AI DevOps
AutoML that just works. Keep training local.
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A hyperparameter tuning service where you train models locally and receive optimization suggestions through a Python SDK.
hyperopt.dev is an AutoML service for hyperparameter tuning that keeps model training local on your computer. You define candidate model configurations, train them locally, and report validation losses to the service, which then suggests which model to train next. The service helps allocate training observations efficiently across candidates, using half the budget of fully training all models in the demo.
Access is provided through the Tuning Client Python SDK, which requires Python 3.11+. The included demos cover classification, regression, and small language and vision tasks, with results saved locally. The demo compares up to ten model configurations at a time with a tuning cap of 50 observations, compared with 100 to fully train all models. The service does not receive model weights, raw datasets, or execute your training code—only model indices, validation losses, and progress are sent.
Pricing model: Free trial
Free beta with no billing. Paid plans launching in the future; pricing not yet announced. Join the waitlist for paid plans without payment or purchase commitment.
The service helps allocate training observations across candidate configurations. You train locally and send validation losses; the service suggests which model to train next. The demo caps tuning at 50 observations compared with 100 to fully train all ten candidates.
Selecting a production model cheaply, especially if periodic retraining is needed; analysing and comparing model families; and optimizing a target KPI rather than just training error.
The service receives model indices, validation losses, and progress. Configuration descriptions are optional. Datasets and weights remain local. Account/session information and optional demo events support operation.
No. Model selection depends on the configurations, data, and training budget. A particular run may select a model that is not best after full training.
The demo compares ten models at a time and uses half the brute-force budget. The beta has 1 active session per account, 20 sessions per day, and up to 2 hours per session including queue time.
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