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This is managed by decorators, or command line flags, or both. It uses the multiprocessing module's Process class to fork each host on a task. It will also check to see if a task is set to be run sequentially instead of in parallel, like functions set to be @runs_once.
execution model if it can't load multiprocessing.
…the Sphinx docs for the new decorators.
It also adds in a bit more checking to make sure a @runs_parallel won't raise an exception on python versions w/o the multiprocessing module.
It'll make it simpler to read what is going on, and describes in detail what cases will make a function run in parallel, if it is able to do so.
I'll wait to try and implement my own Pool, as the lack of shared state is killing the fabric tasks. They lose all knowledge of their env and the specific host they are on. I need to just make pool type class/function that just makes a bubble of size x of Process()es to run, and keeps it to a said size until it completes.
So I have added in the Job_Queue class, and incorperated it into the main fab section to manage the multiprocessing Processes. I have hooked into the state.output to toggle the _debug flag for job_queue. The job queue uses the host_string var that gets set as the Process().name to then set the env.state.host_string again inside the running queue.
…to multiprocessing
I was getting an error, non-fatal, for when a pool was specified that was larger than the sum of the hosts. So in that event I just set the pool size to the same size as number of hosts.
This will make the Job_Queue have a len throughout it's life, instead of just at the close().
…o multiprocessing
Conflicts: fabric/decorators.py fabric/main.py fabric/state.py tests/test_decorators.py
…in the fabfile.
…e're back and working
cast state.env.host_string to a str (https://github.com/goosemo/fabric/is
Setting abort_on_prompts always failed before, regardless of whether the env.hosts was set
…s_always_aborts Cool, pulling that in then
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