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Testing
=======

Currently there is no CI testing enabled for the open-source version
of the TensorRT Inference Server. We will enable CI testing in a
future update.

There is a set of tests in the qa/ directory that can be run manually
to provide some testing. Before running these tests you must first
generate a couple of test model repositories containing the models
needed by the tests.

Generate QA Model Repositories
------------------------------

The QA model repositories contain some simple models that are used to
verify the correctness of the inference server. To generate the QA
model repositories::

  $ cd qa/common
  $ ./gen_qa_model_repository

This will generate multiple model repositories:
/tmp/qa_model_repository, /tmp/qa_reshape_repository,
/tmp/qa_sequence_model_repository, /tmp/qa_variable_model_repository,
/tmp/qa_variable_sequence_model_repository, and
qa_zero_model_repository.  The TensorRT models will be created for the
GPU on the system that CUDA considers device 0 (zero). If you have
multiple GPUs on your system see the documentation in the script for
how to target a specific GPU.

Build QA Container
------------------

Next you need to build a QA version of the inference server
container. This container will contain the inference server, the QA
tests, and all the dependencies needed to run the QA tests. You must
first build the tensorrtserver_client, tensorrtserver_build and
tensorrtserver containers as described in
:ref:`section-getting-the-client-libraries-and-examples` and
:ref:`section-building-the-server` and then build the QA container::

  $ docker build -t tensorrtserver_qa -f Dockerfile.QA .

Run QA Container
----------------

Now run the QA container and mount the QA model repositories into the
container so the tests will be able to access them::

  $ nvidia-docker run -it --rm -v/tmp:/data/inferenceserver tensorrtserver_qa

Within the container the QA tests are in /opt/tensorrtserver/qa. To run a test::

  $ cd <test directory>
  $ ./test.sh