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Conda for Tool Dependencies¶
This document describes configuring Galaxy using YAML based configuraiton options. For Galaxy instances before version 18.01, this variant of this document will be more directly relatable.
Galaxy tools (also called wrappers) have traditionally used Tool Shed package recipes to install their dependencies. These were too tightly tied to Galaxy and to the Tool Shed and so have been replaced with Conda as the package management solution of choice for newer best practice tools. The Galaxy community has taken steps to improve the tool dependency system in order to enable new features and expand its reach. Not only do Conda packages make tool dependencies more reliable and stable, they are also easier to test and faster to develop than the traditional Tool Shed package recipes. This document aims to describe these and answer frequently asked questions.
Conda is a package manager like
guix. We don’t want to argue about the relative merits of various package
managers here, in fact Galaxy supports multiple package managers and we welcome
community contributions (such as implementing a Guix package manager or
enhancing the existing brew support to bring it on par with Conda).
As a community, we have decided that Conda is the one that best fulfills the community’s current needs. The following are some of the crucial Conda features that led to this decision:
Installation of packages does not require root privileges (installation at any location the Galaxy user has write access to)
Multiple versions of software can be installed on the system
Faster and more robust package installations through pre-compiled packages (no build environment complications)
Independent of programming language (R, Perl, Python, Julia, Java, pre-compiled binaries, and more)
Easy to write recipes (1 YAML description file + 1 Bash install script)
An active, large and growing community (with more and more software authors managing their own recipes)
Extensive documentation: Conda documentation and Conda quick-start
Below we answer some common questions (collected by Lance Parsons):
1. How do I enable Conda dependency resolution for Galaxy tools?¶
The short answer is that as of 17.01, Galaxy should install Conda the first time it starts up and be configured to use it by default.
The long answer is that Galaxy’s tool dependency resolution is managed via
dependency_resolvers option in the Galaxy configuration file (
option is discussed in detail in the Dependency Resolvers
documentation. Most Galaxy administrators will be using Galaxy’s default dependency
resolvers configuration. With release 16.04, Galaxy has enabled Conda dependency
resolution by default when Conda was already installed on the system. As of 17.01, Galaxy
will also install Conda as needed when starting up. Having Conda enabled in
dependency_resolvers means that Galaxy can look for tool dependencies using the Conda
system when it attempts to run a job.
Note that the order of resolvers in the configuration option matters and that when
upgrading older Galaxy servers with existing Tool Shed tools installed, the
tool_shed_packages entry should remain first. This means that tools that have
specified Tool Shed packages as their dependencies will work without a change.
The most common configuration settings related to Conda are listed in Table 1.
See Configuration Options for the complete list. Note that these options
conda_ prefix) can also be set on a per-resolver basis under the
The location on the filesystem where Conda packages and environments are installed
Table 1: Commonly used configuration options for Conda in Galaxy.
2. How do Conda dependencies work? Where do things get installed?¶
In contrast to the Tool Shed dependency system, which was used exclusively by Galaxy, Conda is a pre-existing, independent project. With Conda, it is possible for an admin to install and manage packages without touching Galaxy at all. Galaxy can handle these dependencies for you, but admins are not required to use Galaxy for dependency management.
There are a few config options in the
galaxy.yml file (see Table 1 or
Configuration Options for more information), but by default Galaxy will install
Conda (the package manager) and the required packages in the
<tool_dependency_dir>/_conda/ directory. In this directory, Galaxy will
envs folder with all of the environments managed by Galaxy. Each
environment contains a
subdirectories, depending on the tool, which are sufficient to get a Galaxy tool
running. Galaxy simply sources this folder via Conda and makes everything
available before the tool is executed on your system.
To summarize, there are four ways to manage Conda dependencies for use
with Galaxy. For all of these options, Conda dependency management must
be configured in the
dependency_resolvers option in the
Galaxy Admin Interface - Galaxy will install Conda tool dependencies when tools are installed from the Tool Shed if the option Install resolvable dependencies under Show advanced settings on the tool install dialog is checked. Admins may also view and manage Conda dependencies via the Manage Dependencies section of the Admin interface.
Manual Install - Conda dependencies may be installed by administrators from the command line. Conda (and thus the Conda environments) should be installed in the location specified by the
conda_prefixpath (defined in
galaxy.ymland by default
<tool_dependency_dir>/_conda/directory). Galaxy will search these environments for required packages when tools are run. Conda environment names have to follow a specific naming pattern. As an example, to install samtools in version 0.1.19, the administrator can run the command:
$ conda create --name firstname.lastname@example.org samtools==0.1.19 --channel bioconda
Tools that require samtools version 0.1.19 will then be able to find and use the installed Conda package.
Automatically at tool run time - When a tool is run and a dependency is not found, Galaxy will attempt to install the dependency using Conda if
conda_auto_installis activated in the configuration.
Via the API (>= 16.07) - The Galaxy community maintains an ansible role that uses BioBlend and the Galaxy API to install tools. Additionally, the Ephemeris command shed-tools can be used to install tools via the API from the command line.
3. What is required to make use of this? Any specific packages, Galaxy revision, OS version, etc.?¶
The minimum required version of Galaxy to use Conda is 16.01, however version 17.01 or greater is recommended. The 16.07 release of Galaxy has a graphical user interface to manage packages, but this is not required to have Conda dependencies managed and used by Galaxy.
Conda packages should work on all compatible operating systems with glibc version 2.12 or newer (this includes Centos 6). So all packages will run on all major *nix operating systems newer than 2007.
4. If I have Conda enabled, what do I need to do to install tools using it? For example, how can I install the latest Trinity? And how will I know the dependencies are installed?¶
This depends on your
galaxy.yml settings. Starting with release 16.07, Galaxy
can automatically install the Conda package manager for you if you have enabled
conda_auto_init. Galaxy can then install Trinity along with its dependencies
using one of the methods listed in question 2 above. In particular, if
true and Trinity is not installed yet, Galaxy will
try to install it via Conda when a Trinity job is launched.
With release 16.07 you can see which dependencies are being used
in the “Manage installed tools” section of the Admin panel and you can select
whether or not to install Conda packages or Tool Shed package recipes when you
install new tools there, even if
conda_auto_install is disabled.
During a tool installation, the Galaxy admin has control over which systems will be used to install the tool requirements. The default settings will trigger installation of both Tool Shed and Conda packages (if Conda is present), thus depending on the dependency resolvers configuration with regards to what will actually be used during the tool execution.
To check if Galaxy has created a Trinity environment, have a look at folders under
<conda_prefix>/envs if you have changed
conda_prefix in your
We recommend to use Conda on a tool-per-tool basis, by unchecking the checkbox for Tool Shed dependencies during the tool installation, and for tools where there are no available Tool Shed dependencies.
5. Can I mix traditional Galaxy packages and Conda packages?¶
Yes, the way this works is that Galaxy goes through the list of requirements for a tool, and then determines for each requirement if it can be satisfied by any of the active resolver systems.
The order in which resolvers are tried is listed in the
dependency_resolvers configuration option. The default order is
Tool Shed packages
Packages manually installed by administrators
The first system that satisfies a requirement will be used. See Dependency Resolvers for detailed documentation.
This however is not recommended, ideally tools will target and test against Conda for all dependencies. Also resolving all requirements with Conda gives Conda a chance to select compatible versions of dependencies. Read more about selecting compatible versions on Issue #3299 and Pull Request #3391.
6. How do I know what system is being used by a given tool?¶
The Galaxy log will show which dependency resolution system is used
to satisfy each tool dependency and you can specify priorities using the
dependency_resolvers configuration option (see question 5 above). Starting from Galaxy
release 16.07, you can see which dependency will be used (“resolved”) in the
Admin panel (under Tool Management → Manage dependencies).
7. How do I go about specifying Conda dependencies for a tool? All the docs still seem to recommend (or exclusively discuss) the
The simple answer is: you don’t need to do much to make Conda work for a tool.
<requirement> tag in the tool XML file is enough. The name and the
version should correspond to a Conda package in one of the enabled channels
(which are specified by the
conda_ensure_channels directive in
galaxy.yml ). If this is the case you are ready to go. Read
more about Conda channels and browse their packages on https://anaconda.org/ url followed by the channel name (e.g.
We will gradually adjust the documentation about
deprecate it everywhere.
9. Where can I find a list of existing Conda packages that I can point to, so I don’t have to reinvent the wheel for common dependencies?¶
With Conda package manager installed on your system, run:
$ conda search <package_name> -c iuc -c conda-forge -c bioconda
This will search in all channels that are activated by default in Galaxy. If you find your package, you are ready to go. If not please create a Conda package and submit it to BioConda or get in contact with the IUC.
10. How can I create a new Conda package for a dependency?¶
Adding a package to the BioConda or IUC Conda channels will make it available for Galaxy tools to use as a dependency. To learn how, get in touch with the awesome BioConda community. They have great documentation and assist with all development. You will also see a few of us at this project to get you started :)
Don’t be scared! Conda recipes are really simple to write. Conda also offers so called `skeleton` generators that generate recipes from pypi, cran, or cpan for you (mostly) automatically.
11. Is there a way to convert traditional Tool Shed package recipes that are not yet in a Conda channel?¶
First, you do not need to do anything to your wrapper as long as the package name in the requirement tag matches the name of correct Conda package. (You may want to mention in the README or a comment the Conda channel that contains the package).
If you want to migrate some recipes from XML to Conda, IUC is happy to give you a hand. We are trying to get all new versions under Conda and leave the old versions as they are – simply because of time.
12. What is the recommendation for existing installations? Will I continue to maintain both systems or migrate to the new Conda system eventually?¶
Old tools will use the traditional installation system; this system will stay and will be supported for installing old tools to guarantee sustainability and reproducibility. New tools from the IUC and other best practices sources are Conda only.
13. What can I do about this placehold error?¶
If you see a warning similar to the following in your galaxy log files:
ERROR: placeholder '/home/ray/r_3_3_1-x64-3.5/envs/_build_placehold_placehold_placehold_placehold_pl' too short
This means you are very likely using an older version of Conda. This bug has been fixed with the Conda release that is targeted by Galaxy 17.01 or newer.
In the past, the work around for this limitation, was to make sure that the total length
job_working_directory path was less than 50
14. What can I do about this LOCKERROR error?¶
This question addresses workaround for Conda if something like the following message appears in your logs:
Error: LOCKERROR: It looks like conda is already doing something. The lock ['/galaxy/galaxy-app/tool-dependencies/_conda/pkgs/.conda_lock-119903'] was found. Wait for it to finish before continuing. If you are sure that conda is not running, remove it and try again. You can also use: $ conda clean --lock
First, you may wish to enable cached dependencies. This can be done by setting
galaxy.yml. Without this
option, many jobs will create a per-job Conda environment with just the
dependencies needed for that job installed.
This will be placed on the filesystem containing the job working directory. This
is an expensive operation and Conda doesn’t always link environments correctly
across filesystems. Enabling this dependency caching will create a cache
directory for each required combination of requirements inside the directory
galaxy.yml (defaulting to
The cached dependency manager was added to the 16.10 release of Galaxy (see Pull Request #3106). In 17.01 Galaxy was updated to build the cached dependencies as needed if the caching is in fact enabled (see Pull Request #3348) and reduced the number of jobs that would require such caching (see Pull Request #3391).
15. What can I do about linking errors?¶
If Galaxy jobs run on filesystems that cannot hardlink Conda packages managed by Galaxy, linking errors may occur when building environment to execute jobs. There are a few ways to potentially work around this.
The most straight forward and efficient work around is probably just to enable the cached dependency manager as described in the previous question. Notice the default location of the cache is right next to the default Conda directory - so hardlinks should lie on the same file system as the default Conda installation.
If this still doesn’t work, perhaps the underlying file system does not support hard
linking at all. In this case it is best to add
always_softlink: True to Galaxy’s
condarc file, this should be created by Galaxy and placed in
<tool_dependency_dir/_condarc. This requires Conda 4.3 or newer. Note this is a
newer version of Conda than shipped with Galaxy as of 17.01. See the question below
on upgrading Conda if you must use this trick.
Alternatively, copying can be used when creating environments instead of links (either
symbolic or hard). To enable this set
galaxy.yml. This requires at least version 16.07 of Galaxy.
More reading on this can be found at Conda Pull Request #3870, Conda Issue #3308, and Galaxy Issue #3193.
16. What can I do if Conda doesn’t work for me?¶
Please review the common problems covered in the previous few questions, if your problem is different more investigation will be needed.
In rare cases Conda may not have been properly installed by Galaxy.
A symptom for this is if there is no activate script in
<conda_prefix>/bin folder. In that case you can delete the
and restart Galaxy, which will again attempt to install Conda.
If this does not solve your problem or you have any trouble following the instructions, please ask on the Galaxy developing mailing list or the Galaxy Gitter or IRC channel.
17. How can I upgrade Conda?¶
Many potential issues with Conda have been resolved with fixes in Conda itself. The Conda installed by Galaxy can be updated to e.g. version 4.6.14 with the following command:
$ <tool_dependency_dir>/_conda/bin/conda install -c conda-forge conda==4.6.14
The command can obviously be adapted to install any version of Conda. If the above command fails with an error like:
UnsatisfiableError: The following specifications were found to be in conflict: - conda ==4.6.14 -> python >=3.6,<3.7.0a0 - python 3.5* Use "conda info <package>" to see the dependencies for each package.
Then you need to also update the
python package installed in the base
environment by appending to the
conda install command above an appropriate
specification, which for the example error above would be
$ <tool_dependency_dir>/_conda/bin/conda install -c conda-forge conda==4.6.14 python==3.6