Framework Dependencies

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 .

How it works

Upon startup (with, the startup scripts will:

  1. Create a Python virtualenv in the directory .venv.

  2. Unset the $PYTHONPATH environment variable (if set) as this can interfere with installing dependencies.

  3. Download and install packages from the Galaxy project wheel server, as well as the Python Package Index (aka PyPI) , using pip.

  4. Start Galaxy using .venv/bin/python.


A variety of options to 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 .venv or if $VIRTUAL_ENV is set (this variable is set by virtualenv’s activate).

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 directly (an example of this can be found under the Scaling and Load Balancing documentation) or using the --no-create-venv 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 script contained within as python /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 /srv/galaxy/server.

$ virtualenv /srv/galaxy/venv
New python executable in /srv/galaxy/venv/bin/python
Installing setuptools, pip, wheel...done.
$ . /srv/galaxy/venv/bin/activate

Install dependencies

Normally, calls, 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 --no-create-venv option:

(venv)$ PYTHONPATH= sh /srv/galaxy/server/scripts/ --no-create-venv


If your $PYTHONPATH is set, it may interfere with the dependency installation process. Without --no-create-venv the $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. 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/", line 1, in <module>
        from galaxy_ext.metadata.set_metadata import set_metadata; set_metadata()
  File "/srv/galaxy/server/lib/galaxy_ext/metadata/", 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 job_conf.xml:

<destination id="cluster" runner="drmaa">
    <!-- ... other destination params -->
    <env file="/cluster/galaxy/venv/bin/activate" />

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/", 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 job_conf.xml.


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:

  1. Ensure .venv does not exist.

  2. Place conda on your PATH if it isn’t.

  3. Start galaxy using sh or execute sh scripts/

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 .venv.

Adding additional Galaxy dependencies

New packages can be added to Galaxy, or the versions of existing packages can be updated, using poetry and Wheelforge.

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.

  1. Clone and add or edit the meta.yaml file for the package you would like to build.

  2. Submit a pull request to Wheelforge.

  3. Add the new dependency to the [tool.poetry.dependencies] (or to [] if only needed for Galaxy development) section of pyproject.toml .

  4. Run make update-dependencies to update the requirements file in lib/galaxy/dependencies.

  5. Submit a pull request to Galaxy with your changes.