Notes from Recon meeting¶

Moving forward, refinements will all be split into 2 steps, prep and run.

Prepare refine¶

When the user selects to prep a refinement, a web form is provided to select the:

1. refinement method - eman, xmipp, frealign, etc...
2. stack
3. model
4. run parameters - runname, rundir, description
5. stack prep params - lp, hp, last particle, binning

The web then calls prepRefine.py located on the local cluster to prepare the refinement.

Run Refine¶

When the user selects to run a prepared refinement, a web form is provided to select the:

1. prepped refine
2. cluster parameters - ppn, nodes, walltime, cputime, memory, mempernode
3. refine params, both general and method specific

The web server will then:

1. verify the cluster params by checking default_cluster.php
2. if needed, copy the stack and model to a location that can be accessed by the selected cluster
3. verify the user is logged into the cluster
4. pass the list of commands to runJob.py (extended from the Agent class), located on the remote cluster

runJob.py will:

1. format the command tokens in a dictionary of key-value pairs
2. set the job type which was passed in the command
3. create an instance of the job class based on the job type
4. create an instance of the processing host class
5. launch the job based via the processing host
6. update the job status in the appion database (do we have db access from the remote cluster?)

Object Model¶

Processing Host¶

Each processing host (eg. Garibaldi, Guppy, Trestles) will define a class extended from a base ProcessingHost class.
The extended classes know what headers need to be placed at the top of job files and they know how to execute a command based on the specific clusters requirements.
The base ProcessingHost class could be defined as follows:

abstract class ProcessingHost:
    def generateHeader(jobObject) - abstract, extended classes should define this, returns a string
    def executeCommand(command) - abstract, extending classes define this
    def createJobFile(header, commandList) - defined in base class, commandList is a 2D array, each row is a line in the job file.
    def launchJob(jobObject) - defined in base class, jobObject is an instance of the job class specific to the jobtype we are running
        header = generateHeader(jobObject)
        jobFile = createJobFile(header, jobObject.getCommandList())
        executeCommand(jobFile)