Common workflow language reference implementation
This is the reference implementation of the Common Workflow Language. It is intended to be feature complete and provide comprehensive validation of CWL files as well as provide other tools related to working with CWL.
This is written and tested for Python 2.7.
The reference implementation consists of two packages. The “cwltool” package is the primary Python module containing the reference implementation in the “cwltool” module and console executable by the same name.
The “cwlref-runner” package is optional and provides an additional entry point under the alias “cwl-runner”, which is the implementation-agnostic name for the default CWL interpreter installed on a host.
Installing the official package from PyPi (will install “cwltool” package as well):
pip install cwlref-runner
If installing alongside another CWL implementation then:
pip install cwltool
To install from source:
git clone https://github.com/common-workflow-language/cwltool.git cd cwltool && python setup.py install cd cwlref-runner && python setup.py install # co-installing? skip this
Remember, if co-installing multiple CWL implementations then you need to maintain which implementation cwl-runner points to via a symbolic file system link or another facility.
Running tests locally
- Running basic tests (/tests):
python setup.py test
- Running the entire suite of CWL conformance tests:
The GitHub repository for the CWL specifications contains a script that tests a CWL implementation against a wide array of valid CWL files using the cwltest program
Instructions for running these tests can be found in the Common Workflow Language Specification repository at https://github.com/common-workflow-language/common-workflow-language/blob/master/CONFORMANCE_TESTS.md
Run on the command line
cwl-runner [tool-or-workflow-description] [input-job-settings]
Or if you have multiple CWL implementations installed and you want to override the default cwl-runner use:
cwltool [tool-or-workflow-description] [input-job-settings]
Use with boot2docker
boot2docker is running docker inside a virtual machine and it only mounts Users on it. The default behavior of CWL is to create temporary directories under e.g. /Var which is not accessible to Docker containers.
To run CWL successfully with boot2docker you need to set the --tmpdir-prefix and --tmp-outdir-prefix to somewhere under /Users:
$ cwl-runner --tmp-outdir-prefix=/Users/username/project --tmpdir-prefix=/Users/username/project wc-tool.cwl wc-job.json
Tool or workflow loading from remote or local locations
cwltool can run tool and workflow descriptions on both local and remote systems via its support for HTTP[S] URLs.
Input job files and Workflow steps (via the
run directive) can reference CWL
documents using absolute or relative local filesytem paths. If a relative path
is referenced and that document isn’t found in the current directory then the
following locations will be searched:
Use with GA4GH Tool Registry API
Cwltool can launch tools directly from GA4GH Tool Registry API endpoints.
By default, cwltool searches https://dockstore.org/ . Use –add-tool-registry to add other registries to the search path.
cwltool --non-strict quay.io/collaboratory/dockstore-tool-bamstats:master test.json
and (defaults to latest when a version is not specified)
cwltool --non-strict quay.io/collaboratory/dockstore-tool-bamstats test.json
For this example, grab the test.json (and input file) from https://github.com/CancerCollaboratory/dockstore-tool-bamstats
Import as a module
to your script.
The easiest way to use cwltool to run a tool or workflow from Python is to use a Factory:
import cwltool.factory fac = cwltool.factory.Factory() echo = f.make("echo.cwl") result = echo(inp="foo") # result["out"] == "foo"
Leveraging SoftwareRequirements (Beta)
CWL tools may be decoarated with SoftwareRequirement hints that cwltool may in turn use to resolve to packages in various package managers or dependency management systems such as Environment Modules.
Utilizing SoftwareRequirement hints using cwltool requires an optional dependency, for this reason be sure to use specify the deps modifier when installing cwltool. For instance:
$ pip install 'cwltool[deps]'
Installing cwltool in this fashion enables several new command line options. The most general of these options is --beta-dependency-resolvers-configuration. This option allows one to specify a dependency resolvers configuration file. This file may be specified as either XML or YAML and very simply describes various plugins to enable to “resolve” SoftwareRequirement dependencies.
To discuss some of these plugins and how to configure them, first consider the following hint definition for an example CWL tool.
SoftwareRequirement: packages: - package: seqtk version: - r93
Now imagine deploying cwltool on a cluster with Software Modules installed and that a seqtk module is avaialble at version r93. This means cluster users likely won’t have the seqtk the binary on their PATH by default but after sourcing this module with the command modulecmd sh load seqtk/r93 seqtk is available on the PATH. A simple dependency resolvers configuration file, called dependency-resolvers-conf.yml for instance, that would enable cwltool to source the correct module environment before executing the above tool would simply be:
- type: module
The outer list indicates that one plugin is being enabled, the plugin parameters are defined as a dictionary for this one list item. There is only one required parameter for the plugin above, this is type and defines the plugin type. This parameter is required for all plugins. The available plugins and the parameters available for each are documented (incompletely) here. Unfortunately, this documentation is in the context of Galaxy tool requirement s instead of CWL SoftwareRequirement s, but the concepts map fairly directly.
cwltool is distributed with an example of such seqtk tool and sample corresponding job. It could executed from the cwltool root using a dependency resolvers configuration file such as the above one using the command:
cwltool --beta-dependency-resolvers-configuration /path/to/dependency-resolvers-conf.yml \ tests/seqtk_seq.cwl \ tests/seqtk_seq_job.json
This example demonstrates both that cwltool can leverage existing software installations and also handle workflows with dependencies on different versions of the same software and libraries. However the above example does require an existing module setup so it is impossible to test this example “out of the box” with cwltool. For a more isolated test that demonstrates all the same concepts - the resolver plugin type galaxy_packages can be used.
“Galaxy packages” are a lighter weight alternative to Environment Modules that are really just defined by a way to lay out directories into packages and versions to find little scripts that are sourced to modify the environment. They have been used for years in Galaxy community to adapt Galaxy tools to cluster environments but require neither knowledge of Galaxy nor any special tools to setup. These should work just fine for CWL tools.
The cwltool source code repository’s test directory is setup with a very simple directory that defines a set of “Galaxy packages” (but really just defines one package named random-lines). The directory layout is simply:
tests/test_deps_env/ random-lines/ 1.0/ env.sh
If the galaxy_packages plugin is enabled and pointed at the tests/test_deps_env directory in cwltool’s root and a SoftwareRequirement such as the following is encountered.
hints: SoftwareRequirement: packages: - package: 'random-lines' version: - '1.0'
Then cwltool will simply find that env.sh file and source it before executing the corresponding tool. That env.sh script is only responsible for modifying the job’s PATH to add the required binaries.
This is a full example that works since resolving “Galaxy packages” has no external requirements. Try it out by executing the following command from cwltool’s root directory:
cwltool --beta-dependency-resolvers-configuration tests/test_deps_env_resolvers_conf.yml \ tests/random_lines.cwl \ tests/random_lines_job.json
The resolvers configuration file in the above example was simply:
- type: galaxy_packages base_path: ./tests/test_deps_env
It is possible that the SoftwareRequirement s in a given CWL tool will not
match the module names for a given cluster. Such requirements can be re-mapped
to specific deployed packages and/or versions using another file specified using
the resolver plugin parameter
mapping_files. We will
demonstrate this using
galaxy_packages but the concepts apply equally well
to Environment Modules or Conda packages (described below) for instance.
So consider the resolvers configuration file
- type: galaxy_packages base_path: ./tests/test_deps_env mapping_files: ./tests/test_deps_mapping.yml
And the corresponding mapping configuraiton file (
- from: name: randomLines version: 1.0.0-rc1 to: name: random-lines version: '1.0'
This is saying if cwltool encounters a requirement of randomLines at version 1.0.0-rc1 in a tool, to rewrite to our specific plugin as random-lines at version 1.0. cwltool has such a test tool called random_lines_mapping.cwl that contains such a source SoftwareRequirement. To try out this example with mapping, execute the following command from the cwltool root directory:
cwltool --beta-dependency-resolvers-configuration tests/test_deps_env_resolvers_conf_rewrite.yml \ tests/random_lines_mapping.cwl \ tests/random_lines_job.json
The previous examples demonstrated leveraging existing infrastructure to provide requirements for CWL tools. If instead a real package manager is used cwltool has the oppertunity to install requirements as needed. While initial support for Homebrew/Linuxbrew plugins is available, the most developed such plugin is for the Conda package manager. Conda has the nice properties of allowing multiple versions of a package to be installed simultaneously, not requiring evalated permissions to install Conda itself or packages using Conda, and being cross platform. For these reasons, cwltool may run as a normal user, install its own Conda environment and manage multiple versions of Conda packages on both Linux and Mac OS X.
The Conda plugin can be endlessly configured, but a sensible set of defaults that has proven a powerful stack for dependency management within the Galaxy tool development ecosystem can be enabled by simply passing cwltool the --beta-conda-dependencies flag.
With this we can use the seqtk example above without Docker and without any externally managed services - cwltool should install everything it needs and create an environment for the tool. Try it out with the follwing command:
cwltool --beta-conda-dependencies tests/seqtk_seq.cwl tests/seqtk_seq_job.json
The CWL specification allows URIs to be attached to SoftwareRequirement s that allow disambiguation of package names. If the mapping files described above allow deployers to adapt tools to their infrastructure, this mechanism allows tools to adapt their requirements to multiple package managers. To demonstrate this within the context of the seqtk, we can simply break the package name we use and then specify a specific Conda package as follows:
hints: SoftwareRequirement: packages: - package: seqtk_seq version: - '1.2' specs: - https://anaconda.org/bioconda/seqtk - https://packages.debian.org/sid/seqtk
The example can be executed using the command:
cwltool --beta-conda-dependencies tests/seqtk_seq_wrong_name.cwl tests/seqtk_seq_job.json
The plugin framework for managing resolution of these software requirements as maintained as part of galaxy-lib - a small, portable subset of the Galaxy project. More information on configuration and implementation can be found at the following links:
Cwltool control flow
Technical outline of how cwltool works internally, for maintainers.
- Use CWL
load_tool()to load document.
- Fetches the document from file or URL
- Applies preprocessing (syntax/identifier expansion and normalization)
- Validates the document based on cwlVersion
- If necessary, updates the document to latest spec
- Constructs a Process object using
make_tool()callback. This yields a CommandLineTool, Workflow, or ExpressionTool. For workflows, this recursively constructs each workflow step.
- To construct custom types for CommandLineTool, Workflow, or
ExpressionTool, provide a custom
- Iterate on the
job()method of the Process object to get back runnable jobs.
job()is a generator method (uses the Python iterator protocol)
- Each time the
job()method is invoked in an iteration, it returns one of: a runnable item (an object with a
None(indicating there is currently no work ready to run) or end of iteration (indicating the process is complete.)
- Invoke the runnable item by calling
run(). This runs the tool and gets output.
- Output of a process is reported by an output callback.
job()may be iterated over multiple times. It will yield all the work that is currently ready to run and then yield None.
- “Workflow” objects create a corresponding “WorkflowJob” and “WorkflowJobStep” objects to hold the workflow state for the duration of the job invocation.
- The WorkflowJob iterates over each WorkflowJobStep and determines if the inputs the step are ready.
- When a step is ready, it constructs an input object for that step and
iterates on the
job()method of the workflow job step.
- Each runnable item is yielded back up to top level run loop
- When a step job completes and receives an output callback, the job outputs are assigned to the output of the workflow step.
- When all steps are complete, the intermediate files are moved to a final workflow output, intermediate directories are deleted, and the output callback for the workflow is called.
- “CommandLineTool” job() objects yield a single runnable object.
- The CommandLineTool
makeJobRunner()to create a
- The job method configures the CommandLineJob object by setting public attributes
- The job method iterates over file and directories inputs to the CommandLineTool and creates a “path map”.
- Files are mapped from their “resolved” location to a “target” path where they will appear at tool invocation (for example, a location inside a Docker container.) The target paths are used on the command line.
- Files are staged to targets paths using either Docker volume binds (when using containers) or symlinks (if not). This staging step enables files to be logically rearranged or renamed independent of their source layout.
- The run() method of CommandLineJob executes the command line tool or Docker container, waits for it to complete, collects output, and makes the output callback.
- The CommandLineTool
The following functions can be provided to main(), to load_tool(), or to the executor to override or augment the listed behaviors.
executor(tool, job_order_object, **kwargs) (Process, Dict[Text, Any], **Any) -> Tuple[Dict[Text, Any], Text]
A toplevel workflow execution loop, should synchronously execute a process object and return an output object.
makeTool(toolpath_object, **kwargs) (Dict[Text, Any], **Any) -> Process
Construct a Process object from a document.
selectResources(request) (Dict[Text, int]) -> Dict[Text, int]
Take a resource request and turn it into a concrete resource assignment.
() () -> Text
Return version string.
make_fs_access(basedir) (Text) -> StdFsAccess
Return a file system access object.
fetcher_constructor(cache, session) (Dict[unicode, unicode], requests.sessions.Session) -> Fetcher
Construct a Fetcher object with the supplied cache and HTTP session.
resolver(document_loader, document) (Loader, Union[Text, dict[Text, Any]]) -> Text
Resolve a relative document identifier to an absolute one which can be fetched.
Handler object for logging.
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