microsoft/nni

NNI, which stands for Neural Network Intelligence, is an open-source toolkit from Microsoft that automates several of the most time-consuming steps in building machine learning models. Building a good AI model normally requires a lot of manual experimentation: choosing the right settings, deciding on the model's internal structure, and trimming it down to run efficiently. NNI handles much of that experimentation automatically. The toolkit covers four main areas. Hyperparameter tuning is about finding the best configuration settings for a model. Every model has a set of adjustable dials, such as how fast it learns or how many layers it has, and NNI runs many trials with different combinations to find which settings produce the best results. Neural architecture search goes deeper, automatically exploring what the overall structure of a model should look like rather than just its settings. Model compression involves making an already-trained model smaller and faster without losing much accuracy, useful when deploying to devices with limited memory. Feature engineering refers to automatically identifying which pieces of raw input data are most useful for making predictions. NNI supports a wide range of popular machine learning frameworks, including PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, and MXNet. It can run experiments on a single machine or spread them across many machines or cloud environments at once. The toolkit includes more than a dozen different search algorithms for hyperparameter tuning alone, ranging from simple random search to more sophisticated approaches that build a statistical model of which settings work best and focus the search accordingly. Installation is done through pip, which is the standard Python package manager, with a single command. The project is written in Python and released under the MIT license, meaning it is free to use and modify for any purpose.