Project Description

Model Archiver is a tool used for creating archives of trained neural net models that can be consumed by MXNet-Model-Server inference.

Use the Model Archiver CLI to start create a .mar file.

Model Archiver is part of MMS. However,you ca install Model Archiver stand alone.

Detailed documentation and examples are provided in the README.

Prerequisites

ONNX support is optional in model-archiver tool. It’s not installed by default with model-archiver.

If you wish to package a ONNX model, you will need to first install a protobuf compiler, onnx and mxnet manually.

pip install model-archiver

We welcome new contributors of all experience levels. For information on how to install MMS for development, refer to the MMS docs.

You can check the latest source code as follows:

git clone https://github.com/awslabs/mxnet-model-server.git

After installation, try out the MMS Quickstart for Create a model archive and Serving a Model.

If you use MMS in a publication or project, please cite MMS: https://github.com/awslabs/mxnet-model-server

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Table of built distributions (wheels) for model-archiver 1.0.1
File	Interpreter	ABI	Platform	Reset
model_archiver-1.0.1-py2.py3-none-any.whl 19.2 kB Oct 30, 2018	Python 3, Python 2	none	any	Details

Download URL	model_archiver-1.0.1-py2.py3-none-any.whl
Size	19.2 kB
Tags	Python 2 Python 3
SHA-256 checksum How to use checksums	`b2d5b994aa6e6edebfc690ffc2a24438612ee04a20e8396adf06636175e46275`
BLAKE2b-256 checksum How to use checksums	`2b81e30a0b39f49d7c8ef4383bee82165d669980438b35e3dfd5c7881b6cf3c8`
Upload date	Oct 30, 2018
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.12.1 pkginfo/1.4.2 requests/2.20.0 setuptools/40.5.0 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/2.7.15`