ML Training & Optimization

Prodmodel supports building, testing, deploying, and iterating on data science pipelines.

Together AI provides a cloud platform for AI inference, fine-tuning, and GPU clusters.

DeepSpeed optimizes distributed training and inference for deep learning models.

Deeplearning4j provides tools for training and deploying deep learning models in the JVM.

oumi supports fine-tuning, evaluation, and deployment of open-weight language models.

Guild AI automates and optimizes machine learning experiments while tracking each run.

The caret R package provides functions for classification and regression training.

Determined manages distributed model training, hyperparameter tuning, experiment tracking, and resources.

m2cgen converts machine learning models into native code for multiple programming languages without runtime dependencies.

DI-engine provides an OpenDILab framework for reinforcement learning projects.

EvalML builds, optimizes, and evaluates machine learning pipelines with automated data checks and objective functions.

KerasTuner searches hyperparameters to optimize machine learning models built with Keras.

ParaMonte provides parallel Monte Carlo and machine learning tools across several programming languages.

Leanpass provides a lightweight implementation for computing gradients.

KotlinDL supports deep learning model development through a high-level framework for Kotlin.

This Scikit-learn extension accelerates compatible applications through optimized implementations.

Optuna automates hyperparameter optimization for machine learning models.

LeRobot provides tools and learning resources for developing AI systems for robotics.

BayesO provides a Python package for Bayesian optimization algorithms and related modeling tasks.

Opytimizer provides Python implementations of metaheuristic optimization algorithms for search problems.

AutoKeras automates development of deep learning models through a machine learning library.

PEFT provides parameter-efficient fine-tuning methods for adapting pretrained machine learning models.

The somoclu project implements self-organizing maps in parallel across CPUs, GPUs, and clusters.

TF-Agents provides a TensorFlow library for contextual bandits and reinforcement learning.