A Fast Library for Automated Machine Learning & Tuning
FLAML is a lightweight Python library for efficient automation of machine learning and AI operations, including selection of models, hyperparameters, and other tunable choices of an application (e.g., inference hyperparameters for foundation models, configurations in MLOps/LMOps workflows, pipelines, mathematical/statistical models, algorithms, computing experiments, software configurations).
FLAML has a .NET implementation in ML.NET, an open-source, cross-platform machine learning framework for .NET. In ML.NET, you can use FLAML via low-code solutions like Model Builder Visual Studio extension and the cross-platform ML.NET CLI. Alternatively, you can use the ML.NET AutoML API for a code-first experience.
FLAML requires Python version >= 3.7. 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.
Use the following guides to get started with FLAML in .NET:
(New) The autogen package can help you maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4, including:
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. ```python from flaml import oaiWith three lines of code, you can start using this economical and fast AutoML engine as a scikit-learn style estimator.
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 where you can find the API documentation, use cases and examples.
In addition, you can find:
Research and blogposts around FLAML.
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 opencode@microsoft.com with any additional questions or comments.
Source: https://github.com
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