This document is for an in-development version of Galaxy. You can alternatively view this page in the latest release if it exists or view the top of the latest release's documentation.
Galaxy is a large Python application with a long list of Python module dependencies. As a result, the Galaxy developers have made significant effort to provide these dependencies in as simple a method as possible while remaining compatible with the Python packaging best practices. Thus, Galaxy’s runtime setup procedure makes use of virtualenv for package environment isolation, pip for installation, and wheel to provide pre-built versions of dependencies.
In addition to framework dependencies, since Galaxy 18.09, the client (UI) is no longer provided in its built format when downloading Galaxy. For more information, see https://github.com/galaxyproject/galaxy/blob/dev/client/README.md .
How it works¶
Upon startup (with
run.sh), the startup scripts will:
Create a Python virtualenv in the directory
$PYTHONPATHenvironment variable (if set) as this can interfere with installing dependencies.
Start Galaxy using
A variety of options to
run.sh are available to control the above behavior:
--skip-venv: Do not create or use a virtualenv.
--skip-wheels: Do not install wheels.
--no-create-venv: Do not create a virtualenv, but use one if it exists at
$VIRTUAL_ENVis set (this variable is set by virtualenv’s
Managing dependencies manually¶
Create a virtualenv¶
Using a virtualenv in
.venv under the Galaxy source tree is not required. More complicated Galaxy setups may
choose to use a virtualenv external to the Galaxy source tree, which can be done either by not using
(an example of this can be found under the Scaling and Load Balancing documentation) or using the
option, explained in the Options section. It is also possible to force Galaxy to start without a virtualenv at all,
but you should not do this unless you know what you’re doing.
To manually create a virtualenv, you will first need to obtain virtualenv. There are a variety of ways to do this:
pip install virtualenv
brew install virtualenv
Install your Linux distribution’s virtualenv package from the system package manager (e.g.
apt-get install python-virtualenv).
Download the virtualenv source from PyPI, untar, and run the
virtualenv.pyscript contained within as
python virtualenv.py /path/to/galaxy/virtualenv
Once this is done, create a virtualenv. In our example, the virtualenv will live in
/srv/galaxy/venv and the Galaxy
source code has been cloned to
$ virtualenv /srv/galaxy/venv New python executable in /srv/galaxy/venv/bin/python Installing setuptools, pip, wheel...done. $ . /srv/galaxy/venv/bin/activate (venv)$
run.sh calls common_startup.sh, which creates the virtualenv and installs dependencies. You can call
this script yourself to set up Galaxy pip and the dependencies without creating a virtualenv using the
(venv)$ PYTHONPATH= sh /srv/galaxy/server/scripts/common_startup.sh --no-create-venv
$PYTHONPATH is set, it may interfere with the dependency installation process. Without
$PYTHONPATH variable will be automatically unset, but we assume you know what you’re
doing and may want it left intact if you are using
--no-create-venv. If you encounter problems, try unsetting
$PYTHONPATH as shown in the example above.
Dependency management complications¶
Certain deployment scenarios or other software may complicate Galaxy dependency management. If you use any of these, relevant information can be found in the corresponding subsection below.
Galaxy job handlers¶
All Galaxy jobs run a metadata detection step on the job outputs upon completion of the tool. The metadata detection
step requires many of Galaxy’s dependencies. Because of this, it’s necessary to make sure the metadata detection step
runs in Galaxy’s virtualenv. If you run a relatively simple Galaxy deployment (e.g.
run.sh) then this is assured for
you automatically. In more complicated setups (running under supervisor and/or the virtualenv used to start Galaxy is
not on a shared filesystem) it may be necessary to make sure the handlers know where the virtualenv (or a virtualenv
containing Galaxy’s dependencies) can be found.
If the virtualenv cannot be located, you will see job failures due to Python
ImportError exceptions, like so:
Traceback (most recent call last): File "/srv/galaxy/tmp/job_working_directory/001/set_metadata_RK41sy.py", line 1, in <module> from galaxy_ext.metadata.set_metadata import set_metadata; set_metadata() File "/srv/galaxy/server/lib/galaxy_ext/metadata/set_metadata.py", line 23, in <module> from sqlalchemy.orm import clear_mappers ImportError: No module named sqlalchemy.orm
If this is the case, you can instruct jobs to activate the virtualenv with an
env tag in
<destination id="cluster" runner="drmaa"> <!-- ... other destination params --> <env file="/cluster/galaxy/venv/bin/activate" /> </destination>
If your Galaxy server has a different Python version installed than the one on the cluster worker nodes, you might encounter an error containing this message:
File "/usr/lib/python2.7/weakref.py", line 14, in <module> from _weakref import ( ImportError: cannot import name _remove_dead_weakref
If you encounter this error or your Galaxy server’s virtualenv isn’t available on the cluster you can create one manually using the instructions under Managing dependencies manually and activate it using the above-mentioned
env tag in
If using Pulsar’s option to set metadata on the remote server, the same conditions as with Galaxy job handlers
apply. You should create a virtualenv on the remote resource, install Galaxy’s dependencies in to it, and set an
<env> tag pointing to the virtualenv’s
activate as in the Galaxy job handlers section. Instructions on how to
create a virtualenv can be found under the Managing dependencies manually section.
These instruction apply to Galaxy release 19.01 or newer. Please consult the documentation for your version of Galaxy.
Conda and virtualenv are incompatible, unless an adapted
virtualenv package from the conda-forge channel is used.
Galaxy can create a virtualenv using the adapted virtualenv package. Once a valid
.venv environment exists it will be used.
If you would like to use a virtualenv created by Conda, the simplest method is:
.venvdoes not exist.
condaon your PATH if it isn’t.
Start galaxy using
sh run.shor execute
A Conda environment named
_galaxy_ will be created and the appropriate virtualenv package will be installed into this environment.
Using this environment a
.venv is initialized. This is a one-time setup, and all other activation and dependency
management happens exactly as if a system Python was used for creating
Adding additional Galaxy dependencies¶
If wheels already exist on PyPI for all platforms and Python versions supported by Galaxy, you can skip to step 3 in the process below.
Clone https://github.com/galaxyproject/wheelforge/ and add or edit the meta.yaml file for the package you would like to build.
Submit a pull request to Wheelforge.
Add the new dependency to the [tool.poetry.dependencies] (or to [tool.poetry.group.dev.dependencies] if only needed for Galaxy development) section of pyproject.toml .
Run make update-dependencies to update the requirements file in lib/galaxy/dependencies.
Submit a pull request to Galaxy with your changes.