BOOK A MEETING FR Tune is a Python library for experiment execution and hyperparameter tuning at any scale. Features Launch a multi-node distributed hyperparameter sweep in less than 10 lines of code. Supports any machine learning framework, including PyTorch, XGBoost, MXNet, and Keras. Automatically manages checkpoints and logging to TensorBoard. Choose among state of the …
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Talos
BOOK A MEETING FR Talos radically changes the ordinary Keras, TensorFlow (tf.keras), and PyTorch workflow by fully automating hyperparameter tuning and model evaluation. Talos exposes Keras and TensorFlow (tf.keras) and PyTorch functionality entirely and there is no new syntax or templates to learn. Features Single-line optimize-to-predict pipeline talos.Scan(x, y, model, params).predict(x_test, y_test) Automated hyperparameter optimization …
Scikit Optimize
BOOK A MEETING FR Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization. skopt aims to be accessible and easy to use in many contexts. The library is built on top of NumPy, SciPy and Scikit-Learn Features Scikit-Optimize, or …
Optuna
BOOK A MEETING FR Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters. Features Eager search …
Katib
BOOK A MEETING FR Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports Hyperparameter Tuning, Early Stopping and Neural Architecture Search. Katib is the project which is agnostic to machine learning (ML) frameworks. It can tune hyperparameters of applications written in any language of the users’ choice and natively supports many ML …
Hyperopt
BOOK A MEETING FR Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions. Features the objective function to minimize the space over which to search the database in which to store all the point evaluations of the search the search algorithm to …
Hypera ( I think it will be Hyperas)
BOOK A MEETING FR A very simple convenience wrapper around hyperopt for fast prototyping with keras models. Hyperas lets you use the power of hyperopt without having to learn the syntax of it. Instead, just define your keras model as you are used to, but use a simple template notation to define hyper-parameter ranges to …