..
  # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
  #
  # Redistribution and use in source and binary forms, with or without
  # modification, are permitted provided that the following conditions
  # are met:
  #  * Redistributions of source code must retain the above copyright
  #    notice, this list of conditions and the following disclaimer.
  #  * Redistributions in binary form must reproduce the above copyright
  #    notice, this list of conditions and the following disclaimer in the
  #    documentation and/or other materials provided with the distribution.
  #  * Neither the name of NVIDIA CORPORATION nor the names of its
  #    contributors may be used to endorse or promote products derived
  #    from this software without specific prior written permission.
  #
  # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
  # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
  # PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
  # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
  # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
  # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
  # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
  # OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Running the Server
==================

.. _section-example-model-repository:

Example Model Repository
------------------------

Before running the TensorRT Inference Server, you must first set up a
model repository containing the models that TRTIS will make available
for inferencing.

An example model repository containing a Caffe2 ResNet50, a TensorFlow
Inception model, and a simple TensorFlow GraphDef model (used by the
:ref:`simple_client example <section-client-api>`) are provided in the
`docs/examples/model_repository
<https://github.com/NVIDIA/tensorrt-inference-server/tree/master/docs/examples/model_repository>`_
directory. Before using the example model repository you must fetch
any missing model definition files from their public model zoos::

  $ cd docs/examples
  $ ./fetch_models.sh

.. _section-running-the-inference-server:

Running The Inference Server
----------------------------

Before running TRTIS, you must first set up a model repository
containing the models that TRTIS will make available
for inferencing. Section :ref:`section-model-repository` describes how
to create your own model repository. You can also use
:ref:`section-example-model-repository` to set up an example model
repository.

Assuming the sample model repository is available in
/path/to/model/repository, the following command runs the container
you pulled from NGC or built locally::

  $ nvidia-docker run --rm --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -p8000:8000 -p8001:8001 -p8002:8002 -v/path/to/model/repository:/models <tensorrtserver image name> trtserver --model-store=/models

Where *<tensorrtserver image name>* will be something like
**nvcr.io/nvidia/tensorrtserver:18.12-py3** if you pulled the
container from the NGC register, or **tensorrtserver** if you
:ref:`built it from source <section-building-the-server>`.

The nvidia-docker -v option maps /path/to/model/repository on the host
into the container at /models, and the -\\-model-store option to TRTIS
is used to point to /models as the model repository.

The -p flags expose the container ports where TRTIS listens for HTTP
requests (port 8000), listens for GRPC requests (port 8001), and
reports Prometheus metrics (port 8002).

The -\\-shm-size and -\\-ulimit flags are recommended to improve TRTIS
performance. For -\\-shm-size the minimum recommended size is 1g but
larger sizes may be necessary depending on the number and size of
models being served.

For more information on the Prometheus metrics provided by the
inference server see :ref:`section-metrics`.

.. _section-checking-inference-server-status:

Checking Inference Server Status
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The simplest way to verify that TRTIS is running correctly is to use
the Status API to query the server’s status. From the host system use
*curl* to access the HTTP endpoint to request server status. The
response is protobuf text showing the status for the server and for
each model being served, for example::

  $ curl localhost:8000/api/status
  id: "inference:0"
  version: "0.6.0"
  uptime_ns: 23322988571
  model_status {
    key: "resnet50_netdef"
    value {
      config {
        name: "resnet50_netdef"
        platform: "caffe2_netdef"
      }
      ...
      version_status {
        key: 1
        value {
          ready_state: MODEL_READY
        }
      }
    }
  }
  ready_state: SERVER_READY

This status shows configuration information as well as indicating that
version 1 of the resnet50_netdef model is MODEL_READY. This means that
TRTIS is ready to accept inferencing requests for version 1 of that
model. A model version ready_state will show up as MODEL_UNAVAILABLE
if the model failed to load for some reason.