyper parameters tuning | ||||||||||
Tune Sklearn | 449 | 7 | 3 months ago | 21 | April 22, 2022 | 29 | apache-2.0 | Python | ||
A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques. | ||||||||||
Adatune | 144 | 3 years ago | 1 | apache-2.0 | Python | |||||
Gradient based Hyperparameter Tuning library in PyTorch | ||||||||||
Autogbt Alt | 73 | 4 years ago | mit | Python | ||||||
An experimental Python package that reimplements AutoGBT using LightGBM and Optuna. | ||||||||||
Automl | 44 | 4 years ago | 2 | apache-2.0 | Scala | |||||
An automatic machine learning toolkit, including hyper-parameter tuning and feature engineering. | ||||||||||
Enas_nni | 25 | 5 years ago | 5 | Python | ||||||
This code is for running enas on nni. |
🔥 Heads-up: We're preparing to migrate AutoGen into a dedicated github repository. Alongside this move, we'll also launch a dedicated Discord server and a website for comprehensive documentation.
🔥 The automated multi-agent chat framework in AutoGen is in preview from v2.0.0.
🔥 FLAML is highlighted in OpenAI's cookbook.
🔥 autogen is released with support for ChatGPT and GPT-4, based on Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference.
🔥 FLAML supports Code-First AutoML & Tuning Private Preview in Microsoft Fabric Data Science.
FLAML is a lightweight Python library for efficient automation of machine learning and AI operations. It automates workflow based on large language models, machine learning models, etc. and optimizes their performance.
FLAML is powered by a series of research studies from Microsoft Research and collaborators such as Penn State University, Stevens Institute of Technology, University of Washington, and University of Waterloo.
FLAML has a .NET implementation in ML.NET, an open-source, cross-platform machine learning framework for .NET.
FLAML requires Python version >= 3.8. It can be installed from pip:
pip install flaml
Minimal dependencies are installed without extra options. You can install extra options based on the feature you need. For example, use the following to install the dependencies needed by the autogen
package.
pip install "flaml[autogen]"
Find more options in Installation.
Each of the notebook examples
may require a specific option to be installed.
from flaml import autogen
assistant = autogen.AssistantAgent("assistant")
user_proxy = autogen.UserProxyAgent("user_proxy")
user_proxy.initiate_chat(assistant, message="Show me the YTD gain of 10 largest technology companies as of today.")
# This initiates an automated chat between the two agents to solve the task
Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of openai.Completion
or openai.ChatCompletion
with powerful functionalites like tuning, caching, templating, filtering. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.
# perform tuning
config, analysis = autogen.Completion.tune(
data=tune_data,
metric="success",
mode="max",
eval_func=eval_func,
inference_budget=0.05,
optimization_budget=3,
num_samples=-1,
)
# perform inference for a test instance
response = autogen.Completion.create(context=test_instance, **config)
from flaml import AutoML
automl = AutoML()
automl.fit(X_train, y_train, task="classification")
automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])
from flaml import tune
tune.run(evaluation_function, config={}, low_cost_partial_config={}, time_budget_s=3600)
from flaml.default import LGBMRegressor
# Use LGBMRegressor in the same way as you use lightgbm.LGBMRegressor.
estimator = LGBMRegressor()
# The hyperparameters are automatically set according to the training data.
estimator.fit(X_train, y_train)
You can find a detailed documentation about FLAML here.
In addition, you can find:
ML.NET documentation and tutorials for Model Builder, ML.NET CLI, and AutoML API.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
If you are new to GitHub here is a detailed help source on getting involved with development on GitHub.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.