Determined AI

Determined AI: Distributed Deep Learning and Hyperparameter Tuning Platform is an open-source platform designed to accelerate deep learning research and production by enabling distributed model training, scalable hyperparameter optimization, and comprehensive experiment tracking. It abstracts away infrastructure complexity, allowing teams to train at scale without changing their model code, while providing robust resource management, fault tolerance, and reproducibility.

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Overview

• Provides distributed training without requiring code changes. Automatically provisions machines, handles networking, data loading, and fault tolerance.
• Built-in scalable hyperparameter tuning with state-of-the-art search algorithms and visualizations to explore results efficiently.
• End-to-end experiment tracking and artifact management to reproduce results and collaborate effectively.
• Resource management and cluster scheduling that supports on-premises and cloud GPUs, including seamless spot instance support.
• Model registry for deploying trained models and sharing them across teams.
• Compatibility with leading DL frameworks (PyTorch, TensorFlow, Keras) and various data storage systems; easy export to downstream serving systems.
• Real-time experiment dashboard and advanced checkpointing to maximize productivity and minimize downtime.
• Focus on researchers and engineers: reduces time spent on infrastructure tasks, enabling rapid experimentation and iteration.

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How It Works

1. Install and configure Determined on your chosen infrastructure (cloud or on-prem).
2. Connect your existing deep learning code (PyTorch, TensorFlow, or Keras) with Determined’s API; no changes to your training script are required.
3. Launch distributed training jobs that are automatically provisioned, scheduled, and monitored.
4. Use built-in hyperparameter search to explore configurations; visualize results in the Determined UI or TensorBoard.
5. Track experiments, manage artifacts, and deploy validated models via the built-in registry.
6. Share cluster resources securely with your team and scale as needed.

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Use Cases

• Large-scale distributed training without code changes
• Efficient hyperparameter optimization for faster convergence
• Reproducible ML workflows with artifact tracking
• Collaborative model development with shared resources and registries

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Getting Started

• See the Determined GitHub repository for installation and quick start guides.
• Use the Core API to integrate with existing models and workflows.
• Explore example deployments and tutorials to accelerate adoption.

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Safety and Best Practices

• Ensure appropriate access controls when sharing clusters and data.
• Follow organizational policies for data privacy and model deployment.

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Core Features

• Distributed training without code changes
• Scalable hyperparameter tuning with visualizations
• Built-in experiment tracking and artifact management
• Real-time experiment dashboards and advanced checkpointing
• Resource sharing and cluster scheduling for on-prem/cloud GPUs
• Seamless spot instance support
• Wide framework compatibility: PyTorch, TensorFlow, Keras
• Support for multiple data storage systems and easy model export to serving systems
• Model registry for deployment and collaboration