# `cwltool`: The reference implementation of the Common Workflow Language standards

This is the reference implementation of the Common Workflow Language open standards. 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.

`cwltool` is written and tested for Python `3.x {x = 9, 10, 11, 12, 13}`

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.

`cwltool` is provided by the CWL project, a member project of Software Freedom Conservancy and our many contributors.

## Install

### `cwltool` packages

Your operating system may offer cwltool directly. For Debian, Ubuntu, and similar Linux distribution try

    sudo apt-get install cwltool

If you encounter an error, first try to update package information by using

    sudo apt-get update

If you are running macOS X or other UNIXes and you want to use packages prepared by the conda-forge project, then please follow the install instructions for conda-forge (if you haven’t already) and then

    conda install -c conda-forge cwltool

All of the above methods of installing `cwltool` use packages that might contain bugs already fixed in newer versions or be missing desired features. If the packaged version of `cwltool` available to you is too old, then we recommend installing using `pip` and `venv`

    python3 -m venv env      # Create a virtual environment named 'env' in the current directory
    source env/bin/activate  # Activate environment before installing `cwltool`

Then install the latest `cwlref-runner` package from PyPi (which will install the latest `cwltool` package as well)

    pip install cwlref-runner

If installing alongside another CWL implementation (like `toil-cwl-runner` or `arvados-cwl-runner`) then instead run

    pip install cwltool

### MS Windows users

1. Install Windows Subsystem for Linux 2 and Docker Desktop.

2. Install Debian from the Microsoft Store.

3. Set Debian as your default WSL 2 distro: `wsl --set-default debian`.

4. Return to the Docker Desktop, choose `Settings` → `Resources` → `WSL Integration` and under “Enable integration with additional distros” select “Debian”,

5. Reboot if you have not yet already.

6. Launch Debian and follow the Linux instructions above (`apt-get install cwltool` or use the `venv` method)

Network problems from within WSL2? Try these instructions followed by `wsl --shutdown`.

### `cwltool` development version

Or you can skip the direct `pip` commands above and install the latest development version of `cwltool`:

    git clone https://github.com/common-workflow-language/cwltool.git # clone (copy) the cwltool git repository
    cd cwltool           # Change to source directory that git clone just downloaded
    pip install .[deps]  # Installs ``cwltool`` from source
    cwltool --version    # Check if the installation works correctly

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.

### Recommended Software

We strongly suggested to have the following installed:

- One of the following software container engines

  - Podman

  - Docker

  - Singularity/Apptainer: See Using Singularity

  - udocker: See Using uDocker

- node.js for evaluating CWL Expressions quickly (required for udocker users, optional but recommended for the other container engines).

Without these, some examples in the CWL tutorials at http://www.commonwl.org/user_guide/ may not work.

## Run on the command line

Simple command:

    cwl-runner my_workflow.cwl my_inputs.yaml

Or if you have multiple CWL implementations installed and you want to override the default cwl-runner then use:

    cwltool my_workflow.cwl my_inputs.yml

You can set cwltool options in the environment with `CWLTOOL_OPTIONS`, these will be inserted at the beginning of the command line:

    export CWLTOOL_OPTIONS="--debug"

### Use with boot2docker on macOS

boot2docker runs 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

### Docker default platform on macOS with Apple Silicon

If macOS users want to run CWL tools/workflows using `cwltool` with Docker and their software containers only have support for amd64 (64-bit x86) CPUs, but they have an Apple Silicon (aarch64/arm64) CPU, they run into the error:

> WARNING: The requested image’s platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested.

To fix this, export the following environment variable before executing cwltool:

`export DOCKER_DEFAULT_PLATFORM=linux/amd64`

To automatically have this variable set in the future, add the same command to ones respective shell profile (e.g. `~/.zshrc`, `~/.bash_profile`).

### Using uDocker

Some shared computing environments don’t support Docker software containers for technical or policy reasons. As a workaround, the CWL reference runner supports using the udocker program on Linux using `--udocker`.

udocker installation: https://indigo-dc.github.io/udocker/installation_manual.html

Run cwltool just as you usually would, but with `--udocker` prior to the workflow path:

    cwltool --udocker https://github.com/common-workflow-language/common-workflow-language/raw/main/v1.0/v1.0/test-cwl-out2.cwl https://github.com/common-workflow-language/common-workflow-language/raw/main/v1.0/v1.0/empty.json

As was mentioned in the Recommended Software section,

### Using Singularity

`cwltool` can also use Singularity version 2.6.1 or later as a Docker container runtime. `cwltool` with Singularity will run software containers specified in `DockerRequirement` and therefore works with Docker images only, native Singularity images are not supported. To use Singularity as the Docker container runtime, provide `--singularity` command line option to `cwltool`. With Singularity, `cwltool` can pass all CWL v1.0 conformance tests, except those involving Docker container ENTRYPOINTs.

Example

    cwltool --singularity https://github.com/common-workflow-language/common-workflow-language/raw/main/v1.0/v1.0/cat3-tool-mediumcut.cwl https://github.com/common-workflow-language/common-workflow-language/raw/main/v1.0/v1.0/cat-job.json

### Running a tool or workflow 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 filesystem paths. If a relative path is referenced and that document isn’t found in the current directory, then the following locations will be searched: http://www.commonwl.org/v1.0/CommandLineTool.html#Discovering_CWL_documents_on_a_local_filesystem

You can also use cwldep to manage dependencies on external tools and workflows.

### Overriding workflow requirements at load time

Sometimes a workflow needs additional requirements to run in a particular environment or with a particular dataset. To avoid the need to modify the underlying workflow, cwltool supports requirement “overrides”.

The format of the “overrides” object is a mapping of item identifier (workflow, workflow step, or command line tool) to the process requirements that should be applied.

    cwltool:overrides:
      echo.cwl:
        requirements:
          EnvVarRequirement:
            envDef:
              MESSAGE: override_value

Overrides can be specified either on the command line, or as part of the job input document. Workflow steps are identified using the name of the workflow file followed by the step name as a document fragment identifier “#id”. Override identifiers are relative to the top-level workflow document.

    cwltool --overrides overrides.yml my-tool.cwl my-job.yml

    input_parameter1: value1
    input_parameter2: value2
    cwltool:overrides:
      workflow.cwl#step1:
        requirements:
          EnvVarRequirement:
            envDef:
              MESSAGE: override_value

    cwltool my-tool.cwl my-job-with-overrides.yml

### Combining parts of a workflow into a single document

Use `--pack` to combine a workflow made up of multiple files into a single compound document. This operation takes all the CWL files referenced by a workflow and builds a new CWL document with all Process objects (CommandLineTool and Workflow) in a list in the `$graph` field. Cross references (such as `run:` and `source:` fields) are updated to internal references within the new packed document. The top-level workflow is named `#main`.

    cwltool --pack my-wf.cwl > my-packed-wf.cwl

### Running only part of a workflow

You can run a partial workflow with the `--target` (`-t`) option. This takes the name of an output parameter, workflow step, or input parameter in the top-level workflow. You may provide multiple targets.

    cwltool --target step3 my-wf.cwl

If a target is an output parameter, it will only run only the steps that contribute to that output. If a target is a workflow step, it will run the workflow starting from that step. If a target is an input parameter, it will only run the steps connected to that input.

Use `--print-targets` to get a listing of the targets of a workflow. To see which steps will run, use `--print-subgraph` with `--target` to get a printout of the workflow subgraph for the selected targets.

    cwltool --print-targets my-wf.cwl

    cwltool --target step3 --print-subgraph my-wf.cwl > my-wf-starting-from-step3.cwl

### Visualizing a CWL document

The `--print-dot` option will print a file suitable for Graphviz `dot` program. Here is a bash onliner to generate a Scalable Vector Graphic (SVG) file:

    cwltool --print-dot my-wf.cwl | dot -Tsvg > my-wf.svg

### Modeling a CWL document as RDF

CWL documents can be expressed as RDF triple graphs.

    cwltool --print-rdf --rdf-serializer=turtle mywf.cwl

### Environment Variables in cwltool

This reference implementation supports several ways of setting environment variables for tools, in addition to the standard `EnvVarRequirement`. The sequence of steps applied to create the environment is:

0. If the `--preserve-entire-environment` flag is present, then begin with the current environment, else begin with an empty environment.

1. Add any variables specified by `--preserve-environment` option(s).

2. Set `TMPDIR` and `HOME` per the CWL v1.0+ CommandLineTool specification.

3. Apply any `EnvVarRequirement` from the `CommandLineTool` description.

4. Apply any manipulations required by any `cwltool:MPIRequirement` extensions.

5. Substitute any secrets required by `Secrets` extension.

6. Modify the environment in response to `SoftwareRequirement` (see below).

#### Leveraging SoftwareRequirements (Beta)

CWL tools may be decorated 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 resolver’s configuration file. This file may be specified as either XML or YAML and very simply describes various plugins to enable to “resolve” `SoftwareRequirement` dependencies.

Using these hints will allow cwltool to modify the environment in which your tool runs, for example by loading one or more Environment Modules. The environment is constructed as above, then the environment may modified by the selected tool resolver. This currently means that you cannot override any environment variables set by the selected tool resolver. Note that the environment given to the configured dependency resolver has the variable _CWLTOOL set to 1 to allow introspection.

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 available at version `r93`. This means cluster users likely won’t have the binary `seqtk` 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: modules

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 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. (tests/test_deps_env_resolvers_conf_rewrite.yml):

    - type: galaxy_packages
      base_path: ./tests/test_deps_env
      mapping_files: ./tests/test_deps_mapping.yml

And the corresponding mapping configuration file (tests/test_deps_mapping.yml):

    - 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 opportunity 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 evaluated 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 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 or any externally managed services - cwltool should install everything it needs and create an environment for the tool. Try it out with the following 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 the resolution of these software requirements as maintained as part of galaxy-tool-util - a small, portable subset of the Galaxy project. More information on configuration and implementation can be found at the following links:

- Dependency Resolvers in Galaxy

- Conda for [Galaxy] Tool Dependencies

- Mapping Files - Implementation

- Specifications - Implementation

- Initial cwltool Integration Pull Request

### 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.

For example

    cwltool quay.io/collaboratory/dockstore-tool-bamstats:develop test.json

and (defaults to latest when a version is not specified)

    cwltool 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

    wget https://dockstore.org/api/api/ga4gh/v2/tools/quay.io%2Fbriandoconnor%2Fdockstore-tool-bamstats/versions/develop/PLAIN-CWL/descriptor/test.json
    wget https://github.com/CancerCollaboratory/dockstore-tool-bamstats/raw/develop/rna.SRR948778.bam

### Running MPI-based tools that need to be launched

Cwltool supports an extension to the CWL spec `http://commonwl.org/cwltool#MPIRequirement`. When the tool definition has this in its `requirements`/`hints` section, and cwltool has been run with `--enable-ext`, then the tool’s command line will be extended with the commands needed to launch it with `mpirun` or similar. You can specify the number of processes to start as either a literal integer or an expression (that will result in an integer). For example:

    #!/usr/bin/env cwl-runner
    cwlVersion: v1.1
    class: CommandLineTool
    $namespaces:
      cwltool: "http://commonwl.org/cwltool#"
    requirements:
      cwltool:MPIRequirement:
        processes: $(inputs.nproc)
    inputs:
      nproc:
        type: int

Interaction with containers: the MPIRequirement currently prepends its commands to the front of the command line that is constructed. If you wish to run a containerized application in parallel, for simple use cases, this does work with Singularity, depending upon the platform setup. However, this combination should be considered “alpha” – please do report any issues you have! This does not work with Docker at the moment. (More precisely, you get n copies of the same single process image run at the same time that cannot communicate with each other.)

The host-specific parameters are configured in a simple YAML file (specified with the `--mpi-config-file` flag). The allowed keys are given in the following table; all are optional.

Key | Type | Default | Description
--- | --- | --- | ---
runner | str | “mpirun” | The primary command to use.
nproc_flag | str | “-n” | Flag to set number of processes to start.
default_nproc | int | 1 | Default number of processes.
extra_flags | List[str] | [] | A list of any other flags to be added to the runner’s command line before the `baseCommand`.
env_pass | List[str] | [] | A list of environment variables that should be passed from the host environment through to the tool (e.g., giving the node list as set by your scheduler).
env_pass_regex | List[str] | [] | A list of python regular expressions that will be matched against the host’s environment. Those that match will be passed through.
env_set | Mapping[str,str] | {} | A dictionary whose keys are the environment variables set and the values being the values.

### Enabling Fast Parser (experimental)

For very large workflows, cwltool can spend a lot of time in initialization, before the first step runs. There is an experimental flag `--fast-parser` which can dramatically reduce the initialization overhead, however as of this writing it has several limitations:

- Error reporting in general is worse than the standard parser, you will want to use it with workflows that you know are already correct.

- It does not check for dangling links (these will become runtime errors instead of loading errors)

- Several other cases fail, as documented in https://github.com/common-workflow-language/cwltool/pull/1720

## Development

### Running tests locally

- Running basic tests `(/tests)`:

To run the basic tests after installing cwltool execute the following:

    pip install -rtest-requirements.txt
    pytest   ## N.B. This requires node.js or docker to be available

To run various tests in all supported Python environments, we use tox. To run the test suite in all supported Python environments first clone the complete code repository (see the `git clone` instructions above) and then run the following in the terminal: `pip install tox; tox -p`

List of all environment can be seen using: `tox --listenvs` and running a specific test env using: `tox run -e <env name>` and additionally run a specific test using this format: `tox run -e py310-unit -- -v tests/test_examples.py::test_scandeps`

- 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/main/CONFORMANCE_TESTS.md .

### Import as a module

Add

    import cwltool

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 = fac.make("echo.cwl")
    result = echo(inp="foo")

    # result["out"] == "foo"

### CWL Tool Control Flow

Technical outline of how cwltool works internally, for maintainers.

1. Use CWL `load_tool()` to load document.

   1. Fetches the document from file or URL

   2. Applies preprocessing (syntax/identifier expansion and normalization)

   3. Validates the document based on cwlVersion

   4. If necessary, updates the document to the latest spec

   5. Constructs a Process object using `make_tool()`` callback. This yields a CommandLineTool, Workflow, or ExpressionTool. For workflows, this recursively constructs each workflow step.

   6. To construct custom types for CommandLineTool, Workflow, or ExpressionTool, provide a custom `make_tool()`

2. Iterate on the `job()` method of the Process object to get back runnable jobs.

   1. `job()` is a generator method (uses the Python iterator protocol)

   2. Each time the `job()` method is invoked in an iteration, it returns one of: a runnable item (an object with a `run()` method), `None` (indicating there is currently no work ready to run) or end of iteration (indicating the process is complete.)

   3. Invoke the runnable item by calling `run()`. This runs the tool and gets output.

   4. An output callback reports the output of a process.

   5. `job()` may be iterated over multiple times. It will yield all the work that is currently ready to run and then yield None.

3. `Workflow` objects create a corresponding `WorkflowJob` and `WorkflowJobStep` objects to hold the workflow state for the duration of the job invocation.

   1. The WorkflowJob iterates over each WorkflowJobStep and determines if the inputs the step are ready.

   2. When a step is ready, it constructs an input object for that step and iterates on the `job()` method of the workflow job step.

   3. Each runnable item is yielded back up to top-level run loop

   4. When a step job completes and receives an output callback, the job outputs are assigned to the output of the workflow step.

   5. When all steps are complete, the intermediate files are moved to a final workflow output, intermediate directories are deleted, and the workflow’s output callback is called.

4. `CommandLineTool` job() objects yield a single runnable object.

   1. The CommandLineTool `job()` method calls `make_job_runner()` to create a `CommandLineJob` object

   2. The job method configures the CommandLineJob object by setting public attributes

   3. The job method iterates over file and directories inputs to the CommandLineTool and creates a “path map”.

   4. 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.

   5. 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.

   6. 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.

### Extension points

The following functions can be passed to main() to override or augment the listed behaviors.

executor

      executor(tool, job_order_object, runtimeContext, logger)
        (Process, Dict[str, Any], RuntimeContext) -> Tuple[Dict[str, Any], str]

  An implementation of the top-level workflow execution loop should synchronously run a process object to completion and return the output object.

versionfunc

      ()
        () -> str

  Return version string.

logger_handler

      logger_handler
        logging.Handler

  Handler object for logging.

The following functions can be set in LoadingContext to override or augment the listed behaviors.

fetcher_constructor

      fetcher_constructor(cache, session)
        (Dict[unicode, unicode], requests.sessions.Session) -> Fetcher

  Construct a Fetcher object with the supplied cache and HTTP session.

resolver

      resolver(document_loader, document)
        (Loader, str | dict[str, Any]) -> str

  Resolve a relative document identifier to an absolute one that can be fetched.

The following functions can be set in RuntimeContext to override or augment the listed behaviors.

construct_tool_object

      construct_tool_object(toolpath_object, loadingContext)
        (MutableMapping[str, Any], LoadingContext) -> Process

  Hook to construct a Process object (eg CommandLineTool) object from a document.

select_resources

      selectResources(request)
        (Dict[str, int], RuntimeContext) -> Dict[str, int]

  Take a resource request and turn it into a concrete resource assignment.

make_fs_access

      make_fs_access(basedir)
        (str) -> StdFsAccess

  Return a file system access object.

In addition, when providing custom subclasses of Process objects, you can override the following methods:

CommandLineTool.make_job_runner

      make_job_runner(RuntimeContext)
        (RuntimeContext) -> Type[JobBase]

  Create and return a job runner object (this implements concrete execution of a command line tool).

Workflow.make_workflow_step

      make_workflow_step(toolpath_object, pos, loadingContext, parentworkflowProv)
        (Dict[str, Any], int, LoadingContext, Optional[ProvenanceProfile]) -> WorkflowStep

  Create and return a workflow step object.

Contents:

- cwltool Command Line Options
  - cwltool
- Loops
  - The loop condition
  - The loop field
  - Loop output modes
  - Loop-independent iterations
- Provenance capture
  - Who executed the workflow?
  - CWLProv folder structure
  - Research Object manifest
  - PROV profile
  - CWLPROV namespaces
  - Account who launched cwltool
  - Starting a workflow
  - Running workflow steps
  - Data inputs (usage)
  - Data outputs (generation)
  - Ending the workflow
- Python version support policy
- Process generator
  - Example runs
- API Reference
  - cwltool

### Indices and tables

- Index

- Module Index

- Search Page