Local Development¶
Go through the following sequence of commands:
$ git co https://gitlab.idiap.ch/bob/bob.admin
$ #install miniconda (version 4.4 or above required)
$ conda activate base
$ cd beat.backend.python #cd into this package's sources
$ ../bob.admin/conda/conda-bootstrap.py --overwrite --python=2.7 beat-core-dev
$ conda activate beat-core-dev
$ #n.b.: docker must be installed on your system (see next section)
$ buildout -c develop.cfg
These commands should download and install all non-installed dependencies and get you a fully operational test and development environment.
Docker¶
This package depends on Docker and may use it to run user algorithms in a container with the required software stack. You must install the Docker engine and make sure the user running tests has access to it.
In particular, this package controls memory and CPU utilisation of the containers it launches. You must make sure to enable those functionalities on your installation.
Docker Setup¶
Make sure you have the docker
command available on your system. For certain
operating systems, it is necessary to install docker
via an external
virtual machine (a.k.a. the docker machine). Follow the instructions at the
docker website <https://docs.docker.com/engine/installation/> before trying to
execute algorithms or experiments.
We use specific docker images to run user algorithms. Download the following base images before you try to run tests or experiments on your computer:
$ docker pull docker.idiap.ch/beat/beat.env.system.python:1.1.2
$ docker pull docker.idiap.ch/beat/beat.env.db.examples:1.1.1
$ docker pull docker.idiap.ch/beat/beat.env.client:1.2.0
$ docker pull docker.idiap.ch/beat/beat.env.cxx:1.0.2
Optionally, also download the following images to be able to re-run experiments downloaded from the BEAT platform (not required for unit testing):
$ docker pull docker.idiap.ch/beat/beat.env.python:0.0.4
$ docker pull docker.idiap.ch/beat/beat.env.python:1.0.0
$ docker pull docker.idiap.ch/beat/beat.env.db:1.2.2
Testing¶
After installation, it is possible to run our suite of unit tests. To do so,
use nose
:
$ ./bin/nosetests -sv
Note
Some of the tests for our command-line toolkit require a running BEAT
platform web-server, with a compatible beat.core
installed (preferably
the same). By default, these tests will be skipped. If you want to run
them, you must setup a development web server and set the environment
variable BEAT_CORE_TEST_PLATFORM
to point to that address. For example:
$ export BEAT_CORE_TEST_PLATFORM="http://example.com/platform/"
$ ./bin/nosetests -sv
Warning
Do NOT run tests against a production web server.
If you want to skip slow tests (at least those pulling stuff from our servers) or executing lengthy operations, just do:
$ ./bin/nosetests -sv -a '!slow'
To measure the test coverage, do the following:
$ ./bin/nosetests -sv --with-coverage --cover-package=beat.core
Our documentation is also interspersed with test units. You can run them using sphinx:
$ ./bin/sphinx -b doctest doc sphinx
Other bits¶
Profiling¶
In order to profile the test code, try the following:
$ ./bin/python -mcProfile -oprof.data ./bin/nosetests -sv ...
This will dump the profiling data at prof.data
. You can dump its contents
in different ways using another command:
$ ./bin/python -mpstats prof.data
This will allow you to dump and print the profiling statistics as you may find fit.