Metadata-Version: 2.1
Name: mxnet-model-server-prometheus
Version: 1.0.8b20190923
Summary: Model Server for Apache MXNet is a tool for serving neural net models for inference. With Prometheus metrics.
Home-page: https://github.com/crmne/mxnet-model-server
Author: MXNet SDK team
Author-email: carmine@paolino.me
License: Apache License Version 2.0
Description: Project Description
        ===================
        
        Apache MXNet Model Server (MMS) is a flexible and easy to use tool for
        serving deep learning models exported from `MXNet <http://mxnet.io/>`__
        or the Open Neural Network Exchange (`ONNX <http://onnx.ai/>`__).
        
        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 <https://github.com/awslabs/mxnet-model-server/blob/master/docs/README.md>`__.
        
        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 <https://github.com/awslabs/mxnet-model-server/blob/master/docs/install.md>`__.
        
        Important links
        ---------------
        
        -  `Official source code
           repo <https://github.com/awslabs/mxnet-model-server>`__
        -  `Download
           releases <https://pypi.org/project/mxnet-model-server/#files>`__
        -  `Issue
           tracker <https://github.com/awslabs/mxnet-model-server/issues>`__
        
        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 <https://github.com/awslabs/mxnet-model-server/blob/master/README.md#serve-a-model>`__
        - `Create a Model Archive <https://github.com/awslabs/mxnet-model-server/blob/master/README.md#model-archive>`__.
        
        Help and Support
        ----------------
        
        -  `Documentation <https://github.com/awslabs/mxnet-model-server/blob/master/docs/README.md>`__
        -  `Forum <https://discuss.mxnet.io/latest>`__
        
        Citation
        --------
        
        If you use MMS in a publication or project, please cite MMS:
        https://github.com/awslabs/mxnet-model-server
        
Keywords: MXNet Model Server Serving Deep Learning Inference AI
Platform: UNKNOWN
Provides-Extra: mxnet-cu90mkl
Provides-Extra: mxnet-mkl
Provides-Extra: mxnet
