Note:
The job editor displays default settings for memory usage,
parallelization, and precision based on the environment files effective for the
current session on the local machine. When you submit a job to a remote
machine,
Abaqus
replaces the default settings with those from the environment files on the
remote machine. Nondefault settings in the job editor are saved with the job
and will be used regardless of where you run the analysis.
Each queue name that appears in the job editor refers to an entry in your Abaqus environment file in which you specify how you want the job to be run on the host computer. In other words, when you select a queue name in the job editor, you specify not only the desired queue on the host computer but also other options, such as the directory on the host computer in which you want to run the job and the files you want copied back to your local directory when the job is complete. You can specify your preferences for running a job remotely by adding the following to your Abaqus environment file: def onCaeStartup(): import os def makeQueues(*args): session.Queue(name, queueName, hostName, fileCopy, directory, driver, remotePlatform, filesToCopy, deleteAfterCopy, description) addImportCallback('job', makeQueues) This entry is written using the Abaqus command language. The following list describes each argument in the entry above.
The def onCaeStartup(): import os def makeQueues(*args): session.Queue(name='long', queueName='aba_long', hostName='jobserver', directory='/scratch/' + os.environ['USER']) addImportCallback('job', makeQueues) The commands in the example above configure the following:
Since the fileCopy, driver, remotePlatform, filesToCopy, and deleteAfterCopy arguments have been left out of the entry above, the default options for these parameters are assigned to this queue automatically. If you want to create two or more queues, you can repeat the line containing
the def onCaeStartup(): import os def makeQueues(*args): session.Queue(name='long', queueName='aba_long', hostName='jobserver', directory='/scratch/' + os.environ['USER']) session.Queue(name='job', queueName='aba_job', hostName='jobserver', fileCopy=OFF) addImportCallback('job', makeQueues) The monitoring functions described in Monitoring the progress of an analysis job, are available for jobs run remotely just as they are for jobs run locally. However, the output database for the job, like any other analysis files that you may have requested, is not copied to your local directory until after the job is complete. As a result, you must create and start a network output database connector if you want to use the Visualization module to view the results being generated by an analysis running on a remote system. For more information, see Accessing an output database on a remote computer. |