determined-ai/determined

Health v2 · maintenance only

Adoption not part of the score

3236 stars · 373 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Determined is an open-source deep learning platform that combines distributed training, hyperparameter tuning, experiment tracking, and GPU resource management in one system. It works with PyTorch and TensorFlow via a Python library, CLI, and Web UI, and deploys on-prem, on AWS/GCP, Kubernetes, or Slurm.

Use cases

• run distributed deep learning training on multiple GPUs
• tune hyperparameters automatically for PyTorch or TensorFlow models
• track and compare ML experiments with reproducibility
• manage GPU clusters and cut cloud training costs
• deploy a training cluster on AWS, GCP, or Kubernetes
• visualize loss curves and hyperparameter search results in a web UI

When to choose

• you need an all-in-one platform for training, tuning, and tracking deep learning experiments
• you want to scale PyTorch/TensorFlow training across multiple GPUs or nodes
• you need efficient GPU scheduling and resource management for a team
• you want reproducible experiment configs via YAML

When to avoid

• you only need lightweight experiment tracking without cluster management
• your workflow is centered on a single GPU with no distributed training
• you prefer assembling your own stack from separate tools like MLflow and Ray

Facets

application · maturity active

machine-learning llm-training gpu-computing scheduling monitoring cli developer-tools machine-learning deep-learning data-science cloud-computing gpu-computing developer-tools python go cloud self-hosted cli cross-platform mlops hyperparameter-tuning distributed-training experiment-tracking pytorch tensorflow resource-management ml-platform kubernetes docker web-server

Member repositories

For agents

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