Determined AI is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking and resource management for deep learning teams. Built by Determined AI (now part of HPE), it lets you scale PyTorch and TensorFlow jobs from a laptop to a GPU cluster without rewriting training code for each environment. The project has about 3,200 stars on GitHub, with development continuing into 2025.
The platform wraps your existing model code in a lightweight trial API, then takes over the painful infrastructure work: scheduling jobs across machines, checkpointing, fault tolerance, data and artifact management, and tracking every experiment’s metrics and hyperparameters in a web UI. Its hyperparameter search can run grid, random and adaptive searches in parallel across the cluster, automatically allocating more resources to promising configurations. Determined runs on Kubernetes or its own agent-based scheduler, on-prem or in the cloud, and integrates with tools like TensorBoard. Because checkpoints and environments are managed centrally, experiments are reproducible and models can be shared through a built-in model registry rather than scattered across individual workstations. A command-line tool and REST API expose the same operations for scripting and automation.
Determined’s core platform is free and open source under the Apache-2.0 license. HPE also offers Determined AI as a commercial enterprise product with support and additional capabilities for larger organizations.
Determined AI is for ML teams and research groups training deep learning models at scale who want cluster-grade training, tuning and tracking without building their own MLOps platform.