Project Description

Apache MXNet Model Server (MMS) is a flexible and easy to use tool for serving deep learning models exported from MXNet or the Open Neural Network Exchange (ONNX).

Use the MMS Server CLI, or the pre-configured Docker images, to start a service that sets up HTTP endpoints to handle model inference requests.

Detailed documentation and examples are provided in the docs folder.

Prerequisites

java 8: Required. MMS use java to serve HTTP requests. You must install java 8 (or later) and make sure java is on available in $PATH environment variable before installing MMS. If you have multiple java installed, you can use $JAVA_HOME environment vairable to control which java to use.
mxnet: mxnet will not be installed by default with MMS 1.0 any more. You have to install it manually if you use MxNet.

For ubuntu:

sudo apt-get install openjdk-8-jre-headless

For centos

sudo yum install java-1.8.0-openjdk

For Mac:

brew tap caskroom/versions
brew update
brew cask install java8

Install MxNet:

pip install mxnet

MXNet offers MKL pip packages that will be much faster when running on Intel hardware. To install mkl package for CPU:

pip install mxnet-mkl

or for GPU instance:

pip install mxnet-cu92mkl

Installation

pip install mxnet-model-server

Development

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

Important links

Source code

You can check the latest source code as follows:

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

Testing

After installation, try out the MMS Quickstart for

Help and Support

Citation

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

Metadata

Release files for mxnet-model-server 1.0b20190107

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

Built distribution (wheel)

Table of built distributions (wheels) for mxnet-model-server 1.0b20190107
File	Interpreter	ABI	Platform	Reset
mxnet_model_server-1.0b20190107-py2.py3-none-any.whl 4.5 MB Jan 7, 2019 Without provenance	Python 2, Python 3	none	any	Details

Release files / mxnet_model_server-1.0b20190107-py2.py3-none-any.whl

Download URL	mxnet_model_server-1.0b20190107-py2.py3-none-any.whl
Size	4.5 MB
Tags	Python 2 Python 3
SHA-256 checksum How to use checksums	`b951d88eb8b220ccd21324cabf19f068ee35528aecc8555f5265e3575e6d6a37`
BLAKE2b-256 checksum How to use checksums	`0e12f67a6d12b5219abfb0e4f8eb6d25e831dd3667323c1a2b89f56ef524a18f`
Upload date	Jan 7, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.12.1 pkginfo/1.4.2 requests/2.21.0 setuptools/39.2.0 requests-toolbelt/0.8.0 tqdm/4.29.0 CPython/2.7.12`

Security for mxnet-model-server 1.0b20190107

Report project as malware

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.