Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.

Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.

Pre-training a Transformer from scratch.

Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.

Enabling DRfC functionalities via Jupyter Notebook as AWS SageMaker does to empower DeepRacer Model Training

Regression problem

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Propulate is an asynchronous evolutionary optimization algorithm and software package for global optimization and hyperparameter search on high-performance computers.

Empower your real estate decisions with our data-driven model, delivering precise rental predictions for landlords and comprehensive insights for tenants in a dynamic market landscape.

A web app lets users listen to songs based on real-time emotion detection through a mobile camera or web camera.

Hyperparameter optimization package of the mlr3 ecosystem

Additional stoppers for ray tune

A convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!

Testing ray tune with slurm batch submission and optuna and wandb

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

Implement, train, tune, and evaluate a transformer model for antibody classification with this step-by-step code.

Bayesian optimization using Gaussian Process regression (Python)

Automated Machine Learning on Kubernetes