# tobegit3hub/advisor

Open-source implementation of Google Vizier for hyper parameters tuning

Repository: https://github.com/tobegit3hub/advisor
Canonical: https://ross.abutalabs.com/products/advisor
Homepage: https://advisor.readthedocs.io
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: ml, dl, hyperparameters, optimization, blackbox, algorithm, tunning, automl
Last push: 2019-11-11T06:59:32+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3275, "days_push": 2487, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1563, forks 251 (observed 2026-08-28T04:05:04.075089+00:00)

## What it is
Advisor is an open-source implementation of Google Vizier, a hyperparameter tuning system for black-box optimization. It provides a server with web UI, Python SDK, and CLI, supporting algorithms like Bayesian optimization, TPE, grid/random search, and early stopping.

## Use cases
- tune hyperparameters of machine learning models
- run Bayesian optimization for black-box functions
- self-host a Google Vizier-like tuning service
- compare hyperparameter search algorithms like TPE and CMAES
- early-stop underperforming training trials
- manage hyperparameter tuning studies via CLI or Python SDK

## When to choose
- you want an open-source Google Vizier equivalent with API, SDK, web UI, and CLI
- you need multiple search algorithms (Bayesian, TPE, grid, random, CMAES) in one service
- you want to deploy a tuning service on Docker or Kubernetes
- you need early-stopping algorithms for expensive training jobs

## When to avoid
- you need actively maintained software with recent releases
- you want a modern AutoML platform like Optuna, Ray Tune, or Microsoft NNI with larger communities
- you only need simple grid or random search without a server
- you require SMAC or other unsupported algorithms

## Facets
- artifact type: service
- maturity: maintenance
- function: machine-learning, sdk, cli, api-framework
- domain: machine-learning, artificial-intelligence, developer-tools
- platform: python, cli
- tags: hyperparameter-tuning, automl, blackbox-optimization, google-vizier, bayesian-optimization, docker, kubernetes, web-server

## Member repositories
- tobegit3hub/advisor (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:04.075089+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:00:16.518459+00:00, confidence not recorded.
  - readme: https://github.com/tobegit3hub/advisor (fetched 2026-08-28T04:05:04.075089+00:00, sha a75be3264f1c)
  - registry_pypi: https://pypi.org/pypi/advisor/json (fetched 2026-08-29T11:29:41.580987+00:00, sha 8a58748e98be)
- Data as of 2026-08-30T08:39:29.467469+00:00.