Skip to main content

Model Server for Apache MXNet is a tool for serving neural net models for inference

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

If you wish to use ONNX with MMS, you will need to first install a protobuf compiler. This is not needed if you wish to serve MXNet models.

Instructions for installing MMS with ONNX.

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.

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 Serving a Model and Exporting a Model.

Help and Support

Citation

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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

mxnet_model_server-1.0b20181005-py2.py3-none-any.whl (4.5 MB view details)

Uploaded Python 2 Python 3

File details

Details for the file mxnet_model_server-1.0b20181005-py2.py3-none-any.whl.

File metadata

  • Download URL: mxnet_model_server-1.0b20181005-py2.py3-none-any.whl
  • Upload date:
  • Size: 4.5 MB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.18.4 setuptools/39.2.0 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/2.7.12

File hashes

Hashes for mxnet_model_server-1.0b20181005-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 4a1fcf6d95db9f6e6195e7fb1791fd3511d882d9fe17ca979ecb468ca520fdaf
MD5 0269b403e09198383f3c8a44495a57cb
BLAKE2b-256 6e9961a7edc648de474227a370cf7cd27db01e305b538ddbda34faa3eb375052

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page