/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"./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.runSpec.execDepends field of your app and installs packages in the container.runSpec.code field of the app and saves it in the working directory inside an interpreter-specific execution template.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).runSpec.interpreter.DX_SECURITY_CONTEXT{"auth_token":"outside","auth_token_type":"Bearer"}DX_APISERVER_HOSTapi.dnanexus.comDX_APISERVER_PORT443DX_JOB_IDjob-xxxxDX_WORKSPACE_IDcontainer-xxxxDX_RESOURCES_IDcontainer-xxxxDX_PROJECT_CACHE_IDcontainer-xxxxDX_PROJECT_CONTEXT_IDproject-xxxxDX_CLUSTER_MASTER_IP172.168.1.120DX_CLUSTER_LOCAL_IP10.1.1.101DX_CLUSTER_HOSTNAMEec2_instance_public.hostnameDX_CLUSTER_NODE_ID1dxspark:DX_CLUSTER_METASTORE_URIhivemetastore.urijob_input.json{"x": 1, "y": true}job_output.json{"z": {"$dnanexus_link": "file-xxxx"}}job_error.json{"error": {"type": "AppError", "message": "x must be at least 2"}}job_input.jsonjob_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 provide 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.jsonoutput 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.{}) must be saved to job_output.json even if your applet does not have an output spec.job_error.jsonjob_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.error.type in the file job_error.json should be set to one of the recognized error types.AppError{"error": {"type": "AppError", "message": "Out of memory: Please select a larger instance type for your job"}}AppInternalError{"error": {"type": "AppInternalError", "message": "Division by zero at line 256"}}@dxpy.entry_point("entry_point_name"). The following code snippet demonstrates when each part of your script will be run.job_input.json, the Python interpreter will also provide the key-value pairs as keyword arguments when calling your entry points.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.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.-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.aws:us-east-1aws:ap-southeast-2aws:eu-central-1aws:cn-north-1azure:westusazure:westeuroperunSpec.systemRequirements.instanceType field of your dxapp.json.runSpec.distribution, runSpec.release and runSpec.version fields in your dxapp.json from the table below:access.network field of your dxapp.json file, or /applet/new or /app/new. For example, use {"access": {"network": ["*"]}} to request unrestricted access.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.dxapp.json (or directly in the Run Specification input to /app/new) like so:apt-get install --no-install-recommends PACKAGES ... on an Ubuntu system."access": {"network": ["*"]}} to its dxapp.json metadata.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.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)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 thedestdir string (optional): The directory to check themktemp.build_commands string (optional): Arbitrary shell commands tostages array of strings (optional): Same meaning as in otherbundledDepends 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.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.workspaceDX_WORKSPACE_IDprojectCacheDX_PROJECT_CACHE_IDresourcesDX_RESOURCES_IDdxpy.bindings.dxapp_container_functions module provide convenience functions for accessing these workspaces.dx watch.dx-log-stream command (dx-log-stream --help) and the dxlog.py file in the DNAnexus SDK for more details.logger object can then be used to log messages at or above the log level specified, e.g. logger.debug("message").