Installation Packaging

pip install studioml from the master pypi repositry:

pip install studioml

or, install the source and development environment for Studio from the git project directory:

git clone && cd studio && pip install -e .

A is included in the top level of the git repository to allow the creation of tar archives for installation on runners and other systems where git is not the primary means of handling Python artifacts. To create the installable, use the following command from the top level directory of a cloned repository:

python sdist

This command will create a file dist/studio-x.x.tar.gz that can be used with pip as follows:

pip install studio-x.x.tar.gz

Certain types of runners can make use of the Studio software distribution to start projects without any intervention, i.e. devops-less runners. To include the software distribution, add the tar.gz file to your workspace directory under a dist subdirectory. Runners supporting software distribution will unroll the software and install it using virtualenv.

We recommend setting up a virtual environment.

CI/CD pipeline

The Studio project distributes official releases using a travis based build and deploy pipeline. The Travis project that builds the official github repository for Studio has associated encrypted user and password credentials that the Travis .yml file refers to. These secrets can be updated using the Travis configuration found at The PYPI_PASSWORD and PYPI_USER variables should point at an owner account for the project. To rotate these values, remove the old ones using the settings page and re-add the same variables with new values.

When code is pushed to the master branch in the github repository, a traditional build will be performed by Travis. To push a release after the build is complete, add a server compatible version number as a tag to the repository and do a ‘git push –tags’ to trigger the deployment to pypi. Non-tagged builds are never pushed to pypi. Any tag will result in a push to pypi, so care should be taken to manage the visible versions using the PYPI_USER account.

Release process

Studio is released as a binary or source distribution using a hosted package at To release Studio, you must have administrator role access to the Studio Package on the web site. Releases are done using the setup packaging found inside the files.

When working with the pypi command line tooling you should create a ~/.pyirc file with your account details, for example:


repository =
username = {your pipy account}
password = {your password}

username = {your pipy account}

The command to push a release is as follows.

python sdist upload

If you wish to test releases and not pollute our pypi production release train and numbering, please use the ‘-r’ option to specify the test pypi repository. pypi releases are idempotent.

Running tests

To run the unit and regression tests, run

python $(which nosetests) --processes=8 --process-timeout=600

Note that simply running nosetests tends to not use virtualenv correctly. If you have application credentials configured to work with distributed queues and cloud workers, those will be tested as well. Otherwise, such tests will be skipped. The total test runtime, when run in parallel as in the command above, should be no more than 10 minutes. Most of the tests are I/O limited, so parallel execution speeds up things quite a bit. The longest test is the gpu cloud worker test in EC2 cloud (takes about 500 seconds due to installation of the drivers / CUDA on the EC2 instance).