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A simple OpenAI Gym environment for Neural Architecture Search (NAS)
The main features of gomerudo/nas-env are: Automated Machine Learning.
Projects with overlapping indexed features include: autogluon/autogluon — AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end… karpathy/autoresearch — Autoresearch is an autonomous machine learning research agent and architecture search framework. It employs a… ludwig-ai/ludwig — Ludwig is a multimodal machine learning platform and low-code framework designed for building, training, and deploying… microsoft/ai-edu — ai-edu is a comprehensive AI education curriculum and machine learning courseware collection. It provides theoretical… bayesian-optimization/bayesianoptimization — This is a Bayesian optimization library for Python designed to find the maximum value of expensive black box… automl/hpbandster — a distributed Hyperband implementation on Steroids.
Autoresearch is an autonomous machine learning research agent and architecture search framework. It employs a closed-loop system to programmatically rewrite training and architecture source code to discover optimal language model configurations. The system iteratively modifies code and evaluates performance metrics to improve model quality based on a target objective. It optimizes model performance and training efficiency by tracking validation bits per byte, which allows for a fair comparison of architectural changes independently of vocabulary size. The framework manages the full training
AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc
Ludwig is a multimodal machine learning platform and low-code framework designed for building, training, and deploying neural networks. It enables the construction of models that process text, images, audio, and tabular data through a unified interface using declarative configuration files rather than custom code. The system features a specialized low-code framework for large language models, supporting supervised fine-tuning, preference alignment, and a constrained decoding tool to force structured data output via logit extraction. It also includes an automated model architecture search to i
This is a Bayesian optimization library for Python designed to find the maximum value of expensive black box functions. It operates as a global optimizer that uses probabilistic models to identify the peak value of unknown functions through iterative sampling. The tool is specifically designed for hyperparameter tuning in machine learning, where it maximizes model performance while minimizing the number of required training runs. It treats the target function as a black box, selecting optimal input parameters based on statistical priors to reduce manual trial and error. The system utilizes G