The Execution Environment is the system on which your app executes when running in the DNAnexus cloud. Currently, we support Ubuntu Linux 20.04 16.04, and 14.04 (we welcome feedback on which other environments to provide in the future). The App API lets you specify the amount of computational resources your app will need (the instance type that it will be launched on) and the software packages that it requires as dependencies.

The Basics

What's a job?

When you send an /app-id/run, /applet-id/run, or /job/new call to the DNAnexus API, a job object is generated, and then dispatched to a DNAnexus worker node when all of its inputs are ready and it is considered "runnable".

What happens when a job is run?

The DNAnexus worker node performs the following:

• Generates a virtualized Linux container (virtual machine) just for your job. The container is a full-featured Linux OS.

  • If your job runs a sub-job using /job/new, the sub-job gets a completely independent virtual machine. Therefore, each individual job is free to make use of all the resources of its instance.

• Reads the runSpec.execDepends field of your app and installs packages in the container.

• Configures networking and logging services in the container.

• Fetches the code given in the runSpec.code field of the app and saves it in the working directory inside an interpreter-specific execution template.

• If any file objects are found in runSpec.bundledDepends, they are also downloaded to the root directory / and unpacked if compressed (with a mechanism that supports at least tar, gz, and other popular formats).

• ​Cluster jobs execute the bootstrap script (if provided) on all nodes. At this point, slave nodes should be fully initialized. They do not perform the following step of executing the job code.

• Executes the code with the interpreter given in runSpec.interpreter.

• Waits for the code to complete, reports the output or any errors to the platform, and destroys the virtual machine.

What's in this document?

The rest of this document describes the details of what happens in the virtual machine, what is expected of your executable in order to successfully report your output and any errors, and how you can request or provide additional resources for your job.

Environment Variables in the Container

The following environment variables are set in the container by the system before running your code. Their values communicate the information necessary to access the API and your job's data. Please note that the variables below are automatically consumed by the language bindings supplied by DNAnexus, so there is often no action necessary to use them.

Variables only present when the job is running on a cluster:

Variables only present when the job is running on a cluster of type dxspark:

Job I/O and Error Reporting

Job input, output, and error data are passed through JSON files in the working directory where your code runs.

Special Files in the Initial Working Directory

job_input.json

Before executing your code, the system saves the job input in the file job_input.json in the working directory where your code will run. You can either read this file directly, or rely on the execution template and language-specific bindings code (if available) to read it in and provde the input for you. For example, the Python language bindings will read in the job input and pass it as keyword arguments directly to your entry point function.

job_output.json

When your code has finished running, it must return to the system the values it wants to save in the output field of the job object representing the current job. This is done by serializing these values in the file job_output.json in the original working directory. You can either do this yourself, or rely on the execution template and language-specific bindings code (if available) to save the output for you. For example, the Python language bindings will expect your entry point function to return a hash with the output values, and serialize that.

NOTE: An empty hash ({}) must be saved to job_output.json even if your applet does not have an output spec.

job_error.json

If your code encounters a fatal error condition, it must exit with a non-zero exit status (raising an error or throwing an exception will make this happen in most languages). To facilitate debugging, it is also recommended that the job provide extended information about the error. Depending on the interpreter, throwing an exception may be sufficient to report an error message, or you may have to write to the file job_error.json file directly. The system will inspect the contents of this exception or file and set the failure metadata for the job object accordingly.

The file should be formatted like so:

{"error": {"type": "AppInternalError", "message": "Error while running micromap"}}

Error types

The field error.type in the file job_error.json should be set to one of the recognized error types.

Monitoring Jobs

The stdout and stderr of every running job are automatically captured and logged for you, and you can access these logs through the API as the job is running or after it has finished.

Debugging and connecting to jobs via SSH

Jobs can be optionally configured to allow SSH connections from a specified range of IPs, and to hold the execution environment for debugging when certain types of errors happen (debug hold).

For more information, see Connecting to jobs via SSH.

Code Interpreters

Apps and applets can be interpreted by "bash", "python3", and "python2.7" interpreters. We don't recommend using "python2.7" as it is no longer supported by the Python language maintainers.

python3

The Python 3 interpreter makes it easy to write apps in Python.

Entry Points

To designate entry points in your Python script, simply decorate the functions with @dxpy.entry_point("entry_point_name"). The following code snippet demonstrates when each part of your script will be run.

import dxpy
​
@dxpy.entry_point("myfunc")
def myfunc():
    # Gets run when you make a /job/new API call with "function" set to "myfunc"
    pass
​
@dxpy.entry_point("main")
def main():
    # Gets run when you make an /app(let)-xxxx/run API call OR
    # a /job/new API call with "function" set to "main"
    pass
​
# The following line will call the appropriate entry point.
dxpy.run()

Job Input

While the job's input will always be provided in the file job_input.json, the Python interpreter will also provide the key-value pairs as keyword arguments when calling your entry points.

Exception Handling

If your app throws dxpy.AppError, then the interpreter will report the job failure with failure reason AppError. In general, this error should be used for errors resulting from invalid user input. If your app throws an exception of any other class, the job will report the failure as AppInternalError.

bash

This is the general-purpose interpreter which you can use to run whatever shell commands and/or executables you may have packaged together with your app or applet.

Entry Points

To create multiple entry points for your bash executable, simply create bash functions with the same name as your entry point. The following code snippet demonstrates when each part of your script will be run.

# Anything outside the function declarations is always run
​
myfunc() {
    # Gets run when you make a /job/new API call with "function" set to "myfunc"
}
​
main() {
    # Gets run when you make an /app(let)-xxxx/run API call OR
    # a /job/new API call with "function" set to "main"
}

Job Input

While the job's input will always be provided in the file job_input.json, the bash interpreter will also set an environment variable for each key in the job input with value equal to the key's value. Case is preserved.

Exception Handling

Your bash script is interpreted with the -e flag set, so if any command exits with a nonzero exit code, your app will fail at that point with failure reason AppInternalError. To report an error with a more helpful error message, you must first write to the file job_error.json before letting a command exit with a nonzero exit code.

Available Resources

Computational Power and Memory

Default machine sizes vary by region. Below is the default mapping per region. For more precise specifications see Instance Types​

If you need more computational resources for your app, you can request a different machine instance type in the runSpec.systemRequirements.instanceType field of your dxapp.json.

NOTE: Some of the resources on a worker will be shared with DNAnexus processes that help run your job.

Choosing an Application Execution Environment

To specify the Application Execution Environment, please specify runSpec.distribution, runSpec.release and runSpec.version fields in your dxapp.json from the table below:

We recommend Ubuntu 20.04 version 0 Application Execution Environment for all apps and applets.

Network Access

Networking is pre-configured in the execution environment. The DNAnexus API server is always reachable (and can be located via the environment variables), as are other relevant DNAnexus services. Aside from this, network access is restricted by default and must be requested explicitly using the access.network field of your dxapp.json file, or /applet/new or /app/new. For example, use {"access": {"network": ["*"]}} to request unrestricted access.

Software Packages

DNAnexus utilities

The contents of the DNAnexus toolkit are available in the container, and environment variables such as PATH, PYTHONPATH, PERL5LIB, etc. are automatically set before your app runs, so that you can run utilities from the SDK simply as dx etc., as well as import the bindings libraries in scripting languages.

External Utilities

If your program relies on packages that must be present in the system in order to run, you can specify them in the dxapp.json (or directly in the Run Specification input to /app/new) like so:

    { "runSpec": {
        "execDepends": [
            {"name": "samtools"},
            {"name": "bedtools", "version": "2.16.1-1", "stages": ["work"]},
            {"name": "dx-toolkit",
             "package_manager": "git",
             "url": "https://github.com/dnanexus/dx-toolkit.git",
             "tag": "master",
             "destdir": "/opt/dx-toolkit",
             "build_commands": "make install DESTDIR=/ PREFIX=/opt/dnanexus"},
            {"name": "pysam",
             "package_manager": "pip",
             "version": "0.7.4"},
            {"name": "Bio::SeqIO",
             "package_manager": "cpan",
             "version": "1.0b3"},
            {"name": "bio",
             "package_manager": "gem",
             "version": "1.4.3"},
             {"name": "plyr",
             "package_manager": "cran",
             "version": "1.8.1"},
            {"name": "ggplot2",
             "package_manager": "cran"}
        ]
        ...
      },
      ...
    }

Here, the first dependency is an APT package. The second dependency is also an APT package, but specifies a particular version and limits the stages (entry points) to install the dependency for to only "work" (by default, dependencies are installed for all stages). The third dependency instructs the system to fetch directly from a Git repository, and the rest are dependencies for language-specific package managers:

NOTE: the requested APT packages will be installed but their "Recommends" will not be installed. You can simulate this behavior with apt-get install --no-install-recommends PACKAGES ... on an Ubuntu system.

NOTE:: To access any repository other than APT, your app or applet must request network access to the repository's host by adding an entry like "access": {"network": ["*"]}} to its dxapp.json metadata.

External APT Repositories

Loading APT packages in your execDepends only works for packages that are part of the default Ubuntu repositories. If you want to install a package from a third-party repository, you'll have to do so yourself at the beginning of your app code. In addition to configuring APT on the system to use the desired repository, you'll need to bypass the Execution Environment's built-in APT caching proxy and ensure your app has sufficient network permissions.

See the external_apt_repo_example app in the dx-toolkit distribution, which shows all the steps in action and demonstrates installing a package from an external APT repository. (App code; dxapp.json)

Git-specific Arguments

The following arguments are supported in execution dependencies where package_manager is set to git:

• url string (required): The URL pointing to the git repository.

• tag string (optional): The tag to check out from the

  repository. Defaults to the default tag of the remote.

• destdir string (optional): The directory to check the

  repository out into. It will be created if not present. Defaults to

  a temporary directory created by mktemp.

• build_commands string (optional): Arbitrary shell commands to

  run upon completing the checkout, for example "configure && make &&

  make install".

• stages array of strings (optional): Same meaning as in other

  dependency specifications.

Packaging Your Own Resources

Dependencies that are not available readily as packages can be bundled with an executable as data objects linked in the bundledDepends field of the executable's run specification. These can be any type of data objects including files and applets, and they will be made present in the temporary workspace of any job running the executable. Furthermore, any files found in this list of bundled dependencies will automatically be downloaded (and potentially unpacked) in the execution environment to the root directory.

If you are building your executable via the DNAnexus build utility, the tool will archive and upload any local files found in the resources directory in your source tree and extend the bundledDepends list to include this new file object on DNAnexus platform. When your executable is run, a file that you had placed in MyApp/resources/usr/bin/analyze-dna will be available as /usr/bin/analyze-dna in the execution environment. Note: the resources/ subdirectory is unpacked into the root of the virtual filesystem, not the working directory where your executable is started.

Using Application Resource Containers

Application resource containers are platform objects that enclose static or temporary data belonging to the application. Containers behave like projects. There are three types of containers created automatically for apps (only the temporary workspace is available when running applets):

For applications written in Python, methods in the dxpy.bindings.dxapp_container_functions module provide convenience functions for accessing these workspaces.

Logging Service

Messages printed by processes running in the execution environment to their standard output and standard error streams are saved to the job log. The job log has a 2 MB size limit, past which messages will be truncated. Job logs can be monitored in real time through the web interface or on the command line using dx watch.

In addition to logging standard output and standard error, jobs can produce custom log level messages. The valid log levels are:

See the help for the dx-log-stream command (dx-log-stream --help) and the dxlog.py file in the DNAnexus SDK for more details.

Using the logging facility directly in Python

When running Python programs, you can plug the Python logger facility directly into the DNAnexus logging system described above. To do so, use the following code:

import dxpy, logging
logger = logging.getLogger(__name__)
logger.addHandler(dxpy.DXLogHandler())
logger.propagate = False
logger.setLevel(logging.DEBUG)

The logger object can then be used to log messages at or above the log level specified, e.g. logger.debug("message").