Autoresearch as a controlled experiment
A design note on autonomous experimentation: fixed protocols, selection bias, independent evaluation, and durable research campaigns in Polyaxon.
RoadmapAI AgentsExperimentation
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The latest guides, tutorials, and product updates.
Tune PyTorch models with Polyaxon
Tune a PyTorch model's depth, width, dropout, optimizer, and learning rate with native TPE, GPU trials, and validation accuracy in Polyaxon.
PolyaxonHyperparameter TuningPyTorch
Tune hyperparameters with native TPE in Polyaxon
Use completed trial results to guide hyperparameter choices with native TPE, run GPU training trials, and compare validation loss in Polyaxon.
PolyaxonHyperparameter TuningExperimentation
Design a search space before launching the sweep
Use Polyaxon matrices to choose parameter scales, separate meaningful combinations, and control the size of a hyperparameter sweep.
Hyperparameter TuningMLOpsExperimentation
Did you find better hyperparameters—or a lucky seed?
Confirm hyperparameter finalists across matched training seeds, compare variation and paired differences, and reserve compute for a defensible final choice.
Hyperparameter TuningMLOpsExperimentation
Tune LLM hyperparameters within a compute budget
Design a small LoRA fine-tuning sweep, control its execution in Polyaxon, and compare validation quality, resource use, and repeatability.
Hyperparameter TuningLLMOpsGuides