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>log in to commentWe built Hyperopt to let you auto‑tune neural‑network configs while keeping your data and models on your own machine. The Python SDK installs in one step, runs demos locally, and talks to our service to suggest the next training step. Give it a try and let us know what works and what could be better 😊
AutoML tuner runs hyperparameter tuning locally, selecting the best neural-network model using half the training budget.
- for
- ML engineers and data scientists who need efficient model selection on private data.
- pricing
- free
Key features
- Python SDK — Install a single package to run tuning demos and integrate with your code.
- Local data & models — Your data, models and training stay on your machine; only validation errors are sent.
- Half-budget search — Explores ten configurations while using only 50% of the brute-force training budget.
- Benchmark comparison — Built-in benchmarks show the service outperforms reference schedulers on most datasets.
- Beta session limits — 1 active session per account, 20 sessions per day, up to 2 hours each.
Use cases
- Select a production model cheaply for periodic retraining
- Compare and analyze families of neural-network architectures
- Optimize a target KPI rather than raw training error
Hyperopt FAQ
What problem does the service solve?+
It reduces exhaustive hyperparameter tuning cost by aiming to find the best model with half the usual training budget.
Will my proprietary data be shared?+
Only validation errors are sent; raw data, code, and model weights remain on your device.
Does it always find the optimal model?+
No, it provides statistical guarantees and may select a model close to the best.
What are the beta limitations?+
The demo compares up to ten models at a time, uses half the brute-force budget, and enforces session limits.
How can I try it?+
Create a free beta account, install the SDK via pip, and run the provided demo commands.
Summarized by DevHunt from hyperopt.dev · Oct 13, 2026. Details may change; check the official site.
