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Fine-tuningFreemiumOpen Source
DETERMINED AI
Distributed training and hyperparameter tuning made simple
Apache-2.0
ABOUT
Fine-tuning and training large models requires managing GPU clusters, tracking experiments, tuning hyperparameters, and handling failures — all of which become prohibitively complex at scale. Determined AI provides a unified platform that automates distributed training across multiple GPUs, runs intelligent hyperparameter searches, tracks every experiment, and automatically recovers from hardware failures, letting ML engineers focus on model architecture rather than infrastructure.
INSTALL
pip install determinedINTEGRATION GUIDE
1. Distribute large model fine-tuning across multiple GPUs without rewriting training code
2. Run automated hyperparameter sweeps with early stopping to find optimal configurations faster
3. Track and compare thousands of training experiments with metrics, artifacts, and system logs
4. Fine-tune LLMs on custom datasets using distributed PyTorch or TensorFlow training
5. Queue and schedule training jobs across GPU clusters with automatic fault tolerance
TAGS
distributed-traininghyperparameter-tuningexperiment-trackingpytorchtensorflowmlops