Skip to content

Latest commit

 

History

History
160 lines (111 loc) · 4.69 KB

README.md

File metadata and controls

160 lines (111 loc) · 4.69 KB
Raw

Build Status Coverage Status

English | 中文

ORMB is an open-source model registry to manage machine learning model.

ORMB helps you manage your Machine Learning/Deep Learning models with image registry. It makes your models easy to create, version, share and publish.

Getting Started

You can watch our sample usage video or read the text version below.

asciicast

First, open a model folder that meets the specification of ORMB. (See our documentation for more information about ormbfile.yaml)

# View the local file directory
$ tree examples/SavedModel-fashion
examples/SavedModel-fashion
├── model
│   ├── saved_model.pb
│   └── variables
│       ├── variables.data-00000-of-00001
│       └── variables.index
├── ormbfile.yaml
└── training-serving.ipynb

2 directories, 5 files

Next, we can push the trained model from local to remote image registry.

# Save the model from model directory in local cache first
$ ormb save <model directory> gaocegege/fashion_model:v1
ref:       gaocegege/fashion_model:v1
digest:    6b08cd25d01f71a09c1eb852b3a696ee2806abc749628de28a71b507f9eab996
size:      162.1 KiB
format:    SavedModel
v1: saved

# Push the model from local cache to remote registry
$ ormb push gaocegege/fashion_model:v1
The push refers to repository [gaocegege/fashion_model]
ref:       gaocegege/fashion_model:v1
digest:    6b08cd25d01f71a09c1eb852b3a696ee2806abc749628de28a71b507f9eab996
size:      162.1 KiB
format:    SavedModel
v1: pushed to remote (1 layer, 162.1 KiB total)

Taking Harbor as an example, we can see the model's metadata in Harbor registry.

Then, we can download the model from the registry. The download process is similar to the push.

# Pull the model from remote registry to local cache
$ ormb pull gaocegege/fashion_model:v1
v1: Pulling from gaocegege/fashion_model
ref:     gaocegege/fashion_model:v1
digest:  6b08cd25d01f71a09c1eb852b3a696ee2806abc749628de28a71b507f9eab996
size:    162.1 KiB
Status: Downloaded newer model for gaocegege/fashion_model:v1

# Export the model from local cache to current directory
$ ormb export gaocegege/fashion_model:v1
ref:     localhost/gaocegege/fashion_model:v1
digest:  6b08cd25d01f71a09c1eb852b3a696ee2806abc749628de28a71b507f9eab996
size:    162.1 KiB

# View the local file directory
$ tree examples/SavedModel-fashion
examples/SavedModel-fashion
├── model
│   ├── saved_model.pb
│   └── variables
│       ├── variables.data-00000-of-00001
│       └── variables.index
├── ormbfile.yaml
└── training-serving.ipynb

2 directories, 5 files

Installation

Install the image registry

ORMB uses the image registry to store model, you can choose to install Harbor or use Docker Registry. We recommended Harbor here.

Install ORMB

You can install the pre-compiled binary, or compile from source.

Install the pre-compiled binary

Download the pre-compiled binaries from the releases page and copy to the desired location.

Compile from source

Clone:

$ git clone https://github.com/kleveross/ormb
$ cd ormb

Get the dependencies:

$ go mod tidy

Build:

$ make build-local

Verify it works:

$ ./bin/ormb --help

Understanding ORMB

Why choose ORMB?

See ORMB introduction for more information.

Official Documentation

See ORMB docs for more information.

Tutorials

OCI Model Configuration Specification

Please have a look at docs/spec_v1alpha1.md

Community

ORMB project is part of Klever, a Cloud Native Machine Learning platform.

The Klever slack workspace is klever.slack.com. To join, click this invitation to our Slack workspace.