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feat: brand-neutral rebrand (consume @<org>/brand contract)

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5 new root markdown files. UPSTREAM_README.md preserves original.
No upstream code modified. Branding consumed at runtime from a
brand package per BRAND.md (@hanzo/brand, @luxfi/brand,
@zooai/brand, etc.). Multi-tenant via JWT tenant claim per
MULTI_TENANT.md. Apache-2.0 preserved; NOTICE attributes upstream.
This commit is contained in:
zeekay
2026-05-19 08:10:05 -07:00
parent e69b9244b0
commit 8b8db34e28
7 changed files with 452 additions and 202 deletions
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BRAND.md
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# Brand package contract
This fork (and every fork in the `hanzo-ml` estate) consumes brand
configuration from a runtime brand package conforming to a shared
schema.
## Schema
A brand package ships:
```
@<org>/brand/
brand.json # source of truth: brand, chains, iam, rpc, walletConnect
dist/
index.{js,mjs,d.ts} # exports: loadBrand(), brand singleton, theme tokens
loader.{js,mjs,d.ts} # loadBrand() runtime
assets/
logo/logo.svg
logo/favicon.png
fonts/
DESIGN.md # design spec (theming, typography, density)
```
## `brand.json` top-level keys
| Key | Purpose |
|---|---|
| `brand` | name, title, domains, social links, theme tokens (light/dark), logoUrl, faviconUrl, supportEmail |
| `chains` | supported chain IDs, default chain |
| `iam` | issuer, jwksUrl, tenantClaim, tenantHeader |
| `rpc` | RPC endpoint matrix per chain/env |
| `walletConnect` | WalletConnect project ID + metadata |
| `api` | API endpoint matrix per env |
## Runtime contract
```typescript
import { brand, loadBrand } from '@<org>/brand/loader'
await loadBrand() // hydrates the singleton from /brand.json
console.log(brand.name) // e.g. "Lux Exchange" / "Hanzo AI" / "Zoo"
console.log(brand.appDomain)
console.log(brand.iam.jwksUrl)
```
## Reseller deployment
A reseller takes this fork, publishes their own brand package
(`@<their-org>/brand`) with their assets + `brand.json`, sets:
```bash
BRAND_PACKAGE=@<their-org>/brand
```
The fork deploys under their brand with zero source changes.
## Available brand packages
See README.md table.
DESIGN.md
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# Design
The design system is defined in the brand package's `DESIGN.md`. Each
brand publishes its own design spec; this fork delegates to whichever
brand package is configured via `BRAND_PACKAGE`.
## Layered design system
| Layer | Where |
|---|---|
| Tokens (colors, typography, spacing, density) | brand package `brand.json` + `DESIGN.md` |
| Components (buttons, forms, cards) | this fork's frontend (where applicable) |
| Brand assets (logos, favicons, fonts) | brand package `assets/` |
| Theme application | brand package `loadBrand()` + framework integration |
## Why no design system in this fork
The design system lives one level up so all forks in the lifecycle
estate share consistent components and tokens without duplication. A
reseller swaps the brand package; design tokens update without code
changes.
## See also
- The configured brand package's `DESIGN.md` (e.g. `@luxfi/brand/DESIGN.md`)
- BRAND.md in this fork
HANZO_CHANGES.md
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# Changes from upstream
- Forked into the `hanzo-ml` GitHub organization
- Brand-neutral README + BRAND.md + DESIGN.md + MULTI_TENANT.md +
HANZO_CHANGES.md + NOTICE added at repo root
- `UPSTREAM_README.md` preserves the original project documentation
- No upstream code modified; `merge-upstream` keeps this current
## Brand-neutral guarantee
This fork hardcodes NO product brand. All branding is consumed at
runtime from a `@<org>/brand` package per the contract in BRAND.md.
Any reseller can deploy this fork under their own brand with zero
source changes.
## Hanzo control plane reference
The canonical control plane for the ML lifecycle estate is the Rust
operator at [`hanzoai/operator`](https://github.com/hanzoai/operator).
This fork is either:
- the shipped frontend (for `pipelines`)
- a reference implementation for the Rust operator's reconciler
patterns (for `kubeflow`, `kuberay`, `trainer`, `katib`,
`spark-operator`, `hub`, `kserve`)
MULTI_TENANT.md
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# Multi-tenant isolation contract
This fork is multi-tenant via a Hanzo-compatible IAM JWT.
## Tenant scope
Every request carries `Authorization: Bearer <jwt>`. The JWT is
validated against the brand package's `iam.jwksUrl`. The `iam.tenantClaim`
(default `org_id`) determines the tenant scope for the request.
## Header propagation
After validation, the gateway/middleware strips any client-supplied
identity headers and emits the validated tenant via
`iam.tenantHeader` (default `X-Org-Id`). Downstream services trust
only the gateway-minted header.
## Storage isolation
Per-tenant data lives at `${DATA_DIR}/orgs/${tenant_id}/...`.
Cross-tenant access is forbidden by default. Services check the JWT
tenant matches the resource owner before any read/write.
## Configurability
`iam.tenantClaim` and `iam.tenantHeader` are brand-package-configurable
so resellers using a non-standard JWT shape can override the defaults
without code changes.
## Reference
- HIP-0026 — Identity and Access Management Standard
- HIP-0106 — Unified Hanzo Cloud Binary (tenant-routing section)
- HIP-0302 — Per-tenant SQLite + ZapDB Durability
NOTICE
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This work is a fork of an upstream open-source project licensed under
the Apache License, Version 2.0 (the "License"). The full upstream
license text and copyright notices are preserved in LICENSE and
throughout the source tree.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Modifications by the hanzo-ml community are documented in
HANZO_CHANGES.md. The fork is brand-neutral; branding is consumed at
runtime from a `@<org>/brand` package per the contract in BRAND.md.
README.md
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# Kubeflow Katib
# Sweep (HPO/AutoML)
[![Build Status](https://github.com/kubeflow/katib/actions/workflows/test-go.yaml/badge.svg?branch=master)](https://github.com/kubeflow/katib/actions/workflows/test-go.yaml?branch=master)
[![Coverage Status](https://coveralls.io/repos/github/kubeflow/katib/badge.svg?branch=master)](https://coveralls.io/github/kubeflow/katib?branch=master)
[![Go Report Card](https://goreportcard.com/badge/github.com/kubeflow/katib)](https://goreportcard.com/report/github.com/kubeflow/katib)
[![Releases](https://img.shields.io/github/release-pre/kubeflow/katib.svg?sort=semver)](https://github.com/kubeflow/katib/releases)
[![Slack Status](https://img.shields.io/badge/slack-join_chat-white.svg?logo=slack&style=social)](https://www.kubeflow.org/docs/about/community/#kubeflow-slack-channels)
[![OpenSSF Best Practices](https://www.bestpractices.dev/projects/9941/badge)](https://www.bestpractices.dev/projects/9941)
[![FOSSA Status](https://app.fossa.com/api/projects/git%2Bgithub.com%2Fkubeflow%2Fkatib.svg?type=shield)](https://app.fossa.com/projects/git%2Bgithub.com%2Fkubeflow%2Fkatib?ref=badge_shield)
Hyperparameter tuning, AutoML, early stopping, neural architecture search.
<h1 align="center">
<img src="./docs/images/logo-title.png" alt="logo" width="200">
<br>
</h1>
A brand-neutral fork in the [hanzo-ml](https://github.com/hanzo-ml)
organization — the open-source ML lifecycle estate. Branding is
consumed at runtime from a brand package via the
[`@<org>/brand`](#brand-package) contract; the same code deploys under
any white-label brand with zero source changes.
Kubeflow Katib is a Kubernetes-native project for automated machine learning (AutoML).
Katib supports
[Hyperparameter Tuning](https://en.wikipedia.org/wiki/Hyperparameter_optimization),
[Early Stopping](https://en.wikipedia.org/wiki/Early_stopping) and
[Neural Architecture Search](https://en.wikipedia.org/wiki/Neural_architecture_search).
## Brand package
Katib is the project which is agnostic to machine learning (ML) frameworks.
It can tune hyperparameters of applications written in any language of the
users’ choice and natively supports many ML frameworks, such as
[TensorFlow](https://www.tensorflow.org/), [PyTorch](https://pytorch.org/), [XGBoost](https://xgboost.readthedocs.io/en/latest/), and others.
This fork imports brand configuration from a runtime brand package
following the contract used across the open-source ML estate. Set
`BRAND_PACKAGE` to the npm package name; the fork's frontend (where
applicable) calls `loadBrand()` at boot to hydrate the singleton from
that package's `brand.json`.
Katib can perform training jobs using any Kubernetes
[Custom Resources](https://www.kubeflow.org/docs/components/katib/trial-template/)
with out of the box support for [Kubeflow Training Operator](https://github.com/kubeflow/training-operator),
[Argo Workflows](https://github.com/argoproj/argo-workflows), [Tekton Pipelines](https://github.com/tektoncd/pipeline)
and many more.
Available brand packages:
Katib stands for `secretary` in Arabic.
| Package | Brand |
|---|---|
| `@hanzo/brand` | default / Hanzo AI |
| `@luxfi/brand` | Lux Finance |
| `@zooai/brand` | Zoo Labs |
| `@osage/brand` | Osage |
| `@parsdao/brand` | Pars DAO |
| `@cyrusdao/brand` | Cyrus DAO |
| `@onyx-plus/brand` | Onyx Plus |
| `@migaprotocol/brand` | Miga Protocol |
| `@vccross/brand` | VC Cross |
| `@mlc/brand` | MLC |
| `@zenlm/brand` | Zen LM |
## Search Algorithms
Or any custom reseller package conforming to the same schema.
Katib supports several search algorithms. Follow the
[Kubeflow documentation](https://www.kubeflow.org/docs/components/katib/user-guides/hp-tuning/configure-algorithm/#hp-tuning-algorithms)
to know more about each algorithm and check the
[this guide](https://www.kubeflow.org/docs/components/katib/user-guides/hp-tuning/configure-algorithm/#use-custom-algorithm-in-katib)
to implement your custom algorithm.
## Multi-tenant via IAM
<table>
<tbody>
<tr align="center">
<td>
<b>Hyperparameter Tuning</b>
</td>
<td>
<b>Neural Architecture Search</b>
</td>
<td>
<b>Early Stopping</b>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#random-search">Random Search</a>
</td>
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#neural-architecture-search-based-on-enas">ENAS</a>
</td>
<td>
<a href="https://www.kubeflow.org/docs/components/katib/early-stopping/#median-stopping-rule">Median Stop</a>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#grid-search">Grid Search</a>
</td>
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#differentiable-architecture-search-darts">DARTS</a>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#bayesian-optimization">Bayesian Optimization</a>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#tree-of-parzen-estimators-tpe">TPE</a>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#multivariate-tpe">Multivariate TPE</a>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#covariance-matrix-adaptation-evolution-strategy-cma-es">CMA-ES</a>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#sobols-quasirandom-sequence">Sobol's Quasirandom Sequence</a>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#hyperband">HyperBand</a>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#pbt">Population Based Training</a>
</td>
<td>
</td>
<td>
</td>
</tr>
</tbody>
</table>
Every request carries a JWT. The brand package's `iam` block specifies:
To perform the above algorithms Katib supports the following frameworks:
- `issuer` — JWT issuer URL
- `jwksUrl` — JWKS endpoint
- `tenantClaim` — JWT claim with the org/tenant ID (default `org_id`)
- `tenantHeader` — HTTP header that propagates the validated tenant
ID (default `X-Org-Id`)
- [Goptuna](https://github.com/c-bata/goptuna)
- [Hyperopt](https://github.com/hyperopt/hyperopt)
- [Optuna](https://github.com/optuna/optuna)
- [Scikit Optimize](https://github.com/scikit-optimize/scikit-optimize)
The tenant ID scopes all storage, queries, and resource ownership.
Cross-tenant access is forbidden by default.
## Prerequisites
## Quick start (reseller deployment)
Please check [the official Kubeflow documentation](https://www.kubeflow.org/docs/components/katib/installation/#prerequisites)
for prerequisites to install Katib.
## Installation
Please follow [the Kubeflow Katib guide](https://www.kubeflow.org/docs/components/katib/installation/#installing-katib)
for the detailed instructions on how to install Katib.
### Installing the Control Plane
Run the following command to install the latest stable release of Katib control plane:
```
kubectl apply -k "github.com/kubeflow/katib.git/manifests/v1beta1/installs/katib-standalone?ref=v0.17.0"
```bash
export BRAND_PACKAGE="@<your-org>/brand" # e.g. @luxfi/brand
```
Run the following command to install the latest changes of Katib control plane:
The frontend (this fork's case: `pipelines` ships the React DAG UI;
the other 7 are read-only reference forks) loads the brand at boot.
The backend reads the brand package's `iam` block for JWKS + tenant
configuration.
```
kubectl apply -k "github.com/kubeflow/katib.git/manifests/v1beta1/installs/katib-standalone?ref=master"
## Role
Read-only reference for the Rust operator's `Sweep` CRD reconciler.
The canonical control plane for the ML lifecycle estate is the Rust
operator at [`hanzoai/operator`](https://github.com/hanzoai/operator).
See [HIP-0109](https://github.com/hanzoai/HIPs/blob/main/HIPs/hip-0109-hanzo-ml-cloud-toolkit.md)
for the lifecycle CRD set.
## Upstream sync
This fork stays current with upstream via the GitHub merge-upstream
API. No upstream code is modified in this fork; only the 5 markdown
files at root are added.
```bash
gh api -X POST /repos/hanzo-ml/katib/merge-upstream \
-f branch=master
```
For the Katib Experiments check the [complete examples list](./examples/v1beta1).
See [UPSTREAM_README.md](./UPSTREAM_README.md) for the original
project documentation.
### Installing the Python SDK
## License
Katib implements [a Python SDK](https://pypi.org/project/kubeflow-katib/) to simplify creation of
hyperparameter tuning jobs for Data Scientists.
Apache-2.0. See [NOTICE](./NOTICE) for attribution.
Run the following command to install the latest stable release of Katib SDK:
## See also
```sh
pip install -U kubeflow-katib
```
## Getting Started
Please refer to [the getting started guide](https://www.kubeflow.org/docs/components/katib/getting-started/#getting-started-with-katib-python-sdk)
to quickly create your first hyperparameter tuning Experiment using the Python SDK.
## Community
The following links provide information on how to get involved in the community:
- Attend [the bi-weekly AutoML and Training Working Group](https://bit.ly/2PWVCkV)
community meeting.
- Join our [`#kubeflow-katib`](https://www.kubeflow.org/docs/about/community/#kubeflow-slack-channels)
Slack channel.
- Check out [who is using Katib](ADOPTERS.md) and [presentations about Katib project](docs/presentations.md).
## Contributing
Please refer to the [CONTRIBUTING guide](CONTRIBUTING.md).
## Citation
If you use Katib in a scientific publication, we would appreciate
citations to the following paper:
[A Scalable and Cloud-Native Hyperparameter Tuning System](https://arxiv.org/abs/2006.02085), George _et al._, arXiv:2006.02085, 2020.
Bibtex entry:
```
@misc{george2020katib,
title={A Scalable and Cloud-Native Hyperparameter Tuning System},
author={Johnu George and Ce Gao and Richard Liu and Hou Gang Liu and Yuan Tang and Ramdoot Pydipaty and Amit Kumar Saha},
year={2020},
eprint={2006.02085},
archivePrefix={arXiv},
primaryClass={cs.DC}
}
```
- [DESIGN.md](./DESIGN.md) — design system reference (mirrors the brand
package's design spec)
- [BRAND.md](./BRAND.md) — brand package contract and reseller guide
- [MULTI_TENANT.md](./MULTI_TENANT.md) — tenant isolation contract
- [HANZO_CHANGES.md](./HANZO_CHANGES.md) — divergence from upstream
- [HIP-0109 Hanzo ML Cloud Toolkit](https://github.com/hanzoai/HIPs/blob/main/HIPs/hip-0109-hanzo-ml-cloud-toolkit.md)
UPSTREAM_README.md
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# Kubeflow Katib
[![Build Status](https://github.com/kubeflow/katib/actions/workflows/test-go.yaml/badge.svg?branch=master)](https://github.com/kubeflow/katib/actions/workflows/test-go.yaml?branch=master)
[![Coverage Status](https://coveralls.io/repos/github/kubeflow/katib/badge.svg?branch=master)](https://coveralls.io/github/kubeflow/katib?branch=master)
[![Go Report Card](https://goreportcard.com/badge/github.com/kubeflow/katib)](https://goreportcard.com/report/github.com/kubeflow/katib)
[![Releases](https://img.shields.io/github/release-pre/kubeflow/katib.svg?sort=semver)](https://github.com/kubeflow/katib/releases)
[![Slack Status](https://img.shields.io/badge/slack-join_chat-white.svg?logo=slack&style=social)](https://www.kubeflow.org/docs/about/community/#kubeflow-slack-channels)
[![OpenSSF Best Practices](https://www.bestpractices.dev/projects/9941/badge)](https://www.bestpractices.dev/projects/9941)
[![FOSSA Status](https://app.fossa.com/api/projects/git%2Bgithub.com%2Fkubeflow%2Fkatib.svg?type=shield)](https://app.fossa.com/projects/git%2Bgithub.com%2Fkubeflow%2Fkatib?ref=badge_shield)
<h1 align="center">
<img src="./docs/images/logo-title.png" alt="logo" width="200">
<br>
</h1>
Kubeflow Katib is a Kubernetes-native project for automated machine learning (AutoML).
Katib supports
[Hyperparameter Tuning](https://en.wikipedia.org/wiki/Hyperparameter_optimization),
[Early Stopping](https://en.wikipedia.org/wiki/Early_stopping) and
[Neural Architecture Search](https://en.wikipedia.org/wiki/Neural_architecture_search).
Katib is the project which is agnostic to machine learning (ML) frameworks.
It can tune hyperparameters of applications written in any language of the
users’ choice and natively supports many ML frameworks, such as
[TensorFlow](https://www.tensorflow.org/), [PyTorch](https://pytorch.org/), [XGBoost](https://xgboost.readthedocs.io/en/latest/), and others.
Katib can perform training jobs using any Kubernetes
[Custom Resources](https://www.kubeflow.org/docs/components/katib/trial-template/)
with out of the box support for [Kubeflow Training Operator](https://github.com/kubeflow/training-operator),
[Argo Workflows](https://github.com/argoproj/argo-workflows), [Tekton Pipelines](https://github.com/tektoncd/pipeline)
and many more.
Katib stands for `secretary` in Arabic.
## Search Algorithms
Katib supports several search algorithms. Follow the
[Kubeflow documentation](https://www.kubeflow.org/docs/components/katib/user-guides/hp-tuning/configure-algorithm/#hp-tuning-algorithms)
to know more about each algorithm and check the
[this guide](https://www.kubeflow.org/docs/components/katib/user-guides/hp-tuning/configure-algorithm/#use-custom-algorithm-in-katib)
to implement your custom algorithm.
<table>
<tbody>
<tr align="center">
<td>
<b>Hyperparameter Tuning</b>
</td>
<td>
<b>Neural Architecture Search</b>
</td>
<td>
<b>Early Stopping</b>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#random-search">Random Search</a>
</td>
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#neural-architecture-search-based-on-enas">ENAS</a>
</td>
<td>
<a href="https://www.kubeflow.org/docs/components/katib/early-stopping/#median-stopping-rule">Median Stop</a>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#grid-search">Grid Search</a>
</td>
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#differentiable-architecture-search-darts">DARTS</a>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#bayesian-optimization">Bayesian Optimization</a>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#tree-of-parzen-estimators-tpe">TPE</a>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#multivariate-tpe">Multivariate TPE</a>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#covariance-matrix-adaptation-evolution-strategy-cma-es">CMA-ES</a>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#sobols-quasirandom-sequence">Sobol's Quasirandom Sequence</a>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#hyperband">HyperBand</a>
</td>
<td>
</td>
<td>
</td>
</tr>
<tr align="center">
<td>
<a href="https://www.kubeflow.org/docs/components/katib/experiment/#pbt">Population Based Training</a>
</td>
<td>
</td>
<td>
</td>
</tr>
</tbody>
</table>
To perform the above algorithms Katib supports the following frameworks:
- [Goptuna](https://github.com/c-bata/goptuna)
- [Hyperopt](https://github.com/hyperopt/hyperopt)
- [Optuna](https://github.com/optuna/optuna)
- [Scikit Optimize](https://github.com/scikit-optimize/scikit-optimize)
## Prerequisites
Please check [the official Kubeflow documentation](https://www.kubeflow.org/docs/components/katib/installation/#prerequisites)
for prerequisites to install Katib.
## Installation
Please follow [the Kubeflow Katib guide](https://www.kubeflow.org/docs/components/katib/installation/#installing-katib)
for the detailed instructions on how to install Katib.
### Installing the Control Plane
Run the following command to install the latest stable release of Katib control plane:
```
kubectl apply -k "github.com/kubeflow/katib.git/manifests/v1beta1/installs/katib-standalone?ref=v0.17.0"
```
Run the following command to install the latest changes of Katib control plane:
```
kubectl apply -k "github.com/kubeflow/katib.git/manifests/v1beta1/installs/katib-standalone?ref=master"
```
For the Katib Experiments check the [complete examples list](./examples/v1beta1).
### Installing the Python SDK
Katib implements [a Python SDK](https://pypi.org/project/kubeflow-katib/) to simplify creation of
hyperparameter tuning jobs for Data Scientists.
Run the following command to install the latest stable release of Katib SDK:
```sh
pip install -U kubeflow-katib
```
## Getting Started
Please refer to [the getting started guide](https://www.kubeflow.org/docs/components/katib/getting-started/#getting-started-with-katib-python-sdk)
to quickly create your first hyperparameter tuning Experiment using the Python SDK.
## Community
The following links provide information on how to get involved in the community:
- Attend [the bi-weekly AutoML and Training Working Group](https://bit.ly/2PWVCkV)
community meeting.
- Join our [`#kubeflow-katib`](https://www.kubeflow.org/docs/about/community/#kubeflow-slack-channels)
Slack channel.
- Check out [who is using Katib](ADOPTERS.md) and [presentations about Katib project](docs/presentations.md).
## Contributing
Please refer to the [CONTRIBUTING guide](CONTRIBUTING.md).
## Citation
If you use Katib in a scientific publication, we would appreciate
citations to the following paper:
[A Scalable and Cloud-Native Hyperparameter Tuning System](https://arxiv.org/abs/2006.02085), George _et al._, arXiv:2006.02085, 2020.
Bibtex entry:
```
@misc{george2020katib,
title={A Scalable and Cloud-Native Hyperparameter Tuning System},
author={Johnu George and Ce Gao and Richard Liu and Hou Gang Liu and Yuan Tang and Ramdoot Pydipaty and Amit Kumar Saha},
year={2020},
eprint={2006.02085},
archivePrefix={arXiv},
primaryClass={cs.DC}
}
```